Building energy-saving potential assessment method and system based on energy consumption quota

Abstract

The invention provides a building energy-saving potential assessment method and system based on an energy consumption quota, and the method comprises the steps: carrying out the energy consumption analysis of a to-be-assessed building through a clustering analysis algorithm and a regression analysis algorithm according to the energy consumption multi-factor data of the to-be-assessed building, and obtaining the energy consumption quota data and the carbon emission quota data; based on the energy consumption multi-factor data and the energy consumption quota data, performing power consumption prediction on the to-be-evaluated building by using a multiple linear regression algorithm to obtain power consumption prediction data; calculating target energy efficiency data according to the electricity consumption prediction data; according to the energy consumption quota data, the carbon emission quota data and the target energy efficiency data, performing energy-saving potential assessment on the to-be-assessed building to obtain a comprehensive potential score of the to-be-assessed building; according to the method, the energy-saving potential of the building is evaluated by combining the energy consumption quota data, the carbon emission quota data and the power consumption prediction data obtained by using the multiple linear regression algorithm, the actual energy consumption of the building can be comprehensively reflected, and thus the potential energy-saving opportunity of the building can be identified more accurately.

Description

Technical Field

[0001] The present invention relates to the technical field of building energy consumption management, and in particular to a method and system for evaluating building energy-saving potential based on energy consumption quotas. Background Art

[0002] With the growing global energy crisis and environmental challenges, building energy management has become a crucial issue for sustainable development. As one of the primary sectors of global energy consumption, the construction industry's share of global energy consumption continues to rise, placing significant pressure on the environment. Consequently, effective management and optimization of building energy consumption has become a key focus.

[0003] Traditional energy management methods often lack scientific and systematic principles, making them inadequate for meeting the demands of modern businesses for efficient energy use. First, different types of buildings differ significantly in their functions and usage patterns. For example, office buildings, production workshops, and storage facilities all have distinct energy consumption characteristics and operating modes. Second, factors such as the type of equipment within a building, its frequency of use, and operating conditions also significantly influence energy consumption. Furthermore, external factors such as geographic location, climate, and energy prices also significantly impact building energy consumption.

[0004] Existing energy consumption management methods usually adopt a single energy consumption standard or simple energy consumption monitoring means, which cannot fully reflect the actual energy consumption of the building. This extensive management method not only fails to effectively identify energy consumption anomalies and potential energy-saving opportunities, but may also lead to energy waste and increased costs. Summary of the invention

[0005] To address the problem that traditional energy-saving potential assessment methods, which only use a single energy consumption monitoring method, are unable to fully reflect the actual energy consumption of a building, resulting in an inability to effectively identify potential energy-saving opportunities, the present invention proposes a building energy-saving potential assessment method based on energy consumption quotas, including:

[0006] Based on the acquired multi-factor energy consumption data of the building to be evaluated, a cluster analysis algorithm and a regression analysis algorithm are used to perform energy consumption analysis on the building to be evaluated, so as to obtain energy consumption quota data and carbon emission quota data of the building to be evaluated;

[0007] Based on the energy consumption multi-factor data and the energy consumption quota data, a multivariate linear regression algorithm is used to predict the electricity consumption of the building to be evaluated to obtain electricity consumption prediction data of the building to be evaluated;

[0008] Calculating target energy efficiency data based on the electricity consumption forecast data;

[0009] Based on the energy consumption quota data, carbon emission quota data, and the target energy efficiency data, conduct an energy-saving potential assessment on the building to be evaluated to obtain the comprehensive potential score of the building to be evaluated.

[0010] Optionally, the energy consumption analysis of the building to be evaluated using the clustering analysis algorithm and regression analysis algorithm based on the obtained multi-factor energy consumption data of the building to be evaluated to obtain the energy consumption quota data and carbon emission quota data of the building to be evaluated includes:

[0011] Calculate the seasonal electricity load rate characteristics of the building to be evaluated based on the obtained multi-factor energy consumption data of the building to be evaluated;

[0012] Conduct a clustering analysis on the seasonal electricity load rate characteristics using the clustering analysis algorithm to obtain the electricity consumption patterns of the building to be evaluated in different seasons;

[0013] Based on the electricity consumption patterns of the building to be evaluated in different seasons, use the polynomial regression analysis algorithm to fit the load rate characteristic curves of the building to be evaluated in different seasons;

[0014] Based on the load rate characteristic curves of the building to be evaluated in different seasons, calculate the energy consumption quota data and carbon emission quota data of the building to be evaluated.

[0015] Optionally, the calculation of the energy consumption quota data and carbon emission quota data of the building to be evaluated based on the load rate characteristic curves of the building to be evaluated in different seasons includes:

[0016] Calculate the equivalent full-load operation days of the building to be evaluated based on the load rate characteristic curves of the building to be evaluated in different seasons;

[0017] Calculate the energy consumption quota data of the building to be evaluated based on the equivalent full-load operation days of the building to be evaluated;

[0018] Calculate the carbon emission quota data of the building to be evaluated based on the energy consumption quota data.

[0019] Optionally, the calculation of the energy consumption quota data of the building to be evaluated based on the equivalent full-load operation days of the building to be evaluated includes:

[0020] Calculate the sub-item energy consumption data of each energy-consuming system in the building to be evaluated based on the equivalent full-load operation days of the building to be evaluated;

[0021] Perform a summation operation on the sub-item energy consumption data of each energy-consuming system to obtain the energy consumption quota data of the building to be evaluated;

[0022] Among them, the energy consumption system includes one or more of the following: conventional energy consumption systems and special energy consumption coefficients;

[0023] The conventional energy consumption system includes one or more of the following: heating systems, refrigeration systems, lighting and socket systems, and power systems;

[0024] The special energy consumption system includes one or more of the following: power production equipment systems and power transformation equipment systems.

[0025] Optionally, the calculation formula for the energy consumption quota data of the building to be evaluated is as follows:

[0026] E c = ∑ i E i , i = g, z, m, d, s, y;

[0027] In the formula,

[0028]

[0029] Among them, E c represents the energy consumption quota data of the building to be evaluated; E i represents the sub-item energy consumption data of the i-th energy consumption system; E g represents the sub-item energy consumption data of the heating system g; E z represents the sub-item energy consumption data of the refrigeration system z; E m represents the sub-item energy consumption data of the lighting and socket system m; E d represents the sub-item energy consumption data of the power system d; E s represents the sub-item energy consumption data of the power production equipment system s; E y represents the sub-item energy consumption data of the power transformation equipment system y; b i represents the equivalent full-load operation days of the i-th energy consumption system in the building to be evaluated; Q imax represents the maximum daily energy consumption of the i-th energy consumption system throughout the year; S represents the building area of the building to be evaluated; b x represents the equivalent full-load monthly operation days of the x-th month; x = 1…12.

[0030] Optionally, calculating the carbon emission quota data of the building to be evaluated according to the energy consumption quota data includes:

[0031] Performing a multiplication operation on the energy consumption quota data and the pre-set green certificate offset ratio to obtain the non-renewable energy consumption data of the building to be evaluated;

[0032] Performing a multiplication operation on the non-renewable energy consumption data and the pre-obtained carbon emission factor to obtain the carbon emission quota data of the building to be evaluated.

[0033] Optionally, the calculation formula for the carbon emission quota data of the building to be evaluated is as follows:

[0034] C c = E0 * α;

[0035] In the formula,

[0036] E0 = E c *(1 - p);

[0037] Among them, C c represents the carbon emission quota data of the building to be evaluated; E0 represents the non-renewable energy consumption data of the building to be evaluated; E c represents the energy consumption quota data of the building to be evaluated; p represents the green certificate offset ratio of the building to be evaluated; α represents the carbon emission factor of the building to be evaluated.

[0038] Optionally, the multi-factor energy consumption data includes one or more of the following: historical energy consumption data, building attribute data, historical meteorological data, and office data;

[0039] The historical energy consumption data includes: total energy consumption data and sub-item energy consumption data of the energy consumption system;

[0040] The sub-item energy consumption data of the energy consumption system includes: energy consumption data of the conventional energy consumption system and energy consumption data of the special energy consumption system;

[0041] The energy consumption data of the conventional energy consumption system includes one or more of the following: heating system energy consumption data, refrigeration system energy consumption data, lighting and socket system energy consumption data, and power system energy consumption data;

[0042] The energy consumption data of the special energy consumption system includes one or more of the following: energy consumption data of power production equipment and energy consumption data of substation equipment;

[0043] The building attribute data includes one or more of the following: building area data, building type data, location area information, and enclosure structure data;

[0044] The historical meteorological data includes one or more of the following: temperature data, humidity data, wind speed data, wind direction data, air pressure data, and precipitation data of the location area;

[0045] The office data includes one or more of the following: office hour data and number of office workers data.

[0046] Optionally, the expression of the multiple linear regression algorithm is as follows:

[0047] E yi = β0 + β1E 常规 + β2E 特殊+β3S + β4T + β5Time + β6L + ∈;

[0048] Among them, E yi represents the electricity consumption prediction data of the building to be evaluated; β0 represents the first regression coefficient; β1 represents the second regression coefficient; E 常规 represents the energy consumption quota data of the conventional energy system; β2 represents the third regression coefficient; E 特殊 represents the energy consumption quota data of the special energy system; β3 represents the fourth regression coefficient; S represents the building attribute data of the building to be evaluated; β4 represents the fifth regression coefficient; T represents the historical meteorological data of the building to be evaluated; β5 represents the sixth regression coefficient; Time represents the office time data of the building to be evaluated; β6 represents the seventh regression coefficient; L represents the number of office workers in the building to be evaluated; ∈ represents the error term.

[0049] Optionally, calculating the target energy efficiency data according to the electricity consumption prediction data includes:

[0050] Obtaining the energy efficiency ratio of the building to be evaluated according to the electricity consumption prediction data and the actual electricity consumption data of the building to be evaluated obtained;

[0051] Calculating the target energy efficiency data of the building to be evaluated according to the energy efficiency ratio of the building to be evaluated.

