Building energy consumption optimization method and system based on photovoltaic power generation

Abstract

The invention discloses a building energy consumption optimization method and system based on photovoltaic power generation, and relates to the technical field of building energy management, and the method comprises the steps: collecting the material parameters of a building structure, building space layout information, meteorological data and photovoltaic operation data, building a thermal response model based on the material parameters of a building envelope structure and the building space layout information, and carrying out the thermal response model; obtaining thermal inertia parameters of the building structure; calculating the meteorological data and the photovoltaic operation data through a physical mechanism modeling method to obtain photovoltaic power generation power; and performing cross comparison on the photovoltaic power generation power and the thermal inertia parameter of the building structure to obtain a load target of the pre-cooling and pre-heating time periods. According to the method, the photovoltaic power generation power and the building thermal inertia parameters are subjected to cross comparison, a variable load window is recognized, on-site efficient utilization of photovoltaic power is achieved, the thermal hysteresis characteristic and the thermal comfort target are further combined, the optimal pre-cooling / pre-heating time period is determined, and a dynamic energy-saving strategy is formulated.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of building energy management, in particular to a building energy consumption optimization method and system based on photovoltaic power generation. BACKGROUND

[0002] Optimized management of building energy consumption has become an important direction to promote energy saving and emission reduction. The integration of photovoltaic power generation in buildings has gradually become popular, achieving partial self-sufficiency of building energy use. Building energy management methods have also been continuously improved, gradually transitioning from single device energy saving control to comprehensive consideration of meteorological environment, user behavior and energy collaborative optimization scheduling. Through the evaluation of building structure thermal inertia by a thermal response model and the combination of external energy supply conditions for energy management, it is considered as one of the key technical paths to improve building energy efficiency.

[0003] In the field of building energy management technology, fixed strategies or empirical models are used to regulate building thermal behavior and photovoltaic power generation capacity, lacking in-depth exploration of the response relationship between photovoltaic power generation volatility and building thermal inertia. Building energy saving regulation often fails to effectively identify the variable load window of photovoltaic power, resulting in the inability to fully utilize the power generation redundancy for active adjustment of thermal load. In particular, in typical pre-cooling and pre-heating scenarios, building thermal inertia is not quantitatively applied to match power generation utilization, restricting the deep optimization and utilization of photovoltaic resources in buildings. SUMMARY

[0004] In view of the above existing problems, the present application is proposed.

[0005] Therefore, the present application provides a building energy consumption optimization method based on photovoltaic power generation to solve the problem of lack of response matching between photovoltaic power generation volatility and building thermal inertia, inability to effectively identify the variable load window, and limitation of power generation redundancy utilization.

[0006] To solve the above technical problems, the present application provides the following technical solutions: In a first aspect, the present application provides a building energy consumption optimization method based on photovoltaic power generation, comprising, collecting material parameters of building structure, building space layout information, meteorological data and photovoltaic operation data, constructing a thermal response model, and obtaining thermal inertia parameters of building structure; calculating the photovoltaic power generation power by physical mechanism modeling method based on meteorological data and photovoltaic operation data; cross-comparing the photovoltaic power generation power and the thermal inertia parameters of the building structure to obtain the load target of the pre-cooling and pre-heating period, and identifying the variable load window of photovoltaic power generation; based on the variable load window of photovoltaic power generation, calculating the pre-cooling and pre-heating window of the building structure; formulating a building energy saving optimization adjustment strategy through the pre-cooling and pre-heating window of the building structure.

[0007] As a preferred embodiment of the building energy consumption optimization method based on photovoltaic power generation described in this invention, the method includes the following steps: collecting material parameters of the building structure, building spatial layout information, meteorological data, and photovoltaic operation data. The heat transfer coefficient, material layer thickness, specific heat capacity, and material density parameters of the building structure are obtained using an infrared thermal imager. Use BIM building models to obtain building space layout information; Temperature, humidity, and solar radiation intensity are obtained from meteorological data obtained through local weather stations; Photovoltaic operation data is obtained through the communication interface of the photovoltaic inverter.

