Energy monitoring method and system based on smart city

Abstract

The present invention relates to the field of smart city energy supervision, and in particular to an energy monitoring method and system based on a smart city, comprising: deploying sensors and metering equipment at different locations in a city to obtain data information, classifying urban building facilities according to building scale, building purpose, facility type, and facility efficiency, and calculating the influence degree relationship between building facilities under different classification standards; judging according to the influence degree relationship between smart city building facilities, setting the energy consumption in the smart city, and obtaining energy consumption adjustment parameters; optimizing the energy consumption according to the energy consumption adjustment parameters and a preset energy consumption reference adjustment benchmark; establishing an energy demand prediction model in combination with the optimized energy consumption and the energy information obtained in the smart city to predict future energy demand; and monitoring the energy situation of the smart city through the predicted energy demand and the change in energy consumption.

Description

Technical Field

[0001] The present invention relates to the field of smart city energy supervision, and in particular to an energy monitoring method and system based on a smart city. Background Art

[0002] With the acceleration of urbanization and the continuous growth of population size, the demand for energy has also increased, and cities are facing increasing pressure on energy consumption; cities are the main places for energy consumption, including electricity, gas, heat and other energy forms. At the same time, with the development of the economy and the improvement of residents' living standards, urban energy consumption has continued to grow, and energy supply pressure has increased. As residents' requirements for quality of life continue to increase, they need a stable and safe energy supply, which requires the use of data analysis and other methods to monitor smart city energy.

[0003] However, the existing technology still has the following problems: energy monitoring methods often rely on equipment collection and simple data analysis, which are inefficient and have limited accuracy; it is impossible to uniformly divide the building facilities in the smart city to analyze the impact relationship between them, and thus analyze the energy situation; it is not possible to accurately grasp the energy consumption and energy demand in the smart city. Because it is impossible to further monitor the energy, it causes energy waste, poor decision-making, and increased urban operation costs.

[0004] Therefore, there is a need for an energy monitoring method and system based on a smart city. Summary of the invention

[0005] The present invention provides an energy monitoring method and system based on smart city, which aims to improve the efficiency and intelligence level of urban energy monitoring through accurate data analysis, realize the sustainable use of energy, and promote the sustainable development and intelligence process of the city.

[0006] The technical solution of the present invention is specifically as follows:

[0007] An energy monitoring method based on a smart city comprises the following steps:

[0008] Step S1. Deploy sensors and metering equipment at different locations in the city to obtain data information, classify urban building facilities according to building scale, building purpose, facility type, and facility efficiency, and calculate the impact relationship between building facilities under different classification standards;

[0009] Step S2. According to the influence degree relationship between smart city building facilities, the energy consumption in the smart city is determined, and the energy consumption adjustment parameter is obtained;

[0010] Step S3. Optimize the energy consumption according to the energy consumption adjustment parameter and the preset energy consumption reference adjustment benchmark;

[0011] Step S4. Combine the optimized energy consumption and the acquired energy information in the smart city to establish an energy demand forecasting model to predict future energy demand;

[0012] Step S5. Monitor the energy situation of the smart city through the forecast of energy demand and the change of energy consumption.

[0013] Further, step S1 specifically includes:

[0014] By dividing the building facilities of the smart city and extracting features, the association weight of the building facility information set according to the building scale is obtained: ; The associated weight of the data information set divided according to the building use is ; The associated weight of the data information set divided by facility type is ; The associated weight of the data information set divided according to facility efficiency is .

[0015] Furthermore, the impact relationship between building facilities under different classification standards is calculated. The specific process is as follows:

[0016] ;

[0017] in, Indicates the stability of user payment status; , , , They represent the weights of the association weights of the building facility information set according to the building scale, building purpose, facility type, and facility efficiency, respectively. ; Indicates the proportion coefficient of different influencing factors; Indicates that the building facilities are The characteristic influence of each interference factor; Represents the influence coefficient.