[0052] Optionally, evaluating the energy-saving potential of the building to be evaluated according to the energy consumption quota data, carbon emission quota data and the target energy efficiency data, and obtaining the comprehensive potential score of the building to be evaluated includes:

[0053] Determining the energy consumption score of the building to be evaluated according to the energy consumption quota data and the actual energy consumption value of the building to be evaluated obtained;

[0054] Determining the carbon emission score of the building to be evaluated according to the carbon emission quota and the actual carbon emission value of the building to be evaluated obtained;

[0055] Determining the energy efficiency score of the building to be evaluated according to the target energy efficiency data;

[0056] Evaluating the energy-saving potential of the building to be evaluated according to the energy consumption score, the carbon emission score and the energy efficiency score, and obtaining the comprehensive potential score of the building to be evaluated.

[0057] Based on the same inventive concept, the present invention also provides a building energy-saving potential evaluation system based on energy consumption quota, including:

[0058] An energy consumption analysis module, which is used to perform energy consumption analysis on the building to be evaluated according to the obtained multi-factor energy consumption data of the building to be evaluated by using a clustering analysis algorithm and a regression analysis algorithm, so as to obtain the energy consumption quota data and carbon emission quota data of the building to be evaluated;

[0059] An electricity consumption prediction module, which is used to predict the electricity consumption of the building to be evaluated by using a multiple linear regression algorithm based on the multi-factor energy consumption data and the energy consumption quota data, so as to obtain the electricity consumption prediction data of the building to be evaluated;

[0060] An energy efficiency calculation module, which is used to calculate target energy efficiency data according to the electricity consumption prediction data;

[0061] A potential evaluation module, which is used to evaluate the energy-saving potential of the building to be evaluated according to the energy consumption quota data, carbon emission quota data and the target energy efficiency data, so as to obtain the comprehensive potential score of the building to be evaluated.

[0062] Optionally, the energy consumption analysis module includes:

[0063] A load characteristic calculation sub-module, which is used to calculate the seasonal electricity load rate characteristics of the building to be evaluated according to the obtained multi-factor energy consumption data of the building to be evaluated;

[0064] A clustering analysis sub-module, which is used to perform clustering analysis on the seasonal electricity load rate characteristics by using a clustering analysis algorithm to obtain the electricity consumption patterns of the building to be evaluated in different seasons;

[0065] A characteristic curve generation sub-module, which is used to fit the load rate characteristic curves of the building to be evaluated in different seasons by using a polynomial regression analysis algorithm according to the electricity consumption patterns of the building to be evaluated in different seasons;

[0066] A quota calculation sub-module, which is used to calculate the energy consumption quota data and carbon emission quota data of the building to be evaluated according to the load rate characteristic curves of the building to be evaluated in different seasons.

[0067] Optionally, the quota calculation sub-module includes:

[0068] A full-load operation unit, which is used to calculate the equivalent full-load operation days of the building to be evaluated according to the load rate characteristic curves of the building to be evaluated in different seasons;

[0069] An energy consumption quota calculation unit, which is used to calculate the energy consumption quota data of the building to be evaluated according to the equivalent full-load operation days of the building to be evaluated;

[0070] A carbon emission quota calculation unit, which is used to calculate the carbon emission quota data of the building to be evaluated according to the energy consumption quota data.

[0071] Optionally, the energy consumption quota calculation unit includes:

[0072] A sub-item energy consumption calculation sub-unit, configured to calculate sub-item energy consumption data of each energy consumption system in the building to be evaluated according to the equivalent full-load operation days of the building to be evaluated;

[0073] An energy consumption quota determination sub-unit, configured to perform a summation operation on the sub-item energy consumption data of each energy consumption system to obtain energy consumption quota data of the building to be evaluated;

[0074] Wherein, the energy consumption systems include one or more of the following: a conventional energy consumption system and a special energy consumption coefficient;

[0075] The conventional energy consumption system includes one or more of the following: a heating system, a refrigeration system, a lighting and socket system, and a power system;

[0076] The special energy consumption system includes one or more of the following: an electric power production equipment system and a power transformation equipment system.

[0077] Optionally, the calculation formula for the energy consumption quota data of the building to be evaluated is as follows:

[0078] E c = ∑ i E i , i = g, z, m, d, s, y;

[0079] In the formula,

[0080]

[0081] Wherein, E c represents the energy consumption quota data of the building to be evaluated; E i represents the sub-item energy consumption data of the i-th energy consumption system; E g represents the sub-item energy consumption data of the heating system g; E z represents the sub-item energy consumption data of the refrigeration system z; E m represents the sub-item energy consumption data of the lighting and socket system m; E d represents the sub-item energy consumption data of the power system d; E s represents the sub-item energy consumption data of the electric power production equipment system s; E y represents the sub-item energy consumption data of the power transformation equipment system y; b i represents the equivalent full-load operation days of the i-th energy consumption system in the building to be evaluated; Q imax represents the maximum daily energy consumption of the i-th energy consumption system throughout the year; S represents the building area of the building to be evaluated; b x represents the equivalent full-load monthly operation days of the x-th month; x = 1... 12.

[0082] Optionally, the carbon emission quota calculation unit includes:

[0083] A non-renewable energy consumption calculation sub-unit, configured to perform a multiplication operation on the energy consumption quota data and a pre-set green certificate offset ratio to obtain the non-renewable energy consumption data of the building to be evaluated;

[0084] A carbon emission quota determination sub-unit, configured to perform a multiplication operation on the non-renewable energy consumption data and a pre-acquired carbon emission factor to obtain the carbon emission quota data of the building to be evaluated.

[0085] Optionally, the calculation formula for the carbon emission quota data of the building to be evaluated is as follows:

[0086] C c = E0 * α;

[0087] In the formula,

[0088] E0 = E c *(1 - p);

[0089] Wherein, C c represents the carbon emission quota data of the building to be evaluated; E0 represents the non-renewable energy consumption data of the building to be evaluated; E c represents the energy consumption quota data of the building to be evaluated; p represents the green certificate offset ratio of the building to be evaluated; α represents the carbon emission factor of the building to be evaluated.

[0090] Optionally, the multi-factor energy consumption data includes one or more of the following: historical energy consumption data, building attribute data, historical meteorological data, and office data;

[0091] The historical energy consumption data includes: total energy consumption data and sub-item energy consumption data of the energy consumption system;

[0092] The sub-item energy consumption data of the energy consumption system includes: energy consumption data of the conventional energy consumption system and energy consumption data of the special energy consumption system;

[0093] The energy consumption data of the conventional energy consumption system includes one or more of the following: energy consumption data of the heating system, energy consumption data of the cooling system, energy consumption data of the lighting and socket system, and energy consumption data of the power system;

[0094] The energy consumption data of the special energy consumption system includes one or more of the following: energy consumption data of the power production equipment and energy consumption data of the substation equipment;

[0095] The building attribute data includes one or more of the following: building area data, building type data, location area information, and enclosure structure data;

[0096] The historical meteorological data includes one or more of the following: temperature data, humidity data, wind speed data, wind direction data, air pressure data, and precipitation data of the location area;

[0097] The office data includes one or more of the following: office hour data and number of office workers data.

[0098] Optionally, the expression of the multiple linear regression algorithm is as follows:

[0099] E yi = β0 + β1E 常规 + β2E 特殊 + β3S + β4T + β5Time + β6L + ∈;

[0100] Where, E yi represents the electricity consumption prediction data of the building to be evaluated; β0 represents the first regression coefficient; β1 represents the second regression coefficient; E 常规 represents the energy consumption quota data of the conventional energy system; β2 represents the third regression coefficient; E 特殊 represents the energy consumption quota data of the special energy system; β3 represents the fourth regression coefficient; S represents the building attribute data of the building to be evaluated; β4 represents the fifth regression coefficient; T represents the historical meteorological data of the building to be evaluated; β5 represents the sixth regression coefficient; Time represents the office hour data of the building to be evaluated; β6 represents the seventh regression coefficient; L represents the number of office workers data of the building to be evaluated; ∈ represents the error term.

[0101] Optionally, the energy efficiency calculation module includes:

[0102] An energy efficiency ratio calculation sub-module, configured to obtain the energy efficiency ratio of the building to be evaluated according to the electricity consumption prediction data and the actual electricity consumption data of the building to be evaluated obtained;

[0103] A target energy efficiency output sub-module, configured to calculate the target energy efficiency data of the building to be evaluated according to the energy efficiency ratio of the building to be evaluated.

[0104] Optionally, the potential evaluation module includes:

[0105] An energy consumption evaluation sub-module, configured to determine the energy consumption score of the building to be evaluated according to the energy consumption quota data and the actual energy consumption value of the building to be evaluated obtained;

[0106] A carbon emission evaluation sub-module, configured to determine the carbon emission score of the building to be evaluated according to the carbon emission quota and the actual carbon emission value of the building to be evaluated obtained;

[0107] An energy efficiency evaluation sub-module, configured to determine the energy efficiency score of the building to be evaluated according to the target energy efficiency data;

[0108] An energy-saving potential evaluation sub-module, which is used to evaluate the energy-saving potential of the building to be evaluated according to the energy consumption score, the carbon emission score and the energy efficiency score, so as to obtain the comprehensive potential score of the building to be evaluated.

[0109] On the other hand, the present invention also provides an electronic device, including: at least one processor and a memory; the memory and the processor are connected through a bus;

[0110] The memory is used to store one or more programs;

[0111] When the one or more programs are executed by the at least one processor, the above-mentioned method for evaluating the energy-saving potential of a building based on energy consumption quota is implemented.