[0008] As a preferred embodiment of the building energy consumption optimization method based on photovoltaic power generation described in this invention, the following steps are included in constructing a thermal response model based on the material parameters of the building envelope and the building spatial layout information to obtain the thermal inertia parameters of the building structure: The thermal resistance of each material layer is calculated using the thermal resistance calculation formula based on the material layer thickness and material thermal conductivity parameters. Based on the heat transfer coefficient, material layer thickness, and specific heat capacity, the heat capacity formula is used to calculate the heat capacity of each material layer. The heat capacity; A thermal response model is constructed based on thermal resistance and thermal capacity. Meteorological data, material parameters of building structure, and thermal resistance data of hot melt are input into the thermal response model to obtain the power of internal heat source. The thermal inertia parameters of the building structure are obtained based on the power of the internal heat source.

[0009] As a preferred embodiment of the building energy consumption optimization method based on photovoltaic power generation described in this invention, the method for calculating photovoltaic power generation power by using physical mechanism modeling to analyze meteorological data and photovoltaic operation data includes the following steps: Based on photovoltaic operation data and meteorological data, the temperature of photovoltaic modules is obtained using a linear regression algorithm; The photovoltaic module temperature is corrected using a temperature correction model to obtain the photovoltaic module's photoelectric conversion efficiency. Based on the photoelectric conversion efficiency of photovoltaic modules, the photovoltaic power generation is calculated using a physical mechanism modeling method.

[0010] As a preferred embodiment of the building energy consumption optimization method based on photovoltaic power generation described in this invention, the following steps are included: cross-comparing the photovoltaic power generation capacity and the thermal inertia parameters of the building structure to obtain the load targets for the pre-cooling and pre-heating time periods, and identifying the variable load window for photovoltaic power generation. Based on the building spatial layout information, the building foundation load is obtained; Based on the photovoltaic power generation power, the power generation redundancy condition is analyzed, and the difference between the photovoltaic power generation power and the building basic electrical load is calculated; Based on the thermal inertia parameter of the building structure, the maximum pre-cooling and pre-heating energy of the building is obtained; The difference between the photovoltaic power generation power and the building basic electrical load is compared with the maximum pre-cooling and pre-heating energy of the building, and the load target of the pre-cooling and pre-heating time period is obtained; The load target of the pre-cooling and pre-heating time period is calculated, and the photovoltaic power generation variable load window is obtained.

[0011] As a preferred scheme of the building energy consumption optimization method based on photovoltaic power generation, wherein: in the photovoltaic power generation variable load window, the pre-cooling and pre-heating window of the building structure comprises the following steps, Based on the photovoltaic power generation variable load window, the period when the photovoltaic power generation power is higher than the building basic load is screened to obtain the adjustment window; Based on the thermal inertia parameter of the building structure, the pre-cooling and pre-heating window of the building structure is calculated.

[0012] As a preferred scheme of the building energy consumption optimization method based on photovoltaic power generation, wherein: the building energy saving optimization adjustment strategy is formulated through the pre-cooling and pre-heating window of the building structure, comprising the following steps, Based on the pre-cooling and pre-heating window of the building structure, the target temperature of the building is obtained; According to the matching degree of the photovoltaic power generation power and the building basic load, the photovoltaic energy supply redundant period is obtained; The energy consumption of the building in different time periods is obtained by analyzing the photovoltaic energy supply redundant period and the target temperature of the building; Based on the energy consumption, the building energy saving optimization adjustment strategy is obtained.

[0013] In a second aspect, the application provides a building energy saving optimization system based on photovoltaic power generation, comprising a data acquisition model, which acquires material parameters of a building structure, building space layout information, meteorological data and photovoltaic operation data, constructs a thermal response model based on the material parameters of the building envelope structure and the building space layout information, and obtains the thermal inertia parameter of the building structure; The power generation power module calculates the photovoltaic power generation power by physical mechanism modeling method based on the meteorological data and the photovoltaic operation data; The identification module cross-compares the photovoltaic power generation power and the thermal inertia parameter of the building structure to obtain the load target of the pre-cooling and pre-heating time period, and identifies the photovoltaic power generation variable load window; The adjustment module calculates the pre-cooling and pre-heating window of the building structure based on the photovoltaic power generation variable load window; The adjustment module formulates a building energy saving optimization adjustment strategy through the pre-cooling and pre-heating window of the building structure.

[0014] In a third aspect, the present application provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and wherein the computer program, when executed by the processor, implements any step of the building energy consumption optimization method based on photovoltaic power generation according to the first aspect of the present application.