[0018] Further, step S2 specifically includes:

[0019] Define the judgment threshold as , ,and , Indicates the impact relationship between smart city building facilities; energy consumption is set to ;

[0020] if , indicating that the influence between smart city building facilities is high; if , indicating that the impact between smart city building facilities is medium; if , indicating that the impact between smart city building facilities is low; among them, represents the judgment coefficient; Indicates bias; represents the exponential coefficient;

[0021] Furthermore, the energy consumption adjustment parameters are calculated based on environmental conditions and energy equipment maintenance frequency. , the specific process is as follows:

[0022] ;

[0023] in, Indicated in The total amount of energy consumption obtained at the moment; represents the regulation coefficient of energy consumption; Indicated in The energy usage fees that need to be paid are accumulated at all times; represents energy utilization rate; The function representing the impact of energy equipment maintenance frequency on energy consumption; Indicates the maintenance frequency of energy equipment; Indicates the impact of dynamic changes in environmental conditions on energy consumption; Represents the correction factor.

[0024] Further, step S3 specifically includes:

[0025] Define the first energy consumption reference adjustment benchmark as The second energy consumption reference adjustment benchmark is ,in, ;

[0026] if , the optimized energy consumption is , ;

[0027] if , the optimized energy consumption is , ;

[0028] if , the optimized energy consumption is , .

[0029] Furthermore, the energy demand prediction model is established to analyze the data. The specific process is as follows:

[0030] ;

[0031] in, represents the established energy demand forecasting model; It represents the smart city energy data set collected by different types of sensors, including electricity meter sensors, water meter sensors, and gas sensors; represents the energy loss data set recorded during the monitoring process; Indicates the energy utilization transformation of smart cities; Represents the output of the energy demand forecasting model.

[0032] Further, step S5 specifically includes:

[0033] ;

[0034] in, Monitoring indicators that represent the energy situation of smart cities; Indicates the current time Predicted value of smart city energy demand; represents the compensation coefficient; Indicates the current time Expected smart city energy demand; Indicates the energy demand forecast result of the previous time; Indicated in The energy consumption at that time.

[0035] An energy monitoring system based on a smart city includes the following contents:

[0036] Data acquisition and processing module, building facility classification module, impact degree relationship module, energy consumption regulation module, energy consumption optimization module, energy demand prediction module, smart city energy monitoring module;

[0037] The data acquisition and processing module is responsible for collecting the energy consumption data of the smart city from sensors, devices and data sources;

[0038] Building facility classification module, by classifying building facilities, divides the city's buildings according to building scale, building purpose, facility type, and facility efficiency;

[0039] The impact relationship module divides the building facility information of the smart city in detail, calculates the impact relationship between the building facilities under different classification standards, and provides a basis for subsequent energy regulation and optimization;

[0040] Energy consumption adjustment module, which calculates energy consumption adjustment parameters based on real-time data and impact degree relationship, combined with environmental conditions and energy equipment maintenance frequency;

[0041] Energy consumption optimization module, which analyzes and optimizes energy consumption data according to energy consumption adjustment parameters and compares them with preset energy consumption reference adjustment benchmarks;

[0042] Energy demand forecasting module, based on historical data and trend analysis, uses deep learning to establish an energy demand forecasting model to predict future energy demand;

[0043] The smart city energy monitoring module combines the forecast of energy demand with the changes in energy consumption, sets monitoring indicators for the smart city energy situation, and monitors the smart city energy situation.

[0044] Beneficial effects: 1. The present invention helps to improve overall energy efficiency, reduce energy waste and lower operating costs by identifying and optimizing the mutual influence between building facilities; it can analyze the synergy between building facilities, enhance the overall performance of urban building systems and promote the sustainable development of cities; and through the influence relationship between smart city building facilities, it can provide data support for calculating subsequent energy consumption.

[0045] 2. The present invention can help optimize energy utilization and reduce the impact on the environment by setting the energy consumption in the smart city and calculating the energy consumption adjustment parameters; by monitoring the energy consumption of building facilities through smart technology, it can identify and improve the links with low energy utilization efficiency, reduce energy waste, and achieve effective management and regulation of energy consumption.

[0046] 3. By comparing the adjustment parameters with the benchmark values, the present invention can identify the energy consumption differences and potential optimization space in building facilities, thereby realizing refined energy management; optimizing energy consumption can reduce energy use costs and adverse impacts on the environment, such as reducing greenhouse gas emissions and reducing demand for natural resources, which helps protect the environment.

[0047] 4. By predicting future energy demand, the present invention can help city managers better plan and allocate energy resources, ensure the balance of energy supply and demand in the city, avoid energy supply shortages or surpluses, help city managers optimize the energy supply chain and energy utilization methods, improve energy utilization efficiency and operational efficiency, and reduce energy costs.