[0112] On the other hand, the present invention also provides a computer-readable storage medium with an execution program stored thereon. When the execution program is executed, the above-mentioned method for evaluating the energy-saving potential of a building based on energy consumption quota is implemented.

[0113] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0114] The present invention provides a method and system for evaluating the energy-saving potential of a building based on energy consumption quota, including: according to the obtained multi-factor energy consumption data of the building to be evaluated, using the clustering analysis algorithm and the regression analysis algorithm to perform energy consumption analysis on the building to be evaluated, so as to obtain the energy consumption quota data and carbon emission quota data of the building to be evaluated; based on the multi-factor energy consumption data and the energy consumption quota data, using the multiple linear regression algorithm to predict the power consumption of the building to be evaluated, so as to obtain the power consumption prediction data of the building to be evaluated; according to the power consumption prediction data, calculate the target energy efficiency data; according to the energy consumption quota data, carbon emission quota data and the target energy efficiency data, evaluate the energy-saving potential of the building to be evaluated, so as to obtain the comprehensive potential score of the building to be evaluated; based on the multi-factor data related to energy consumption, using the clustering algorithm and the regression analysis algorithm, this application generates the energy consumption quota data and carbon emission quota data, which can comprehensively reflect the actual energy consumption situation of the building and provide a more accurate energy consumption benchmark; and using the multiple linear regression algorithm for power consumption prediction can accurately reflect the future power consumption demand and reduce the prediction error; therefore, by combining the energy consumption benchmark value and the power consumption prediction data to evaluate the energy-saving potential of the building, the present invention can more accurately identify the potential energy-saving opportunities of the building. BRIEF DESCRIPTION OF THE DRAWINGS

[0115] Figure 1 It is a schematic flow chart of a method for evaluating the energy-saving potential of a building based on energy consumption quota provided by the present invention;

[0116] Figure 2Schematic diagram of the structural composition of a building energy conservation potential assessment system provided by the present invention;

[0117] Figure 3 Schematic diagram of the structure of an electronic device provided by the present invention. Specific implementation manners

[0118] The present invention provides a method, a system, a device and a medium for assessing the building energy conservation potential based on energy consumption quotas. The following further details the specific implementation manners of the present invention with reference to the accompanying drawings.

[0119] Embodiment 1:

[0120] The present invention provides a method for assessing the building energy conservation potential based on energy consumption quotas. The process schematic diagram is as Figure 1 shown and includes:

[0121] Step 1: According to the obtained multi-factor energy consumption data of the building to be evaluated, use the clustering analysis algorithm and the regression analysis algorithm to perform energy consumption analysis on the building to be evaluated, and obtain the energy consumption quota data and carbon emission quota data of the building to be evaluated;

[0122] Step 2: Based on the multi-factor energy consumption data and the energy consumption quota data, use the multiple linear regression algorithm to predict the electricity consumption of the building to be evaluated, and obtain the electricity consumption prediction data of the building to be evaluated;

[0123] Step 3: Calculate the target energy efficiency data according to the electricity consumption prediction data;

[0124] Step 4: According to the energy consumption quota data, the carbon emission quota data and the target energy efficiency data, evaluate the energy conservation potential of the building to be evaluated, and obtain the comprehensive potential score of the building to be evaluated.

[0125] Generally, when managing the energy consumption of buildings, a single energy consumption standard or simple energy consumption monitoring means is usually adopted, which is difficult to comprehensively reflect the actual energy consumption of buildings. Especially in large enterprises (such as power grid enterprises), there are a large number of self-owned buildings with complex types, and this traditional energy consumption management method is difficult to cope with. To solve the above problems, it is particularly important to develop a scientific and reasonable energy consumption quota compilation method and energy conservation potential assessment scheme. In this context, in order to fully tap the energy conservation potential of buildings, the present invention considers introducing a new evaluation framework, which combines modern data analysis technology and advanced mathematical algorithms to ensure a comprehensive assessment of building energy consumption. Specifically:

[0126] In one implementation manner, the process of performing energy consumption analysis on the building to be evaluated according to the obtained multi-factor energy consumption data of the building to be evaluated by using the clustering analysis algorithm and the regression analysis algorithm to obtain the energy consumption quota data and the carbon emission quota data of the building to be evaluated may include:

[0127] Based on the multi-factor energy consumption data of the building to be evaluated obtained, calculate the seasonal electricity load rate characteristics of the building to be evaluated;

[0128] Use the clustering analysis algorithm to perform clustering analysis on the seasonal electricity load rate characteristics, and obtain the electricity consumption patterns of the building to be evaluated in different seasons;

[0129] According to the electricity consumption patterns of the building to be evaluated in different seasons, use the polynomial regression analysis algorithm to fit the load rate characteristic curves of the building to be evaluated in different seasons;

[0130] According to the load rate characteristic curves of the building to be evaluated in different seasons, calculate the energy consumption quota data and carbon emission quota data of the building to be evaluated;

[0131] In order to address the problems of large quantity and complex types in self-owned buildings, the above multi-factor energy consumption data may include one or more of the following: historical energy consumption data, building attribute data, historical meteorological data, and office data;

[0132] Historical energy consumption data may include: total energy consumption data and sub-item energy consumption data of energy-using systems;

[0133] The sub-item energy consumption data of energy-using systems may include: energy consumption data of conventional energy-using systems and energy consumption data of special energy-using systems;

[0134] The energy consumption data of conventional energy-using systems may include one or more of the following: energy consumption data of heating systems, energy consumption data of cooling systems, energy consumption data of lighting and socket systems, and energy consumption data of power systems;

[0135] The energy consumption data of special energy-using systems may include one or more of the following: energy consumption data of power production equipment and energy consumption data of substation equipment; By considering the sub-item energy consumption data of energy-using systems, the energy consumption characteristics of each energy-using system in the building can be accurately identified, providing a scientific basis for the exploration of energy-saving potential;

[0136] Building attribute data may include one or more of the following: building area data, building type data, location area information, and envelope structure data; By considering building attribute data, the physical characteristics and usage functions of the building itself can be fully considered, thus more accurately evaluating its energy consumption level and energy-saving potential;

[0137] Historical meteorological data may include one or more of the following: temperature data, humidity data, wind speed data, wind direction data, air pressure data, and precipitation data of the location area; By considering historical meteorological data, the influence of external environmental factors on building energy consumption can be dynamically reflected, especially the energy consumption changes in different seasons and climate conditions, providing more refined support for the formulation of energy-saving measures;

[0138] Office data may include one or more of the following: office hour data and office population data; by introducing office data, the impact of building usage patterns on energy consumption can be further captured, thereby providing more targeted suggestions for energy consumption management and energy-saving optimization;

[0139] Specifically, in this implementation manner, the process of calculating the seasonal electricity load rate characteristics of the building to be evaluated according to the obtained multi-factor energy consumption data of the building to be evaluated may include:

[0140] First, establish a monthly electricity load rate model, construct a matrix for the monthly partial load rate data of the four quarters. The building electricity load rate is defined as the ratio of the actual output capacity of each electrical equipment during building operation to the maximum capacity actually used throughout the year. The change of the building electricity partial load rate varies with the building operation time and seasons. Classify the daily changes of the monthly load rate of each building. For example, the partial load rate of each day can be expressed as:

[0141]

[0142] Thus, the monthly partial load rate matrix [P x = [P1, P2, P3, … P 30 can be obtained. By inference, the annual monthly partial load rate matrix [P day,x of each building can be obtained n (day = 1, 2, 3, … ≤ 31; month x = 1, 2, 3, …, 12; number of sample buildings n = 1, 2, …, l n ).

[0143] Due to the large influence of seasonal changes, all monthly partial load rates are divided into four seasons, for example, spring (March - May), summer (June - August), autumn (September - November), winter (December - February), to form four monthly partial load rate matrices respectively representing the four seasons:

[0144] [P day,x T (day = 1, 2, 3, … ≤ 31; month x = 1, 2, 3, …, 12; season T = {spring, summer, autumn, winter});

[0145] In this implementation manner, the process of using the clustering analysis algorithm to perform clustering analysis on the seasonal electricity load rate characteristics to obtain the electricity usage patterns of the building to be evaluated in different seasons may include:

[0146] Use the K - means clustering algorithm to perform clustering analysis on the monthly partial load rate matrices of the four seasons, obtain clustering results with different numbers, and use the silhouette coefficient to compare the clustering effects under different numbers of clusters, and select the clustering number with the optimal evaluation index as the best clustering number. ​

[0147] Randomly select k samples as the initial clustering centers, and preset the number of clusters k to be in the range of [2, 5]; for each sample in the dataset, calculate its distances to the k clustering centers using the Euclidean distance, and assign it to the cluster where the nearest clustering center is located; recalculate the clustering center of each cluster, that is, calculate the average features of all samples within the cluster; repeat this step until the clustering centers no longer change or reach the preset number of iterations.

[0148]

[0149] Among them, d AB represents the Euclidean distance between sample A and sample B; [P day,x TA represents the monthly partial load rate data of sample A for four quarters, [P day,x TB represents the monthly partial load rate data of sample B for four quarters.

[0150] Calculate the silhouette coefficient of each sample, and then calculate the average value of the silhouette coefficients of all samples to obtain the overall silhouette coefficient under this number of clusters. Compare the overall silhouette coefficients under different numbers of clusters, and select the number of clusters corresponding to the maximum silhouette coefficient as the optimal number of clusters.

[0151] For example, the calculation formula of the silhouette coefficient for sample A is as follows:

[0152]

[0153] Among them, S A represents the silhouette coefficient of sample A; a A is the average distance from sample A to other samples within its affiliated cluster; b A is the minimum distance from sample A to other cluster centers.

[0154] The average value of the silhouette coefficients: Among them, N is the total number of samples in the dataset; represents the silhouette coefficient of sample A0; A0 = 1…N.