[0015] In a fourth aspect, the present application provides a computer readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements any step of the building energy consumption optimization method based on photovoltaic power generation according to the first aspect of the present application.

[0016] The present application has the following beneficial effects: by cross-comparing photovoltaic power generation and building thermal inertia parameters, a variable load window is identified, efficient use of photovoltaic power is achieved, further combined with thermal hysteresis characteristics and thermal comfort targets, an optimal pre-cooling / pre-heating period is determined, a dynamic energy-saving strategy is formulated, building energy consumption peaks are effectively reduced, energy utilization efficiency and user comfort are improved, building and photovoltaic optimization is promoted, and good energy-saving and carbon-reducing effects and practical application value are achieved. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0018] Fig. 1 Flowchart of the building energy consumption optimization method based on photovoltaic power generation.

[0019] Fig. 2 Schematic diagram of the building energy-saving optimization system based on photovoltaic power generation.

[0020] Fig. 3 Flowchart of the thermal inertia parameters of the building structure.

[0021] Fig. 4 Flowchart of the photovoltaic power generation variable load window. DETAILED DESCRIPTION

[0022] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings of the specification.

[0023] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. However, it will be apparent to one skilled in the art that the present application can be practiced without the specific details set forth in this description. In other instances, well-known methods, procedures, components, and circuits have not been described in detail so as not to unnecessarily obscure aspects of the present application.

[0024] It should also be noted that, as used in the specification and in the claims, the article "a", "an", or "the" is intended to mean that there is at least one of the particular feature unless the context clearly dictates otherwise. Thus, by way of example, "a feature of "an element of the present application" means that there are one or more of the features or one or more of the elements of the present application. For purposes of the specification and claims, the expression "coupled" or "connected to" means that the element under consideration is either directly connected to or indirectly connected to the other element through one or more intervening elements. The term "coupled" or "connected" does not necessarily mean that the elements are directly connected together.

[0025] Reference will now be made to Figs. 1-4 For one embodiment of the present application, the embodiment provides a building energy consumption optimization method based on photovoltaic power generation, comprising the following steps: S1, collecting material parameters of building structure, building space layout information, meteorological data and photovoltaic operation data.

[0026] S1.1, obtaining the heat transfer coefficient, material layer thickness, specific heat capacity and material density parameters of the material parameters of the building structure through the infrared thermal imager.

[0027] S1.2, obtaining the building space layout information by using the BIM building model.

[0028] Furthermore, the structural components, functional partitions, floor distribution, open bay depth size and layer height parameters of the building are digitally modeled and data exported by using the BIM building model, the use function, volume, mutual relationship and heat exchange path of each room are determined, and a complete building space heat distribution structure is formed.

[0029] S1.3, obtaining the temperature, humidity and solar radiation intensity in the meteorological data through the local meteorological station.

[0030] Furthermore, the temperature, relative humidity and solar radiation intensity per unit area in the external environment of the building are respectively monitored and recorded in real time by using high-precision temperature and humidity sensors and radiation intensity sensors.

[0031] S1.4, obtaining the photovoltaic operation data through the communication interface of the photovoltaic inverter.

[0032] Furthermore, the operation data of the photovoltaic system, including the photovoltaic power generation power, voltage and current parameters at each time node, are read in real time based on the Modbus Ethernet and other standard communication protocols through the communication interface of the photovoltaic inverter.

[0033] S2, constructing a thermal response model based on the material parameters of the building envelope structure and the building space layout information to obtain the thermal inertia parameters of the building structure.

[0034] S2.1, calculate the thermal resistance of each layer of material based on the material layer thickness and material thermal conductivity coefficient parameters using the thermal resistance calculation formula, specifically, the expression is, ; wherein, is the thermal resistance of the first layer of building material, is the thickness of the first layer of building material, is the heat transfer coefficient of the first layer of building material.

[0035] Further, according to the thermal detection of the building envelope by the external thermal imager, the heat transfer coefficient, material layer thickness, specific heat capacity and material density physical parameters are obtained, the thermal resistance of each layer of material is calculated by using the thermal resistance calculation formula, and the thermal resistance of the building material is obtained.

[0036] S2.2, based on the heat transfer coefficient, material layer thickness and specific heat capacity, calculate the heat capacity of each layer of material using the heat capacity formula, specifically, the expression is, ; wherein, is the heat capacity of the first layer of building material, is the density of the first layer of building material, is the specific heat capacity of the first layer of building material.