[0048] 5. The present invention utilizes monitoring indicators to monitor in real time the predicted situation of energy demand and the changes in actual energy consumption, helping city managers to timely adjust energy supply and energy utilization strategies to cope with emergencies or changing demands; by monitoring changes in energy situation indicators, timely evaluating the effectiveness of policies, facilitating the discovery of energy problems and hidden dangers, improving the city's emergency response capabilities, effectively handling energy supply shortages or other emergencies, and ensuring a balance between energy supply and demand. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 A flow chart of an energy monitoring method based on a smart city according to the present invention;

[0050] Figure 2 This is a module diagram of an energy monitoring system based on a smart city of the present invention. DETAILED DESCRIPTION

[0051] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods. It should also be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0052] Refer to the attached Figure 1 , this embodiment provides an energy monitoring method based on a smart city, comprising the following steps:

[0053] S1. Deploy smart sensors and metering equipment, including but not limited to electricity meter sensors, water meter sensors, gas sensors, etc., at various key energy consumption points in different locations in the city, such as buildings, transportation systems, industrial facilities, etc., to monitor the use of various energy sources in real time, and transmit data to the data collection center through the Internet or other communication networks for further analysis and processing.

[0054] In an embodiment of the present invention, a smart city building facility data information set is obtained. , ;in, Represents the total number of buildings and facilities in the smart city, set Any element in express, , represents the qth building facility information subset; specifically, , P represents the number of q-th building facility information, the set Any element of express, , Represents the pth information of the qth building facility.

[0055] Urban building facilities are divided according to building scale, building purpose, facility type, and facility efficiency. The impact degree relationship between building facilities under different classification standards is calculated, and energy consumption is defined based on the correlation.

[0056] According to the building scale, the building scale includes large buildings, medium buildings and small buildings. Specifically, large buildings are buildings with large building areas and high energy consumption; medium buildings are buildings of moderate size; small buildings are buildings with small building areas and relatively low energy consumption. The specific process is as follows:

[0057] ;

[0058] in, Represents a large building-level collection; A collection representing the middle-sized building level; represents a collection of small building classes; and .

[0059] According to the purpose of the building, the building uses include residential buildings, commercial buildings, industrial buildings, public buildings, and mixed-use buildings. Specifically, residential buildings include apartments, villas, townhouses, etc.; commercial buildings include shopping centers, office buildings, hotels, etc.; industrial buildings include factories, warehouses, logistics centers, etc.; public buildings include schools, hospitals, gymnasiums, libraries, etc.; mixed-use buildings are buildings that combine multiple functions such as residential, commercial, and office. The specific process is as follows:

[0060] ;

[0061] in, A collection representing the residential building hierarchy; A collection representing the commercial building hierarchy; Represents a collection of industrial building levels; Represents a collection of common building levels; represents a collection of mixed-use building tiers; and .

[0062] According to the type of facilities, the types of facilities include transportation facilities, public facilities, and green facilities. Specifically, transportation facilities include electric vehicle charging stations, public transportation systems (subways, buses), etc.; public facilities include public services such as lighting, water supply, drainage, and garbage disposal; and green facilities include urban green spaces, park irrigation systems, lighting, etc. The specific process is as follows:

[0063] ;

[0064] in, Represents a collection of transportation facility levels; Represents a collection of utility levels; represents a collection of green facility hierarchies; and .

[0065] According to the efficiency of facilities, the efficiency of facilities includes high-efficiency facilities and low-efficiency facilities. Specifically, high-efficiency facilities include energy-saving buildings, renewable energy facilities, smart grids and energy storage systems, etc.; low-efficiency facilities include old buildings, traditional energy facilities, high-emission transportation facilities and buildings, traditional power grids, etc. Specifically, it can be expressed as .

[0066] After a detailed division of the building facility information of the smart city, feature extraction is performed using existing technology; the impact relationship between building facilities under different classification standards is calculated.

[0067] First, calculate the correlation weights between any two smart city buildings. Take the data information of the building scale division set as an example. The process is as follows:

[0068] ;

[0069] in, Represents the scale correlation between any two building facilities; Indicates The weighting factor of the scale of each building facility; Indicates The weighting factor of the scale of each building facility; Represents a collection based on building size When building facilities and The probability of intersection; , Respectively represent , Characteristic information of buildings and facilities; Represents a collection of building scale categories Middle Energy saving of each building facility; Represents a collection of building scale categories Middle Energy saving of building facilities.

[0070] From this, we can derive the association weight of the building facility information set according to the building scale: , ,in, express A set of association weights divided by building size.