[0155] After determining the optimal number of clusters, use the polynomial regression analysis algorithm to perform regression analysis on the building energy consumption data in each cluster to fit its partial load rate characteristic curve, and the expression is as follows:

[0156] P day (Z) = a0 + a1Z + a2Z 2 +…+ a z Z m ;

[0157] Among them, Z represents the independent variable of the partial load rate characteristic curve; P​​day (Z) represents the load factor on the Z-th day; a0 represents the baseline energy consumption of the building when the independent variable is 0; a0 - a Z represent the regression coefficients of each term respectively; m represents the order of the polynomial.

[0158] Calculate the equivalent full - load monthly operating days (i.e., monthly equivalent effective operating days):

[0159]

[0160] where, P day (x) represents the partial load factor on the x - th month and the Z - th day; b x represents the equivalent full - load monthly operating days of the x - th month; x = 1…12;

[0161] And, the above - mentioned daily load factor is obtained from the partial load factor characteristic curve.

[0162] In this implementation, by considering multi-factor energy consumption data with various possibilities, and through data cleaning, missing value filling, and outlier handling, the accuracy and reliability of the data are ensured. Taking the buildings owned by power grid enterprises as an example, by classifying the buildings, analyzing their functional characteristics and operation models, collecting the energy consumption data of the buildings owned by power grid enterprises under different usage conditions, and identifying the operational differences of different building types, a solid foundation is provided for subsequent energy consumption analysis. On this basis, this implementation establishes an energy consumption quota index model and sets the key factors of energy consumption quota, including building area, number of users, equipment type, and energy consumption system, etc. Through data collection and analysis, the benchmark energy consumption values of various types of buildings are determined and dynamically adjusted according to the actual operation conditions. This refined modeling method can not only comprehensively reflect the actual energy consumption characteristics of buildings but also provide a scientific basis for the formulation of energy consumption quotas and carbon emission quotas. Through cluster analysis and regression analysis, this implementation can dynamically identify the electricity consumption patterns of buildings in different seasons, fit the load rate characteristic curves, thus significantly improving the accuracy and comprehensiveness of energy consumption analysis. And by calculating the equivalent full-load operation days, the complex partial load rate data is converted into energy consumption quota data that is easy to understand and apply. Combining the target energy efficiency data and actual energy consumption data, the energy-saving potential of buildings is scientifically evaluated. This evaluation method not only considers the total energy consumption but also combines multi-dimensional indicators such as carbon emissions and energy efficiency ratio, providing comprehensive data support for the energy-saving transformation and optimized management of buildings. It is particularly suitable for the situation where there are numerous and complex types of buildings in large enterprises or parks, and can classify and model the energy consumption characteristics of different types of buildings to achieve efficient management of complex building groups. Although cluster analysis and regression analysis are common methods in the existing technology in this implementation, in terms of the refined modeling of seasonal electricity load rate characteristics, the organic combination of cluster analysis and regression analysis, and the calculation method of equivalent full-load operation days, etc., this implementation can more comprehensively and accurately reflect the actual energy consumption characteristics of buildings.

[0163] In the above implementation, through the cluster analysis and regression analysis algorithms, the load rate characteristic curves of the building to be evaluated in different seasons can be accurately fitted, and the energy consumption quota data and carbon emission quota data can be preliminarily calculated. However, to further ensure the accuracy and practicality of the energy consumption quota and carbon emission quota, more detailed calculations and verifications of these data can be considered. Specifically:

[0164] In one implementation, the process of calculating the energy consumption quota data and carbon emission quota data of the building to be evaluated according to the load rate characteristic curves of the building to be evaluated in different seasons can include:

[0165] Calculate the equivalent full-load operation days of the building to be evaluated according to the load rate characteristic curves of the building to be evaluated in different seasons;

[0166] Calculate the energy consumption quota data of the building to be evaluated according to the equivalent full - load operation days of the building to be evaluated;

[0167] Calculate the carbon emission quota data of the building to be evaluated according to the energy consumption quota data;

[0168] In this implementation, through refined modeling and dynamic adjustment, it is possible to comprehensively and accurately reflect the actual energy consumption characteristics of the building. And through the calculation of the load factor characteristic curve, it is possible to dynamically identify the energy - using patterns of the building in different seasons, thus significantly improving the accuracy and comprehensiveness of energy consumption analysis. Although the calculation method of equivalent full - load operation days is not uncommon in the prior art, in this implementation, by combining multi - factor data (such as historical energy consumption data, building attribute data, meteorological data, and office data) and refined modeling methods, and dynamically adjusting the calculation results of energy consumption quota and carbon emission quota according to the load factor characteristic curve in different seasons, this dynamic adjustment ability enables this implementation to more accurately reflect the actual energy consumption of the building. Especially in the case of a large number of buildings with complex types in large enterprises or parks, it is possible to classify and model the energy consumption characteristics of different types of buildings, realizing the efficient management of complex building groups. In addition, by combining the energy consumption quota data with the carbon emission quota data, this implementation can not only quantify the energy consumption level of the building, but also provide data support for the formulation of carbon emission reduction strategies. This comprehensive evaluation method can more comprehensively and accurately reflect the actual energy consumption characteristics of the building in terms of the comprehensive application of multi - factor data, refined modeling methods, and dynamic adjustment ability, and provide a scientific basis for energy - saving potential assessment.

[0169] After calculating the equivalent full - load operation days of the building to be evaluated through the load factor characteristic curve, to further refine the calculation process of the energy consumption quota data, the overall energy consumption of the building can be decomposed into each energy - using system, so as to more accurately reflect the actual energy consumption distribution of the building. By calculating and summarizing the sub - item energy consumption data of each energy - using system, the comprehensiveness and accuracy of the energy consumption quota data can be ensured, providing a reliable basis for subsequent energy - saving potential assessment. Specifically:

[0170] In one implementation, the process of calculating the energy consumption quota data of the building to be evaluated according to the equivalent full - load operation days of the building to be evaluated described above may include:

[0171] Calculate the sub - item energy consumption data of each energy - using system in the building to be evaluated according to the equivalent full - load operation days of the building to be evaluated;

[0172] Perform a summation operation on the sub - item energy consumption data of each energy - using system to obtain the energy consumption quota data of the building to be evaluated;

[0173] Among them, the above energy consumption system may include one or more of the following: conventional energy consumption systems and special energy consumption coefficients;

[0174] The above conventional energy consumption system may include one or more of the following: heating system, refrigeration system, lighting socket system, and power system;

[0175] The above special energy consumption system may include one or more of the following: power production equipment system and power transformation equipment system. For example, the calculation formula for the energy consumption quota data of the building to be evaluated may be as follows:

[0176] E c =∑ i E i , i = g, z, m, d, s, y;

[0177] In the formula,

[0178]

[0179] Among them, E c represents the energy consumption quota data of the building to be evaluated, with the unit of kW·h / (m 2 ·a); E i represents the sub-item energy consumption data of the i-th energy consumption system; E g represents the sub-item energy consumption data of the heating system g; E z represents the sub-item energy consumption data of the refrigeration system z; E m represents the sub-item energy consumption data of the lighting socket system m; E d represents the sub-item energy consumption data of the power system d; E s represents the sub-item energy consumption data of the power production equipment system s; E y represents the sub-item energy consumption data of the power transformation equipment system y; b i represents the equivalent full-load operation days of the i-th energy consumption system in the building to be evaluated; Q imax represents the maximum daily energy consumption of the i-th energy consumption system throughout the year; S represents the building area of the building to be evaluated; b x represents the equivalent full-load monthly operation days of the x-th month; x = 1…12; P day (x) represents the partial load rate on the day day of the x-th month;

[0180] In the above implementation, through refined modeling and multi-dimensional data analysis, it is possible to comprehensively and accurately reflect the actual energy consumption characteristics of buildings, and provide a scientific basis for energy consumption management and energy-saving potential assessment. Traditional methods usually rely on a single energy consumption standard or simple monitoring means, and it is difficult to capture the complexity and differences of building energy systems. However, in this implementation, by decomposing the total building energy consumption into multiple sub-item energy consumption indicators (such as heating systems, cooling systems, lighting and socket systems, power systems, power generation equipment systems, and substation equipment systems, etc.), and combining the calculation method of equivalent full-load operation days, it is possible to dynamically identify the energy consumption characteristics of each energy system, thus significantly improving the accuracy and comprehensiveness of energy consumption quota data. Although the calculation and summation operations of sub-item energy consumption data are relatively common in the existing technology, this implementation can dynamically adjust the calculation results of energy consumption quotas according to the operating characteristics of different energy systems by combining multi-factor data (such as historical energy consumption data, building attribute data, meteorological data, and office data) and refined modeling methods. This dynamic adjustment ability enables this implementation to more accurately reflect the actual energy consumption situation of buildings. Especially in the case of a large number of buildings and complex types in large enterprises or parks, it is possible to classify and model the energy consumption characteristics of different types of buildings, realizing the efficient management of complex building groups. In addition, this implementation can provide a clear goal and basis for building energy-saving management by setting the quota level to 0.85 (indicating that the actual energy consumption cannot exceed 85% of the quota). Therefore, in terms of the comprehensive application of multi-factor data, refined modeling methods, and dynamic adjustment ability, this implementation can more comprehensively and accurately reflect the actual energy consumption characteristics of buildings, and provide a scientific basis for energy-saving potential assessment.

[0181] After calculating the energy consumption quota data of the building to be evaluated through the equivalent full-load operation days, in order to further evaluate the carbon emission level of the building, the energy consumption quota data can be combined with non-renewable energy consumption and carbon emission factors, so as to scientifically calculate the carbon emission quota data of the building. This process can not only quantify the carbon emissions of the building, but also provide data support for subsequent carbon emission reduction strategies, helping to achieve the goal of green and low-carbon development. Specifically:

[0182] In one implementation, the process of calculating the carbon emission quota data of the building to be evaluated based on the energy consumption quota data can include:

[0183] Perform a multiplication operation on the energy consumption quota data and the pre-set green certificate offset ratio to obtain the non-renewable energy consumption data of the building to be evaluated;

[0184] Perform a multiplication operation on the non-renewable energy consumption data and the pre-obtained carbon emission factor to obtain the carbon emission quota data of the building to be evaluated.