[0037] S2.3, based on the thermal resistance and heat capacity, a thermal response model is constructed by RC network modeling method, meteorological data, material parameters of building structure and thermal melting thermal resistance data are input into the thermal response model, internal heat source power is obtained, specifically, the expression is, ; wherein, is the internal heat source power at time , is the internal temperature at time , is the external temperature at time , is the heat transfer coefficient at time , is the heat transfer coefficient of the internal temperature at time , is the heat capacity of the building material.

[0038] Furthermore, based on the thermal resistance and thermal capacity parameters of each building material, thermal resistance is treated as resistance and thermal capacity as capacitance. A multi-node thermal equivalent circuit network corresponding to the building envelope is established according to the heat transfer direction. RC structural units are connected in series according to the heat transfer direction for each layer of the structure. The time-series temperature information from meteorological data is combined with RC network modeling methods to construct a thermal response model. Then, meteorological data, building material parameters, and thermal resistance data are input into the thermal response model for calculation to obtain the internal heat source power.

[0039] S2.4. Based on the internal heat source power, the thermal inertia parameters of the building structure are obtained using the thermal response differential solution method.

[0040] Furthermore, the internal heat source power is analyzed and quantified. This includes determining the internal heat source power based on specific energy consumption data of electrical appliance use, lighting, and personnel activities. Specifically, real-time power data of electrical equipment is collected through smart meters, combined with lighting and HVAC operating parameters, and the number of people indoors is counted using personnel sensors. Human heat dissipation is calculated based on a standard of 65W per person. Equipment power consumption, lighting load (considering a 90% photothermal conversion rate for the example), and personnel heat generation are superimposed over time, and the total heat source power is synthesized using a heat-power conversion formula. Moving average filtering is used to eliminate short-term fluctuations in power data caused by sensor noise, transient equipment start-up and shutdown, and randomness of personnel movement, outputting the internal heat source power with minute-level accuracy. The internal heat source power is combined with the collected temperature, humidity, and solar radiation intensity data, and processed using interpolation fitting methods to obtain the temperature distribution of the building structure at different times and locations, thus obtaining the thermal inertia parameters of the building structure.

[0041] S3. The photovoltaic power generation is obtained by calculating the meteorological data and photovoltaic operation data through the physical mechanism modeling method.

[0042] S3.1 Based on photovoltaic operation data and meteorological data, the temperature of photovoltaic modules is obtained using a linear regression algorithm.

[0043] Further, the timestamps of the photovoltaic (1 minute) and meteorological (5 minutes) data are aligned by time interpolation, and then the data is cleaned by removing the night zero-power segment, filtering abnormal irradiance (such as <50 or >1200 W / m²), and filling missing values with a sliding average; then the irradiance, temperature and other features are normalized, and moving average and other time series features are constructed; finally, the photovoltaic current, voltage and meteorological parameters are fused according to the accurate timestamp to form a standardized data set, the normalized ambient temperature and solar irradiance are taken as input features, the measured temperature of the backboard of the photovoltaic module is taken as the target variable, a linear regression equation is established, the regression coefficients are estimated by the least squares method, the linear regression model parameters are optimized by 10-fold cross-validation, and the determination coefficient and root mean square error RMSE are calculated to evaluate the precision of the linear regression model; and finally the photovoltaic module temperature prediction value is output.

[0044] S3.2, the temperature of the photovoltaic module is corrected by the temperature correction model to obtain the photoelectric conversion efficiency of the photovoltaic module.

[0045] Further, the operating data of the photovoltaic module under different environmental temperatures and solar radiation intensities are collected, including the actual operating temperature of the photovoltaic module, the environmental temperature, the solar radiation intensity and the output power, the historical operating data is divided into a training set and a validation set in a ratio of 7:3, and it is ensured that the training set and the validation set cover different seasons and weather conditions; a random forest regression algorithm is used, the environmental temperature, the solar irradiance and the operating temperature of the module are taken as input features, and the ratio of the actual output power to the theoretical power under standard test conditions is taken as the target variable for modeling; the best super parameter combination is determined by grid search optimization, including setting 200 decision trees and 15 layers of maximum depth; a nonlinear relationship model between temperature and power characteristics is established on the training set; finally, the performance of the nonlinear relationship model is evaluated using the validation set, requiring a determination coefficient of 0.92 or more and an average absolute error of no more than 2.5%, and finally a temperature correction model that can accurately reflect the influence of temperature on efficiency is obtained, the actual operating temperature of the photovoltaic module, the environmental temperature, the solar radiation intensity and the output power are converted into the photoelectric conversion efficiency of the photovoltaic module by linking the photovoltaic operating data and the meteorological data to obtain the current actual operating temperature and the standard reference temperature of the photovoltaic module.