[0071] Therefore, the association weight of the data information set divided according to the building use can be obtained as follows: ; The associated weight of the data information set divided by facility type is ; The associated weight of the data information set divided according to facility efficiency is .

[0072] The relationship between the influence degree of building facilities under different classification standards is now defined as follows: , the specific process is as follows:

[0073] ;

[0074] in, Indicates the stability of user payment status; , , , They represent the weights of the association weights of the building facility information set according to the building scale, building purpose, facility type, and facility efficiency, respectively. ; Indicates the proportion coefficient of different influencing factors; Indicates that the building facilities are The characteristic influence of each interference factor; Represents the influence coefficient.

[0075] The present invention helps to improve overall energy efficiency, reduce energy waste and lower operating costs by identifying and optimizing the mutual influence between building facilities. It can analyze the synergy between building facilities, enhance the overall performance of urban building systems, promote the sustainable development of cities, and improve service quality and user satisfaction. It can also provide data support for calculating subsequent energy consumption through the influence relationship between smart city building facilities.

[0076] S2. According to the influence relationship between smart city building facilities, determine the energy consumption in the smart city and calculate the energy consumption adjustment parameters.

[0077] According to the relationship between the impact of smart city building facilities Further judgment is made, and the energy consumption in the smart city is set as E according to the judgment result. In this embodiment, the judgment threshold is defined as , , which is pre-set by a large amount of experimental data and expert experience, and , the preset energy consumption is , the specific process is as follows:

[0078] if , indicating that the impact between smart city building facilities is high, so the energy consumption is set , ; represents a first preset adjustment coefficient;

[0079] if , indicating that the impact between smart city building facilities is medium, then the energy consumption is set , ; represents a first preset adjustment coefficient;

[0080] if , indicating that the impact between smart city building facilities is low, so the energy consumption is set , ; represents a first preset adjustment coefficient;

[0081] in, represents the judgment coefficient; Indicates bias; represents the exponential coefficient; represents the error term; t represents the total duration of the recording; It represents the difference between the expected energy consumption and the actual energy consumption monitored; represents the coefficient of variation of equipment efficiency in smart cities; Table 1. Energy price change coefficient; The coefficient of variation that indicates energy supply conditions; , , Represent the corresponding weights respectively.

[0082] Calculate energy consumption adjustment parameters based on environmental conditions and energy equipment maintenance frequency , the specific process is as follows:

[0083] ;

[0084] in, Indicated in The total amount of energy consumption obtained at the moment; represents the regulation coefficient of energy consumption; Indicated in The energy usage fees that need to be paid are accumulated at all times; represents energy utilization rate; The function representing the impact of energy equipment maintenance frequency on energy consumption; Indicates the maintenance frequency of energy equipment; Indicates the impact of dynamic changes in environmental conditions on energy consumption; Represents the correction factor.

[0085] The present invention can help optimize energy utilization and reduce the impact on the environment by setting the energy consumption in the smart city and calculating the energy consumption adjustment parameters; by monitoring the energy consumption of building facilities through smart technology, it can identify and improve the links with low energy utilization efficiency, reduce energy waste, and achieve effective management and regulation of energy consumption.

[0086] S3. Optimize the energy consumption by comparing the energy consumption adjustment parameter with the preset energy consumption reference adjustment benchmark.

[0087] In the embodiment of the present invention, the first energy consumption reference adjustment benchmark is defined as The second energy consumption reference adjustment benchmark is ,in, ; Adjust the energy consumption parameters Respectively , Compare and optimize the energy consumption according to the comparison results. The specific process is as follows:

[0088] if , the optimized energy consumption is , ;

[0089] if , the optimized energy consumption is , ;

[0090] if , the optimized energy consumption is , .

[0091] By comparing the adjustment parameters with the benchmark values, the present invention can identify the energy consumption differences and potential optimization space in building facilities, thereby realizing refined energy management; optimizing energy consumption can reduce energy use costs and adverse impacts on the environment, such as reducing greenhouse gas emissions and reducing the demand for natural resources, which helps to protect the environment.

[0092] S4. Based on the optimized energy consumption and the energy-related information obtained in the smart city, an energy demand forecasting model is established to predict future energy demand, optimize energy supply and utilization in a targeted manner, and effectively monitor energy.