[0185] Taking the self-owned buildings of power grid enterprises as an example, this implementation method determines the carbon emission factor of the self-owned buildings of power grid enterprises according to the carbon emission factors of the country or region. If the self-owned buildings of power grid enterprises use green electricity or green certificate trading, it is necessary to calculate the carbon emission factors of conventional electricity and green electricity separately. The carbon emission factor of green electricity is usually zero, and the corresponding carbon emission calculation formula can be as follows:

[0186] C 常 =E 常 *α;

[0187] C 绿电 =E 绿电 *γ = 0;

[0188] E c =E 总 -E 绿电 -E 光伏 ;

[0189] C c =E c *(1 - p)*α;

[0190] Among them, C 常 represents the carbon emissions of conventional electricity in the self-owned buildings of power grid enterprises; E 常 represents the consumption of conventional electricity in the self-owned buildings of power grid enterprises; α represents the conventional carbon emission factor of the self-owned buildings of power grid enterprises; C 绿电 represents the carbon emissions of the green electricity part in the self-owned buildings of power grid enterprises; E 绿电 represents the electricity consumption of the green electricity part in the self-owned buildings of power grid enterprises; γ represents the carbon emission factor corresponding to the green electricity of the self-owned buildings of power grid enterprises; E c represents the energy consumption quota data; E 总 represents the total building energy consumption; E 光伏 represents the photovoltaic power generation of the self-owned buildings of power grid enterprises; C c represents the carbon emission quota data of the building to be evaluated; p represents the green certificate offset ratio of the building to be evaluated;

[0191] In this implementation method, by applying the green certificate offset ratio to the energy consumption quota data, the boundary between the use of non-renewable energy and renewable energy becomes clearer, which is conducive to enterprises achieving more accurate carbon neutrality goals. In addition, in this implementation method, by introducing different carbon emission factors to calculate the carbon emissions of conventional electricity and green electricity separately, it can ensure that the environmental benefits of using clean energy can be correctly evaluated and reflected. Especially for the scenario of using photovoltaic power generation, it reflects the contribution of enterprises' self-generated electricity to reducing the carbon footprint.

[0192] For example, the expression of the multiple linear regression algorithm in step 2 above can be as follows:

[0193] E yi= β0 + β1E 常规 + β2E 特殊 + β3S + β4T + β5Time + β6L + ∈;

[0194] Where, E yi represents the electricity consumption prediction data of the building to be evaluated; β0 represents the first regression coefficient; β1 represents the second regression coefficient; E 常规 represents the energy consumption quota data of the conventional energy system; β2 represents the third regression coefficient; E 特殊 represents the energy consumption quota data of the special energy system; β3 represents the fourth regression coefficient; S represents the building attribute data of the building to be evaluated; β4 represents the fifth regression coefficient; T represents the historical meteorological data of the building to be evaluated; β5 represents the sixth regression coefficient; Time represents the office hour data of the building to be evaluated; β6 represents the seventh regression coefficient; L represents the office staff number data of the building to be evaluated; ∈ represents the error term; In this example, using the multiple linear regression method, based on information such as the energy consumption of the building's conventional energy system, the energy consumption of the special energy system, the building attributes, the number of building personnel, and office hours, a predicted electricity consumption model of the building is constructed to accurately predict the electricity demand of the building. By fully integrating multi - aspect data such as the energy consumption characteristics, the number of users, office hours, and meteorology of the building, it demonstrates strong prediction ability. The regression coefficients in the model further reveal the specific impacts of various factors on energy consumption, enabling enterprises to make more scientific and accurate decisions when formulating energy - saving measures. This analysis ability not only improves the management efficiency of enterprises in different seasons and usage scenarios but also helps enterprises arrange energy resources more reasonably, thereby achieving cost savings and benefit maximization.

[0195] After obtaining the electricity consumption prediction data of the building to be evaluated through the multiple linear regression algorithm, to further evaluate the energy efficiency level of the building, it can be considered to compare and analyze the prediction data with the actual electricity consumption data, thereby calculating the energy efficiency ratio of the building. This process can quantify the difference between the actual energy consumption and the predicted energy consumption of the building. Specifically:

[0196] In one implementation, the process of calculating the target energy efficiency data according to the electricity consumption prediction data in step 3 above may include:

[0197] Obtain the energy efficiency ratio of the building to be evaluated based on the electricity consumption prediction data and the actual electricity consumption data of the building to be evaluated obtained;

[0198] Calculate the target energy efficiency data of the building to be evaluated according to the energy efficiency ratio of the building to be evaluated;

[0199] For example, the calculation formula of the energy efficiency ratio η of the above - mentioned building to be evaluated is as follows:

[0200]

[0201] Among them, E yi represents the predicted electricity consumption data (already normalized) of the building to be evaluated; E si represents the actual electricity consumption of the building to be evaluated.

[0202] It is stipulated that the evaluation score F = 100×(1 - the cumulative percentage corresponding to the energy efficiency ratio η). Taking the buildings owned by power grid enterprises as an example, for the estimation of the energy-saving potential of the improvement of the energy efficiency score of the buildings owned by power grid enterprises, assuming that the predicted electricity consumption data E yi after normalization remains unchanged (i.e., the influencing factors remain unchanged), the energy efficiency ratio of the building to be evaluated is η, the evaluation score is F1, and the budget requirement rises to F2. The range of the percentage reduction in energy consumption required (i.e., the target energy efficiency data) is estimated as follows:

[0203] The upper and lower limits of the energy efficiency ratio of F2 are η 2min and η 2max (η 2min < η 2max < η), and the upper and lower limits of the percentage reduction in energy consumption in the budget are ΔE% min and ΔE% max :

[0204]

[0205] The upper and lower limit ranges form the target energy efficiency data; ΔE max represents the upper limit of the reduction in energy consumption in the budget; ΔE min represents the lower limit of the reduction in energy consumption in the budget;

[0206] In this implementation method, by combining the predicted electricity consumption data with the actual electricity consumption data, not only can the energy efficiency level be evaluated, but also the energy-saving potential can be clearly identified. The calculation of the energy efficiency ratio enables enterprises to intuitively understand the efficiency of building energy use under the existing conditions, so as to formulate practical energy-saving goals. And through the introduction of the evaluation score, a quantitative method is provided to evaluate the energy efficiency performance of buildings. By setting different score criteria, enterprises are encouraged to continuously improve energy efficiency. This mechanism not only motivates enterprises to take effective energy-saving measures, but also helps with long-term energy consumption management and optimization. The change in the budget requirement enables enterprises to clearly identify the key points of future work in energy efficiency improvement. By determining the upper and lower limits of the energy efficiency target, enterprises can clarify the energy reduction amplitude required in different situations, which helps to allocate resources more precisely and formulate action plans, ultimately achieving higher energy efficiency and sustainable development.

[0207] After calculating the energy efficiency ratio and target energy efficiency data of the building to be evaluated through electricity consumption prediction data and actual electricity consumption data, in order to further comprehensively evaluate the energy-saving potential of the building, it is possible to consider combining energy consumption quota data, carbon emission quota data, and target energy efficiency data to quantitatively analyze the comprehensive energy-saving potential of the building. By calculating the energy consumption score, carbon emission score, and energy efficiency score respectively, the performance of the building in terms of energy consumption, carbon emission, and energy efficiency can be systematically evaluated, providing a scientific basis for energy-saving renovation and optimized management. Specifically:

[0208] In one implementation, in the above step 4, the process of evaluating the energy-saving potential of the building to be evaluated based on the energy consumption quota data, carbon emission quota data, and target energy efficiency data to obtain the comprehensive potential score of the building to be evaluated may include:

[0209] Determine the energy consumption score of the building to be evaluated according to the energy consumption quota data and the actual energy consumption value of the building to be evaluated obtained;

[0210] Determine the carbon emission score of the building to be evaluated according to the carbon emission quota and the actual carbon emission value of the building to be evaluated obtained;

[0211] Determine the energy efficiency score of the building to be evaluated according to the target energy efficiency data;

[0212] Evaluate the energy-saving potential of the building to be evaluated according to the energy consumption score, carbon emission score, and energy efficiency score to obtain the comprehensive potential score of the building to be evaluated;

[0213] In this implementation, taking the buildings owned by power grid enterprises as an example, first, according to the energy consumption quota data of the buildings owned by power grid enterprises, the buildings are divided into different energy efficiency levels (where E represents the actual energy consumption of the building; E c represents the energy consumption quota data of the building):

[0214]

[0215] Secondly, according to the carbon emission quota of the buildings owned by power grid enterprises, the carbon emissions of the buildings are divided into different levels (where C represents the actual carbon emissions of the building; C c represents the carbon emission quota data of the building):

[0216]

[0217]

[0218] Finally, according to the estimated energy-saving potential, the levels are divided (where ΔE% min represents the lower limit of the percentage of the budgeted reduced energy consumption):

[0219]

[0220] Combined with the energy consumption quota, carbon emission quota and energy-saving potential assessment of the power grid enterprise's own buildings, formulate a comprehensive grading standard;

[0221] For example, each score can be converted according to the following criteria: A = 4 points, B = 3 points, C = 2 points, D = 1 point;

[0222] For example, the expression of the above comprehensive potential score can be as follows:

[0223]

[0224] Among them, G represents the comprehensive potential score; E p represents the energy consumption score; C p represents the carbon emission score; represents the energy efficiency score; Q1-Q3 represent the weights corresponding to each score; Preferably, Q1 = 0.4; Q2 = 0.3; Q3 = 0.3;