[0046] S3.3, based on the photoelectric conversion efficiency of the photovoltaic module, the photovoltaic power is calculated by using the physical mechanism modeling method, specifically, ; wherein, is the photovoltaic power at time , is the solar radiation intensity at time , is the light receiving area of the photovoltaic module, For the performance ratio of power generation, For the photoelectric conversion efficiency.

[0047] S4, cross comparison of photovoltaic power generation and thermal inertia parameters of building structure, get the load target of pre-cooling and pre-heating period, identify the photovoltaic power generation variable load window.

[0048] S4.1, based on building space layout information, using area load density method to get building basic load.

[0049] Further, from the BIM building model, the space layout information of each room or functional area is extracted, the use function type of each area is determined, and the building area is obtained. According to different functional areas, set the basic load index per square meter, multiply the basic load index by the building area of the corresponding area to get the basic load value of each area, and finally summarize the basic load value of the functional area to get the building basic load.

[0050] S4.2, based on photovoltaic power generation power, using load matching algorithm to analyze the condition of power generation redundancy, calculating the difference between photovoltaic power generation power and building basic electric load, specifically, the expression is, ; Among them, is the difference between photovoltaic power generation power and building basic load at time , and is the building basic load at time .

[0051] Further, by obtaining the photovoltaic power generation power, the difference between the photovoltaic power generation power and the basic electric load is calculated. When the difference between the photovoltaic power generation power and the basic electric load is greater than zero, it is positive. When the difference between the photovoltaic power generation power and the basic electric load is positive, it means that there is redundant power for energy storage. When the difference between the photovoltaic power generation power and the basic electric load is less than zero, it is negative. When the difference between the photovoltaic power generation power and the basic electric load is negative, it means that the building basic load exceeds the photovoltaic supply capacity, and the external power grid needs to be supplemented.

[0052] S4.3, based on the thermal inertia parameters of building structure, using thermal energy balance method to get the maximum pre-cooling and pre-heating energy of building.

[0053] Further, according to the heat capacity parameter, the energy storage capacity corresponding to the unit temperature change of the building envelope is determined, the target temperature interval of pre-cooling is set to be from 30℃ to 25℃, and combined with the current internal temperature and external temperature change trend, the heat that the envelope can store or release in the temperature interval, through the heat balance formula, the changes of heat input, heat output and internal heat source are integrated, the maximum pre-cooling energy and maximum pre-heating energy per unit time are obtained, the expression is, ; wherein, is the maximum pre-cooling energy, is the heat input of the external envelope, is the internal heat source, is the heat output of the external envelope; ; wherein, is the maximum pre-heating energy; S4.4, cross-comparing the difference between the photovoltaic power and the building's basic electrical load with the building's maximum pre-cooling and pre-heating energy, to obtain the load target of the pre-cooling and pre-heating period.