[0093] By building an energy demand prediction model through deep learning to analyze the data, we can get the energy demand situation in the smart city. The specific process is as follows:

[0094] ;

[0095] in, represents the established energy demand forecasting model; It represents the smart city energy data set collected by different types of sensors, including electricity meter sensors, water meter sensors, gas sensors, etc. represents the energy loss data set recorded during the monitoring process; Indicates the energy utilization transformation of smart cities; Represents the output result of the energy demand forecasting model. The specific calculation process is as follows:

[0096] ;

[0097] in, Indicated in Energy data collected by various sensors at a certain point in time; represents the number of samples; Indicated in Energy consumption data at each time point; Indicated in Energy consumption data recorded at each time point; Indicates the effect of smart city energy utilization transformation on the model; Represents the stability coefficient of the prediction process; A grade coefficient that indicates the conversion of energy utilization.

[0098] By predicting future energy demand, the present invention can help city managers better plan and allocate energy resources, ensure the balance of energy supply and demand in the city, avoid energy supply shortages or surpluses, and help city managers optimize the energy supply chain and energy utilization methods, improve energy utilization efficiency and operational efficiency, and reduce energy costs.

[0099] S5. Monitor the energy situation of the smart city through the prediction of energy demand and changes in energy consumption. The specific process is as follows:

[0100] ;

[0101] in, Monitoring indicators that represent the energy situation of smart cities; Indicates the current time Predicted value of smart city energy demand; represents the compensation coefficient; Indicates the current time Expected smart city energy demand; Indicates the energy demand forecast result of the previous time; Indicated in The energy consumption at that time.

[0102] The present invention utilizes monitoring indicators to monitor in real time the predicted situation of energy demand and the changes in actual energy consumption, helping city managers to timely adjust energy supply and energy utilization strategies to cope with emergencies or changing demands; by monitoring changes in energy situation indicators, timely evaluating the effectiveness of policies, facilitating the discovery of energy problems and hidden dangers, improving the city's emergency response capabilities, effectively handling energy supply shortages or other emergencies, and ensuring a balance between energy supply and demand.

[0103] Refer to the attached Figure 2 , this embodiment provides an energy monitoring system based on a smart city, including the following contents:

[0104] Data acquisition and processing module, building facility classification module, impact degree relationship module, energy consumption regulation module, energy consumption optimization module, energy demand prediction module, smart city energy monitoring module;

[0105] The data acquisition and processing module is responsible for collecting the energy consumption data of the smart city from various sensors, devices and data sources;

[0106] The building facility classification module classifies the building facilities and divides the city's buildings according to building scale, building purpose, facility type, and facility efficiency, so as to better understand and analyze the impact of different buildings on energy consumption;

[0107] The impact relationship module divides the building facility information of the smart city in detail, calculates the impact relationship between the building facilities under different classification standards, and provides a basis for subsequent energy regulation and optimization;

[0108] The energy consumption adjustment module calculates energy consumption adjustment parameters based on real-time data and impact relationship, combined with environmental conditions and energy equipment maintenance frequency, optimizes energy utilization methods, and improves energy utilization efficiency;

[0109] Energy consumption optimization module, which analyzes and optimizes energy consumption data according to energy consumption adjustment parameters and compares them with preset energy consumption reference adjustment benchmarks;

[0110] The energy demand forecasting module uses deep learning to establish an energy demand forecasting model based on historical data and trend analysis to predict future energy demand and help city managers make reasonable energy planning and decisions;

[0111] The smart city energy monitoring module combines the forecast of energy demand with the changes in energy consumption, sets monitoring indicators for the smart city energy situation, and monitors the smart city energy situation.