[0225] According to the calculated comprehensive potential score, the final energy efficiency level can be obtained:

[0226]

[0227] In this implementation, through a comprehensive assessment of the energy consumption, carbon emissions and energy-saving potential of buildings, a comprehensive and systematic comprehensive potential scoring mechanism is established, providing strong support for the decision-making of power grid enterprises in energy conservation and emission reduction. This process is not only a quantitative analysis of the building's energy-saving ability, but also an important tool for realizing the green operation of buildings; by grading energy consumption and carbon emissions respectively and combining the comprehensive assessment of energy-saving potential, enterprises can clearly identify the specific performance and optimization space of buildings in terms of energy efficiency improvement. This systematic assessment method helps to break through the limitations of traditional single indicators, enabling management to formulate energy-saving strategies from a more macroscopic perspective. In the scoring process, the weight design is carefully considered to ensure that evaluations in different dimensions can reasonably reflect their impact on overall energy efficiency. This flexible weight allocation allows enterprises to adjust priorities according to specific needs or goals, making the evaluation results both scientific and, to a certain extent, strategically adaptable. In addition, through the classification of the comprehensive potential score, enterprises can quickly identify high-potential and low-potential buildings and then formulate corresponding improvement measures. Therefore, this implementation method can not only meet the needs of modern building energy-saving management, but also provide a reference for the formulation of future green building standards. Through the establishment of quantitative indicators and the application of the grading mechanism, it can contribute to energy-saving improvements and low-carbon emission practices in a wider range within the industry, further promoting the overall society towards the goal of sustainable development.

[0228] In summary, in view of the problem that the traditional energy-saving potential assessment method is difficult to comprehensively reflect the actual energy consumption of buildings due to only using a single energy consumption monitoring means during assessment, resulting in the inability to effectively identify potential energy-saving opportunities, the present invention proposes a building energy-saving potential assessment method based on energy consumption quotas. By deeply analyzing the functional characteristics and operation differences of buildings, scientific determination of energy consumption quota indicators is promoted for building energy-saving potential assessment based on energy consumption quotas. Through multi-source data collection and intelligent processing, the comprehensiveness and accuracy of data are ensured, providing reliable data support for energy consumption and carbon emission management. During the energy-saving assessment process, with the help of a multiple linear regression model, various influencing factors such as building attributes, environmental factors, and human behavior are comprehensively considered to achieve accurate calculation of energy consumption and carbon emission quotas, and then the energy efficiency ratio and energy-saving potential of buildings are estimated. This method provides a scientific and reasonable energy consumption management tool for enterprises, promotes the refined management of building energy consumption, helps enterprises identify energy-saving spaces, and reduces energy consumption and operating costs.

[0229] Embodiment 2:

[0230] Based on the same inventive concept, the present invention also provides a building energy-saving potential assessment system based on energy consumption quotas. The schematic structural composition diagram is as Figure 2 shown, including:

[0231] An energy consumption analysis module, configured to perform energy consumption analysis on a building to be evaluated according to the obtained multi-factor energy consumption data of the building to be evaluated by using a clustering analysis algorithm and a regression analysis algorithm, and obtain energy consumption quota data and carbon emission quota data of the building to be evaluated;

[0232] An electricity consumption prediction module, configured to perform electricity consumption prediction on the building to be evaluated based on the multi-factor energy consumption data and the energy consumption quota data by using a multiple linear regression algorithm, and obtain electricity consumption prediction data of the building to be evaluated;

[0233] An energy efficiency calculation module, configured to calculate target energy efficiency data according to the electricity consumption prediction data;

[0234] A potential assessment module, configured to perform energy-saving potential assessment on the building to be evaluated according to the energy consumption quota data, the carbon emission quota data, and the target energy efficiency data, and obtain a comprehensive potential score of the building to be evaluated.

[0235] In one implementation manner, the above-mentioned energy consumption analysis module may include:

[0236] A load characteristic calculation sub-module, configured to calculate the seasonal electricity load rate characteristics of the building to be evaluated according to the obtained multi-factor energy consumption data of the building to be evaluated;

[0237] A clustering analysis sub-module, configured to perform clustering analysis on the seasonal electricity load rate characteristics by using a clustering analysis algorithm to obtain the electricity consumption patterns of the building to be evaluated in different seasons;

[0238] A characteristic curve generation sub-module, which is used to fit the load rate characteristic curves of the building to be evaluated in different seasons by using the polynomial regression analysis algorithm according to the electricity consumption patterns of the building to be evaluated in different seasons;

[0239] A quota calculation sub-module, which is used to calculate the energy consumption quota data and carbon emission quota data of the building to be evaluated according to the load rate characteristic curves of the building to be evaluated in different seasons.

[0240] Exemplarily, the above-mentioned multi-factor energy consumption data may include one or more of the following: historical energy consumption data, building attribute data, historical meteorological data, and office data;

[0241] The historical energy consumption data may include: total energy consumption data and sub-item energy consumption data of the energy-using system;

[0242] The sub-item energy consumption data of the energy-using system may include: energy consumption data of the conventional energy-using system and energy consumption data of the special energy-using system;

[0243] The energy consumption data of the conventional energy-using system may include one or more of the following: energy consumption data of the heating system, energy consumption data of the cooling system, energy consumption data of the lighting and socket system, and energy consumption data of the power system;

[0244] The energy consumption data of the special energy-using system may include one or more of the following: energy consumption data of the power production equipment and energy consumption data of the substation equipment;

[0245] The building attribute data may include one or more of the following: building area data, building type data, location area information, and envelope structure data;

[0246] The historical meteorological data may include one or more of the following: temperature data, humidity data, wind speed data, wind direction data, air pressure data, and precipitation data of the location area;

[0247] The office data may include one or more of the following: office hour data and office population data.

[0248] In this implementation manner, the above-mentioned quota calculation sub-module may include:

[0249] A full-load operation unit, which is used to calculate the equivalent full-load operation days of the building to be evaluated according to the load rate characteristic curves of the building to be evaluated in different seasons;

[0250] An energy consumption quota calculation unit, which is used to calculate the energy consumption quota data of the building to be evaluated according to the equivalent full-load operation days of the building to be evaluated;

[0251] A carbon emission quota calculation unit, which is used to calculate the carbon emission quota data of the building to be evaluated according to the energy consumption quota data.

[0252] In this implementation manner, the above energy consumption quota calculation unit may include:

[0253] A sub-item energy consumption calculation subunit, configured to calculate sub-item energy consumption data of each energy consumption system in the building to be evaluated according to the equivalent full-load operation days of the building to be evaluated;

[0254] An energy consumption quota determination subunit, configured to perform a summation operation on the sub-item energy consumption data of each energy consumption system to obtain the energy consumption quota data of the building to be evaluated;

[0255] Wherein, the energy consumption system may include one or more of the following: a conventional energy consumption system and a special energy consumption coefficient;

[0256] The conventional energy consumption system may include one or more of the following: a heating system, a refrigeration system, a lighting and socket system, and a power system;

[0257] The special energy consumption system may include one or more of the following: an electric power production equipment system and a power transformation equipment system.

[0258] Exemplarily, the calculation formula for the energy consumption quota data of the above building to be evaluated may be as follows:

[0259] E c = ∑ i E i , i = g, z, m, d, s, y;

[0260] In the formula,

[0261]

[0262] Wherein, E c represents the energy consumption quota data of the building to be evaluated; E i represents the sub-item energy consumption data of the i-th energy consumption system; E g represents the sub-item energy consumption data of the heating system g; E z represents the sub-item energy consumption data of the refrigeration system z; E m represents the sub-item energy consumption data of the lighting and socket system m; E d represents the sub-item energy consumption data of the power system d; E s represents the sub-item energy consumption data of the electric power production equipment system s; E y represents the sub-item energy consumption data of the power transformation equipment system y; b i represents the equivalent full-load operation days of the i-th energy consumption system in the building to be evaluated; Q imax represents the maximum daily energy consumption of the i-th energy consumption system throughout the year; S represents the building area of the building to be evaluated; b x represents the equivalent full-load monthly operation days of the x-th month; x = 1... 12.

[0263] In this implementation manner, the above carbon emission quota calculation unit may include:

[0264] A non-renewable energy consumption calculation subunit, configured to perform a multiplication operation on the energy consumption quota data and a pre-set green certificate offset ratio to obtain the non-renewable energy consumption data of the building to be evaluated;

[0265] A carbon emission quota determination subunit, configured to perform a multiplication operation on the non-renewable energy consumption data and a pre-obtained carbon emission factor to obtain the carbon emission quota data of the building to be evaluated.

[0266] Exemplarily, the calculation formula of the above carbon emission quota data of the building to be evaluated may be as follows:

[0267] C c = E0 * α;

[0268] In the formula,

[0269] E0 = E c * (1 - p);

[0270] Wherein, C c represents the carbon emission quota data of the building to be evaluated; e0 represents the non-renewable energy consumption data of the building to be evaluated; E c represents the energy consumption quota data of the building to be evaluated; p represents the green certificate offset ratio of the building to be evaluated; α represents the carbon emission factor of the building to be evaluated.

[0271] Exemplarily, the expression of the above multiple linear regression algorithm may be as follows:

[0272] E yi = β0 + β1E 常规 + β2E 特殊 + β3S + β4T + β5Time + β6L + ∈;

[0273] Wherein, E yi represents the electricity consumption prediction data of the building to be evaluated; β0 represents the first regression coefficient; β1 represents the second regression coefficient; E 常规 represents the energy consumption quota data of the conventional energy system; β2 represents the third regression coefficient; E 特殊 represents the energy consumption quota data of the special energy system; β3 represents the fourth regression coefficient; S represents the building attribute data of the building to be evaluated; β4 represents the fifth regression coefficient; T represents the historical meteorological data of the building to be evaluated; β5 represents the sixth regression coefficient; Time represents the office hour data of the building to be evaluated; β6 represents the seventh regression coefficient; L represents the number of office workers in the building to be evaluated; ∈ represents the error term.