[0054] Further, the total building power and photovoltaic power original data per minute are collected from the smart meter and photovoltaic inverter, and the abnormal values and null values are removed through data cleaning; then, based on the same timestamp, the photovoltaic power is subtracted from the building's basic electrical load (the fixed load such as lighting, socket and equipment obtained through the BMS system) to obtain the net power difference per minute; then, the difference value data is subjected to moving average filtering processing (5-minute window is adopted) to eliminate instantaneous fluctuations; finally, the continuous minute-level difference value data after processing is stored using a time series database to form a standard time series curve containing four fields of timestamp, photovoltaic power, basic load and net difference value, and the photovoltaic surplus period in which the power difference is greater than zero is identified; then, the available residual power of each photovoltaic surplus period is matched with the building's thermal demand, and when the residual power exceeds the pre-cooling demand threshold, the pre-cooling mode is started, and when the residual power exceeds the pre-heating demand threshold, the pre-heating mode is started; at the same time, the building's thermal inertia time constant is considered to ensure that the operation period matches the temperature response delay; finally, through an iterative optimization algorithm, the optimal pre-cooling / pre-heating load target value of each period is determined under the condition of satisfying the indoor comfort constraint, to obtain the load target of the pre-cooling and pre-heating period; It should also be noted that the pre-cooling demand threshold is set: based on the calculation of the building envelope thermal capacity characteristics, the total heat removed by the building in the target temperature interval (such as 28℃ to 26℃) is first determined, and then divided by the pre-cooling operation period length (such as 2 hours) to obtain the minimum pre-cooling power demand per unit time. At the same time, considering that 70% of the rated power is the upper limit, the smaller of the two is finally taken as the pre-cooling demand threshold; The pre-heating demand threshold is set: by analyzing the building heat loss coefficient, the minimum heating power required to maintain the target temperature (such as 16℃ to 20℃) is calculated. Combined with the historical heating data, 60% of the building's heat load under the typical winter working condition is taken as the reference value, and a redundancy of 5%-10% is added to cope with temperature fluctuations, and finally the pre-heating demand threshold is determined.

[0055] S4.5, the load target of the pre-cooling and pre-heating time period is calculated by a load-power generation difference method to obtain a photovoltaic power generation variable load window, and the expression is, ; wherein, is the photovoltaic power generation variable load window, is the maximum pre-cooling and maximum pre-heating energy at a time , is the pre-cooling energy, is the pre-heating energy, is the load target.

[0056] S5, based on the photovoltaic power generation variable load window, the pre-cooling and pre-heating window of the building structure is calculated.

[0057] S5.1, based on the photovoltaic power generation variable load window, the adjustment window is obtained by screening the time period in which the photovoltaic power generation power is higher than the building basic load.

[0058] Further, according to the photovoltaic power generation power and the building basic load value, the power difference between the photovoltaic power generation power and the basic electric load is obtained, and all time periods satisfying the difference between the photovoltaic power generation power and the basic electric load greater than zero are identified, and the continuous positive difference time period set is defined as the photovoltaic redundant adjustable window.

[0059] S5.2, the pre-cooling and pre-heating window of the building structure is calculated by a thermal hysteresis characteristic analysis method, and the expression is, ; wherein, is the pre-cooling and pre-heating window of the building structure, is the thermal load at a time , is the photovoltaic redundant power, is the pre-cooling window of the building structure, is the pre-heating window of the building structure.

[0060] S6, the building energy-saving optimization adjustment strategy is formulated through the pre-cooling and pre-heating window of the building structure.

[0061] S6.1, based on the pre-cooling and pre-heating window of the building structure, the target temperature of the building is obtained by a thermal comfort zone inversion calculation method.

[0062] Further, the pre-cooling / pre-heating operation time window is determined according to the duration of the photovoltaic power exceeding the basic electrical load, the maximum heat energy that can be stored / released in the duration is calculated in combination with the heat capacity parameter of the building envelope (such as the wall thermal inertia index), then based on the ASHRAE 55 thermal comfort standard, the temperature adjustment range that meets the comfort requirement is calculated by inversely solving the heat balance equation after considering factors such as personnel activity mode, clothing thermal resistance and indoor radiant temperature, with the constraint condition of the predicted mean vote index PMV±0.5, and finally the target temperature of the building that can be achieved in the range is selected according to the matching degree of the building energy storage capacity and the photovoltaic residual power (such as the pre-cooling target 26±0.5℃ in summer and the pre-heating target 20±0.5℃ in winter).

[0063] S6.2, the photovoltaic power supply energy redundancy period is obtained according to the matching degree of the photovoltaic power and the building basic load.

[0064] Further, the matching degree between the photovoltaic power and the building basic load is obtained by comparing the difference between the photovoltaic power and the building basic load in the same time period, the period when the photovoltaic power exceeds the building basic load is analyzed, and the redundant power of the photovoltaic power generation period is identified, when the photovoltaic power is greater than the building basic load, the part of the excess power is regarded as redundant energy supply, indicating that there is extra power available in the photovoltaic power supply energy redundancy period.

[0065] S6.3, the photovoltaic power supply energy redundancy period and the target temperature of the building are analyzed using the time series energy consumption balance analysis method, and the energy consumption of the building in different time periods is obtained.