[0112] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0113] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0114] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0115] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0116] The above contents are only for explaining the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. An energy monitoring method based on smart city, characterized in that: The following steps are involved: Step S1. Deploy sensors and metering equipment at different locations in the city to obtain data information, classify urban building facilities according to building scale, building purpose, facility type, and facility efficiency, and calculate the impact relationship between building facilities under different classification standards; By dividing the building facilities of the smart city and extracting features, the association weight of the building facility information set according to the building scale is obtained: ; The association weight of the data information set divided according to the building use is ; The associated weight of the data information set divided by facility type is ; The associated weight of the data information set divided according to facility efficiency is ; The relationship between the impact levels of building facilities under different classification standards is calculated as follows: ； in, Indicates the relationship between the influence levels of smart city buildings and facilities; , , , They represent the weights of the association weights of the building facility information set according to the building scale, building purpose, facility type, and facility efficiency, respectively. ; Indicates the proportion coefficient of different influencing factors; Indicates that the building facilities are The characteristic influence of each interference factor; represents the influence coefficient; Step S2. According to the influence degree relationship between smart city building facilities, the energy consumption in the smart city is determined, and the energy consumption adjustment parameter is obtained; Define the judgment threshold as , ,and , Indicates the influence relationship between smart city building facilities; if , indicating that the impact between smart city building facilities is high, so the energy consumption is set , ; represents the first preset adjustment coefficient; if , indicating that the impact between smart city building facilities is medium, then the energy consumption is set , ; represents the first preset adjustment coefficient; if , indicating that the impact between smart city building facilities is low, so the energy consumption is set , ; represents a first preset adjustment coefficient; in, represents the judgment coefficient; Indicates bias; represents the exponential coefficient; represents the error term; t represents the total duration of the recording; It represents the difference between the expected energy consumption and the actual energy consumption monitored; represents the coefficient of variation of equipment efficiency in smart cities; Table 1. Energy price change coefficient; The coefficient of variation that indicates energy supply conditions; , , Respectively represent the corresponding weights; Calculate energy consumption adjustment parameters based on environmental conditions and energy equipment maintenance frequency , the specific process is as follows: ； in, Indicated in The total amount of energy consumption obtained at the moment; represents the regulation coefficient of energy consumption; Indicated in The energy usage fees that need to be paid are accumulated at all times; represents energy utilization rate; The function representing the impact of energy equipment maintenance frequency on energy consumption; Indicates the maintenance frequency of energy equipment; Indicates the impact of dynamic changes in environmental conditions on energy consumption; represents the correction factor; Step S3. Optimize the energy consumption according to the energy consumption adjustment parameter and the preset energy consumption reference adjustment benchmark; Define the first energy consumption reference adjustment benchmark as The second energy consumption reference adjustment benchmark is ,in, ; if , the optimized energy consumption is , ; if , the optimized energy consumption is , ; if , the optimized energy consumption is , ; Step S4. Combine the optimized energy consumption and the acquired energy information in the smart city to establish an energy demand forecasting model to predict future energy demand; Step S5. Monitor the energy situation of the smart city through the forecast of energy demand and changes in energy consumption.

2. The energy monitoring method based on smart city according to claim 1 is characterized in that: The energy demand prediction model is established to analyze the data. The specific process is as follows: ； in, represents the established energy demand forecasting model; It represents the smart city energy data set collected by different types of sensors, including electricity meter sensors, water meter sensors, and gas sensors; represents the energy loss data set recorded during the monitoring process; Indicates the energy utilization transformation of smart cities; Represents the output of the energy demand forecasting model.

3. The energy monitoring method based on smart city according to claim 2 is characterized in that: The step S5 specifically includes: ； in, Monitoring indicators that represent the energy situation of smart cities; Indicates the current time Predicted value of smart city energy demand; represents the compensation coefficient; Indicates the current time Expected smart city energy demand; Indicates the energy demand forecast result of the previous time; Indicated in The energy consumption at that time.

4. An energy monitoring system based on a smart city, applied to the energy monitoring method based on a smart city according to claim 1, characterized in that: Includes the following: Data acquisition and processing module, building facility classification module, impact degree relationship module, energy consumption regulation module, energy consumption optimization module, energy demand prediction module, smart city energy monitoring module; The data acquisition processing module is responsible for collecting energy consumption data of the smart city from sensors, devices and data sources; The building facility classification module classifies the building facilities and divides the city's buildings according to building scale, building purpose, facility type, and facility efficiency; The impact relationship module divides the building facility information of the smart city in detail, calculates the impact relationship between the building facilities under different classification standards, and provides a basis for subsequent energy regulation and optimization; The energy consumption adjustment module calculates energy consumption adjustment parameters based on real-time data and impact degree relationships, combined with environmental conditions and energy equipment maintenance frequency; The energy consumption optimization module analyzes and optimizes the energy consumption data according to the energy consumption adjustment parameters and compares them with the preset energy consumption reference adjustment benchmark; The energy demand forecasting module uses deep learning to establish an energy demand forecasting model based on historical data and trend analysis to predict future energy demand; The smart city energy monitoring module sets monitoring indicators for the smart city energy situation in combination with the predicted energy demand and the change in energy consumption, and monitors the smart city energy situation.

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