[0274] In one implementation manner, the above energy efficiency calculation module may include:

[0275] The energy efficiency ratio calculation sub-module is used to obtain the energy efficiency ratio of the building to be evaluated according to the electricity consumption prediction data and the actual electricity consumption data of the building to be evaluated obtained;

[0276] The target energy efficiency output sub-module is used to calculate the target energy efficiency data of the building to be evaluated according to the energy efficiency ratio of the building to be evaluated.

[0277] In one implementation manner, the above potential evaluation module may include:

[0278] The energy consumption evaluation sub-module is used to determine the energy consumption score of the building to be evaluated according to the energy consumption quota data and the actual energy consumption value of the building to be evaluated obtained;

[0279] The carbon emission evaluation sub-module is used to determine the carbon emission score of the building to be evaluated according to the carbon emission quota and the actual carbon emission value of the building to be evaluated obtained;

[0280] The energy efficiency evaluation sub-module is used to determine the energy efficiency score of the building to be evaluated according to the target energy efficiency data;

[0281] The energy-saving potential evaluation sub-module is used to evaluate the energy-saving potential of the building to be evaluated according to the energy consumption score, the carbon emission score and the energy efficiency score, and obtain the comprehensive potential score of the building to be evaluated.

[0282] Embodiment 3:

[0283] As Figure 3 shown, the present invention further provides an electronic device, and this electronic device may be a computer device, a single-chip microcomputer device, a smart mobile device, etc. The electronic device in this embodiment may include a processor, a memory, a transceiver component, etc. The memory, the processor and the transceiver component are connected by a bus; the memory can be used to store an execution program, and an exemplary execution program may include instructions; the processor is used to execute the instructions stored in the memory. The memory can also be used to store data, and this data can be called and / or modified when the instructions are executed.

[0284] The processor may be a Central Processing Unit (CPU), or it may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and is suitable for implementing one or more instructions. Specifically, it is suitable for loading and executing one or more instructions in the storage medium to implement the corresponding method flow or corresponding function, so as to implement the steps of a building energy-saving potential assessment method based on energy consumption quota in the above embodiments.

[0285] Embodiment 4:

[0286] Based on the same inventive concept, the present invention also provides a readable storage medium, specifically an electronic device-readable storage medium (Memory). The electronic device-readable storage medium is a memory device in the electronic device, used to store programs and data. It can be understood that the storage medium here can include both the built-in storage medium in the electronic device, and of course, can also include the extended storage medium supported by the electronic device. The storage medium provides a storage space, and this storage space stores the operating system of the terminal. And, in this storage space, there are also stored one or more instructions suitable for being loaded and executed by the processor. These instructions can be one or more execution programs (including program codes). It should be noted that the storage medium here can be a high-speed RAM memory, or a non-volatile memory, such as at least one disk memory. By the processor loading and executing one or more instructions stored in the storage medium, the steps of a building energy-saving potential assessment method based on energy consumption quota in the above embodiments can be implemented.

[0287] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes.

[0288] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combinations of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable data processing devices to generate a machine, such that the instructions executed by the processors of the computer or other programmable data processing devices produce means for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0289] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including instruction means that implement the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0290] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0291] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit the scope of its protection. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that after reading the present invention, various changes, modifications, or equivalent replacements can still be made to the specific implementation manners of the application. However, these changes, modifications, or equivalent replacements are all within the scope of the protection of the pending claims of the application.

Claims

1. A method for evaluating the building energy-saving potential based on energy consumption quotas, characterized in that Including: According to the multi-factor energy consumption data of the building to be evaluated obtained, use the clustering analysis algorithm and the regression analysis algorithm to perform energy consumption analysis on the building to be evaluated, and obtain the energy consumption quota data and carbon emission quota data of the building to be evaluated; Based on the multi-factor energy consumption data and the energy consumption quota data, use the multiple linear regression algorithm to predict the electricity consumption of the building to be evaluated, and obtain the electricity consumption prediction data of the building to be evaluated; Calculate the target energy efficiency data according to the electricity consumption prediction data; According to the energy consumption quota data, carbon emission quota data and the target energy efficiency data, evaluate the energy-saving potential of the building to be evaluated, and obtain the comprehensive potential score of the building to be evaluated.

2. The method according to claim 1, characterized in that The step of according to the multi-factor energy consumption data of the building to be evaluated obtained, using the clustering analysis algorithm and the regression analysis algorithm to perform energy consumption analysis on the building to be evaluated, and obtaining the energy consumption quota data and carbon emission quota data of the building to be evaluated includes: According to the multi-factor energy consumption data of the building to be evaluated obtained, calculate the seasonal electricity load rate characteristics of the building to be evaluated; Use the clustering analysis algorithm to perform clustering analysis on the seasonal electricity load rate characteristics, and obtain the electricity consumption patterns of the building to be evaluated in different seasons; According to the electricity consumption patterns of the building to be evaluated in different seasons, use the polynomial regression analysis algorithm to fit the load rate characteristic curves of the building to be evaluated in different seasons; According to the load rate characteristic curves of the building to be evaluated in different seasons, calculate the energy consumption quota data and carbon emission quota data of the building to be evaluated.

3. The method according to claim 2, wherein The step of according to the load rate characteristic curves of the building to be evaluated in different seasons, calculating the energy consumption quota data and carbon emission quota data of the building to be evaluated includes: According to the load rate characteristic curves of the building to be evaluated in different seasons, calculate the equivalent full-load operation days of the building to be evaluated; According to the equivalent full-load operation days of the building to be evaluated, calculate the energy consumption quota data of the building to be evaluated; According to the energy consumption quota data, calculate the carbon emission quota data of the building to be evaluated.

4. The method according to claim 3, wherein The step of according to the equivalent full-load operation days of the building to be evaluated, calculating the energy consumption quota data of the building to be evaluated includes: According to the equivalent full-load operation days of the building to be evaluated, calculate the sub-item energy consumption data of each energy-using system in the building to be evaluated; Perform a summation operation on the sub-item energy consumption data of each energy-using system to obtain the energy consumption quota data of the building to be evaluated; Wherein, the energy-using system includes one or more of the following: conventional energy-using systems and special energy-using coefficients; The conventional energy-using system includes one or more of the following: heating system, refrigeration system, lighting and socket system, and power system; The special energy-using system includes one or more of the following: power production equipment system and substation equipment system.

5. The method according to claim 4, characterized in that, The calculation formula of the energy consumption quota data of the building to be evaluated is as follows: E c = ∑ i E i , where i = g, z, m, d, s, y; In the formula, Among them, E c represents the energy consumption quota data of the building to be evaluated; E i represents the sub-item energy consumption data of the i-th energy-using system; E g represents the sub-item energy consumption data of the heating system g; E z represents the sub-item energy consumption data of the refrigeration system z; E m represents the sub-item energy consumption data of the lighting and socket system m; E d represents the sub-item energy consumption data of the power system d; E s represents the sub-item energy consumption data of the power production equipment system s; E y represents the sub-item energy consumption data of the substation equipment system y; b i represents the equivalent full-load operation days of the i-th energy-using system in the building to be evaluated; Q imax represents the maximum daily energy consumption of the i-th energy-using system throughout the year; S represents the building area of the building to be evaluated; b x represents the equivalent full-load monthly operation days of the x-th month; x = 1…12.

6. The method according to claim 3, characterized in that, The step of according to the energy consumption quota data, calculating the carbon emission quota data of the building to be evaluated includes: Perform a multiplication operation on the energy consumption quota data and the pre-set green certificate offset ratio to obtain the non-renewable energy consumption data of the building to be evaluated; Performing a multiplication operation on the non-renewable energy consumption data and the pre-acquired carbon emission factors to obtain the carbon emission quota data of the building to be evaluated.

7. The method according to claim 6, wherein The calculation formula for the carbon emission quota data of the building to be evaluated is as follows: C c = E0 * α; In the formula, E0 = E c *(1 - p); Among them, C c represents the carbon emission quota data of the building to be evaluated; E0 represents the non-renewable energy consumption data of the building to be evaluated; E c represents the energy consumption quota data of the building to be evaluated; p represents the green certificate offset ratio of the building to be evaluated; α represents the carbon emission factor of the building to be evaluated.

8. The method according to claim 1, characterized in that, The multi-factor energy consumption data includes one or more of the following: historical energy consumption data, building attribute data, historical meteorological data, and office data; The historical energy consumption data includes: total energy consumption data and sub-item energy consumption data of the energy consumption system; The sub-item energy consumption data of the energy consumption system includes: energy consumption data of the conventional energy consumption system and energy consumption data of the special energy consumption system; The energy consumption data of the conventional energy consumption system includes one or more of the following: heating system energy consumption data, refrigeration system energy consumption data, lighting and socket system energy consumption data, and power system energy consumption data; The energy consumption data of the special energy consumption system includes one or more of the following: energy consumption data of power production equipment and energy consumption data of substation equipment; The building attribute data includes one or more of the following: building area data, building type data, location area information, and enclosure structure data; The historical meteorological data includes one or more of the following: temperature data, humidity data, wind speed data, wind direction data, air pressure data, and precipitation data of the location area; The office data includes one or more of the following: office hours data and number of office workers data.

9. The method according to claim 1 or 8, characterized in that, The expression of the multiple linear regression algorithm is as follows: E yi = β0 + β1E 常规 + β2E 特殊 + β3S + β4T + β5Time + β6L + ∈; Among them, E yi represents the electricity consumption prediction data of the building to be evaluated; β0 represents the first regression coefficient; β1 represents the second regression coefficient; E 常规 represents the energy consumption quota data of the conventional energy system; β2 represents the third regression coefficient; E 特殊 represents the energy consumption quota data of the special energy system; β3 represents the fourth regression coefficient; S represents the building attribute data of the building to be evaluated; β4 represents the fifth regression coefficient; T represents the historical meteorological data of the building to be evaluated; β5 represents the sixth regression coefficient; Time represents the office hour data of the building to be evaluated; β6 represents the seventh regression coefficient; L represents the number of office workers data of the building to be evaluated; ∈ represents the error term.

10. The method according to claim 1, characterized in that, Calculating the target energy efficiency data according to the electricity consumption prediction data includes: Obtaining the energy efficiency ratio of the building to be evaluated based on the electricity consumption prediction data and the actual electricity consumption data of the building to be evaluated obtained; Calculating the target energy efficiency data of the building to be evaluated according to the energy efficiency ratio of the building to be evaluated.

11. The method according to claim 1, wherein Evaluating the energy-saving potential of the building to be evaluated based on the energy consumption quota data, carbon emission quota data, and the target energy efficiency data to obtain the comprehensive potential score of the building to be evaluated, including: Determining the energy consumption score of the building to be evaluated based on the energy consumption quota data and the actual energy consumption value of the building to be evaluated obtained; Determining the carbon emission score of the building to be evaluated based on the carbon emission quota and the actual carbon emission value of the building to be evaluated obtained; Determining the energy efficiency score of the building to be evaluated based on the target energy efficiency data; Evaluating the energy-saving potential of the building to be evaluated based on the energy consumption score, the carbon emission score, and the energy efficiency score to obtain the comprehensive potential score of the building to be evaluated.

12. An energy-saving potential assessment system for buildings based on energy consumption quotas, characterized in that, Including: An energy consumption analysis module, configured to perform energy consumption analysis on the building to be evaluated by using a clustering analysis algorithm and a regression analysis algorithm according to the multi-factor energy consumption data of the building to be evaluated obtained, to obtain the energy consumption quota data and the carbon emission quota data of the building to be evaluated; An electricity consumption prediction module, configured to perform electricity consumption prediction on the building to be evaluated by using a multiple linear regression algorithm based on the multi-factor energy consumption data and the energy consumption quota data, to obtain the electricity consumption prediction data of the building to be evaluated; An energy efficiency calculation module, configured to calculate the target energy efficiency data according to the electricity consumption prediction data; A potential evaluation module for evaluating the energy-saving potential of the building to be evaluated based on the energy consumption quota data, carbon emission quota data, and the target energy efficiency data, and obtaining the comprehensive potential score of the building to be evaluated.

13. The system according to claim 12, characterized in that, The energy consumption analysis module includes: A load characteristic calculation sub-module for calculating the seasonal electricity load rate characteristics of the building to be evaluated based on the obtained multi-factor energy consumption data of the building to be evaluated. A clustering analysis sub-module for performing clustering analysis on the seasonal electricity load rate characteristics using a clustering analysis algorithm to obtain the electricity consumption patterns of the building to be evaluated in different seasons. A characteristic curve generation sub-module for fitting the load rate characteristic curves of the building to be evaluated in different seasons using a polynomial regression analysis algorithm based on the electricity consumption patterns of the building to be evaluated in different seasons. A quota calculation sub-module for calculating the energy consumption quota data and carbon emission quota data of the building to be evaluated based on the load rate characteristic curves of the building to be evaluated in different seasons.

14. The system according to claim 13, wherein The quota calculation sub-module includes: A full-load operation unit for calculating the equivalent full-load operation days of the building to be evaluated based on the load rate characteristic curves of the building to be evaluated in different seasons. An energy consumption quota calculation unit for calculating the energy consumption quota data of the building to be evaluated based on the equivalent full-load operation days of the building to be evaluated. A carbon emission quota calculation unit for calculating the carbon emission quota data of the building to be evaluated based on the energy consumption quota data.

15. The system according to claim 14, wherein, The energy consumption quota calculation unit includes: A sub-item energy consumption calculation sub-unit for calculating the sub-item energy consumption data of each energy-using system in the building to be evaluated based on the equivalent full-load operation days of the building to be evaluated. An energy consumption quota determination sub-unit for performing a summation operation on the sub-item energy consumption data of each energy-using system to obtain the energy consumption quota data of the building to be evaluated. Wherein, the energy-using system includes one or more of the following: a conventional energy-using system and a special energy-using coefficient; The conventional energy-using system includes one or more of the following: a heating system, a cooling system, a lighting and socket system, and a power system; The special energy-using system includes one or more of the following: an electric power production equipment system and a substation equipment system.

16. The system according to claim 15, wherein The calculation formula for the energy consumption quota data of the building to be evaluated is as follows: E C = ∑ i E i , where i = g, z, m, d, s, y; In the formula, Among them, E c represents the energy consumption quota data of the building to be evaluated; E i represents the sub-item energy consumption data of the i-th energy-using system; E g represents the sub-item energy consumption data of the heating system g; E z represents the sub-item energy consumption data of the cooling system z; E m represents the sub-item energy consumption data of the lighting and socket system m; E d represents the sub-item energy consumption data of the power system d; E s represents the sub-item energy consumption data of the power production equipment system s; E y represents the sub-item energy consumption data of the substation equipment system y; b i represents the equivalent full-load operation days of the i-th energy-using system in the building to be evaluated; Q imax represents the maximum daily energy consumption of the i-th energy-using system throughout the year; S represents the building area of the building to be evaluated; b x represents the equivalent full-load monthly operation days of the x-th month; x = 1…12.

17. The system according to claim 14, wherein The carbon emission quota calculation unit includes: A non-renewable energy consumption calculation sub-unit for performing a multiplication operation on the energy consumption quota data and a pre-set green certificate offset ratio to obtain the non-renewable energy consumption data of the building to be evaluated. A carbon emission quota determination sub-unit for performing a multiplication operation on the non-renewable energy consumption data and a pre-obtained carbon emission factor to obtain the carbon emission quota data of the building to be evaluated.

18. The system according to claim 17, wherein The calculation formula for the carbon emission quota data of the building to be evaluated is as follows: C c = E0 * α; In the formula, b0 = E c *(1 - p); Among them, C c represents the carbon emission quota data of the building to be evaluated; E0 represents the non-renewable energy consumption data of the building to be evaluated; E c represents the energy consumption quota data of the building to be evaluated; p represents the green certificate offset ratio of the building to be evaluated; α represents the carbon emission factor of the building to be evaluated.

19. The system according to claim 12, wherein The multi-factor energy consumption data includes one or more of the following: historical energy consumption data, building attribute data, historical meteorological data, and office data; The historical energy consumption data includes: total energy consumption data and sub-item energy consumption data of the energy-using system. The sub-item energy consumption data of the energy-using system include: the energy consumption data of the conventional energy-using system and the energy consumption data of the special energy-using system; The energy consumption data of the conventional energy-using system include one or more of the following: the energy consumption data of the heating system, the energy consumption data of the cooling system, the energy consumption data of the lighting and socket system, and the energy consumption data of the power system; The energy consumption data of the special energy-using system include one or more of the following: the energy consumption data of the power production equipment and the energy consumption data of the substation equipment; The building attribute data include one or more of the following: the building area data, the building type data, the location area information, and the envelope structure data; The historical meteorological data include one or more of the following: the temperature data, the humidity data, the wind speed data, the wind direction data, the air pressure data, and the precipitation data of the location area; The office data include one or more of the following: the office hours data and the number of office workers data.

20. The system according to claim 12 or 19, characterized in that The expression of the multiple linear regression algorithm is as follows: E yi = β0 + β1E 常规 + β2E 特殊 + β3S + β4T + β5Time + β6L + ∈; Among them, E yi represents the electricity consumption prediction data of the building to be evaluated; β0 represents the first regression coefficient; β1 represents the second regression coefficient; E 常规 represents the energy consumption quota data of the conventional energy system; β2 represents the third regression coefficient; E 特殊 represents the energy consumption quota data of the special energy system; β3 represents the fourth regression coefficient; S represents the building attribute data of the building to be evaluated; β4 represents the fifth regression coefficient; T represents the historical meteorological data of the building to be evaluated; β5 represents the sixth regression coefficient; Time represents the office hour data of the building to be evaluated; β6 represents the seventh regression coefficient; L represents the number of office workers data of the building to be evaluated; ∈ represents the error term.

21. The system according to claim 12, wherein The energy efficiency calculation module includes: The energy efficiency ratio calculation sub-module is used to obtain the energy efficiency ratio of the building to be evaluated according to the electricity consumption prediction data and the actual electricity consumption data of the building to be evaluated obtained; The target energy efficiency output sub-module is used to calculate the target energy efficiency data of the building to be evaluated according to the energy efficiency ratio of the building to be evaluated.

22. The system according to claim 12, wherein The potential evaluation module includes: The energy consumption evaluation sub-module is used to determine the energy consumption score of the building to be evaluated according to the energy consumption quota data and the actual energy consumption value of the building to be evaluated obtained; The carbon emission evaluation sub-module is used to determine the carbon emission score of the building to be evaluated according to the carbon emission quota and the actual carbon emission value of the building to be evaluated obtained; The energy efficiency evaluation sub-module is used to determine the energy efficiency score of the building to be evaluated according to the target energy efficiency data; The energy conservation potential evaluation sub-module is used to conduct an energy conservation potential evaluation on the building to be evaluated according to the energy consumption score, the carbon emission score, and the energy efficiency score, and obtain the comprehensive potential score of the building to be evaluated.

23. An electronic device, characterized in that, Include: At least one processor and a memory; The memory and the processor are connected by a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, an energy conservation potential evaluation method for a building based on an energy consumption quota as described in any one of claims 1 to 11 is implemented.

24. A computer-readable storage medium, characterized in that, There is an execution program stored thereon, and when the execution program is executed, an energy conservation potential evaluation method for a building based on an energy consumption quota as described in any one of claims 1 to 11 is implemented.

Images