[0066] Further, the photovoltaic inverter output power and the building sub-metering meter data are obtained in real time, the net difference between the power generation and the basic load is calculated at an interval of 1 minute, the sliding time window algorithm (window length 30 minutes) is used to identify the continuous positive difference period, and the photovoltaic power supply energy redundancy period is marked. Based on the building thermal zoning parameters derived from the BIM model, in combination with the target temperature curve and the measured indoor and outdoor temperature and humidity, a dynamic heat balance equation is established: in the EnergyPlus simulation platform, a 15-minute time step is set, the thermal property parameters of the building envelope, the HVAC system COP curve and the personnel density schedule are input, the conduction, convection and radiation heat transfer equations are solved simultaneously, the actual energy consumption of the wall heat storage, air conditioning refrigeration and lighting equipment in each period is quantified, and the actual energy consumption is obtained.

[0067] S6.4, based on the energy consumption, the rolling optimization algorithm is used to obtain the building energy saving optimization adjustment strategy.

[0068] Further, based on the energy consumption data of the lighting device, the system state is updated every 15 minutes based on the latest sensor data through a rolling time domain optimization framework, and the optimal control sequence for the next 4 hours is solved; the load flexibility adjustment coefficient is introduced to quantify the interruptible characteristics of the device, and the robust optimization is used to handle the uncertainty of photovoltaic power generation, and by dynamically adjusting the HVAC operation curve and the time zone lighting strategy, the daily energy consumption is reduced by 12%-18% while ensuring that the indoor PMV index fluctuation does not exceed ±0.5, and the building energy saving optimization adjustment strategy is realized.

[0069] The embodiment also provides a building energy saving optimization system based on photovoltaic power generation, comprising: a data acquisition model, which acquires material parameters of a building structure, building space layout information, meteorological data and photovoltaic operation data, constructs a thermal response model based on the material parameters of the building envelope and the building space layout information, and obtains thermal inertia parameters of the building structure; A power generation power module calculates the meteorological data and the photovoltaic operation data by a physical mechanism modeling method to obtain photovoltaic power generation power; An identification module cross-compares the photovoltaic power generation power and the thermal inertia parameters of the building structure to obtain load targets of pre-cooling and pre-heating time periods and identifies a photovoltaic power generation variable load window; An adjustment module calculates pre-cooling and pre-heating windows of the building structure based on the photovoltaic power generation variable load window; An adjustment module formulates a building energy saving optimization adjustment strategy through the pre-cooling and pre-heating windows of the building structure.

[0070] The embodiment also provides a computer device suitable for the building energy consumption optimization method based on photovoltaic power generation, comprising: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions to realize the building energy consumption optimization method based on photovoltaic power generation proposed in the above embodiment.

[0071] The computer device can be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected by a system bus. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for running the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is configured to perform wired or wireless communication with an external terminal. The wireless communication can be achieved by WIFI, an operator network, NFC (Near Field Communication) or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer overlaid on the display screen, or a key, trackball or touchpad arranged on the shell of the computer device, or an external keyboard, touchpad or mouse, etc.

[0072] The embodiment also provides a storage medium having a computer program stored thereon, the program being executed by a processor to implement the building energy consumption optimization method based on photovoltaic power generation.

[0073] In summary, the present application realizes efficient utilization of photovoltaic power on site by cross-comparing photovoltaic power generation power and building thermal inertia parameters to identify a variable load window, further determines an optimal pre-cooling / pre-heating period in combination with thermal hysteresis characteristics and thermal comfort targets, formulates a dynamic energy-saving strategy, effectively reduces building energy consumption peak, improves energy utilization efficiency and user comfort, promotes collaborative optimization of buildings and photovoltaic power, and has good energy-saving and carbon-reducing effects and practical application value. It should be noted that the above examples are only used to illustrate the technical solutions of the present application but not limit the present application. Although the present application is described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalently replaced, without departing from the spirit and scope of the technical solutions of the present application, which should be covered in the scope of the claims of the present application.

Claims

1. A method for building energy consumption optimization based on photovoltaic power generation, characterized in that: comprising, Collecting material parameters of building structure, building space layout information, meteorological data and photovoltaic operation data, constructing a thermal response model to obtain thermal inertia parameters of building structure; Through physical mechanism modeling method, the meteorological data and the photovoltaic operation data are calculated to obtain the photovoltaic power generation power; Cross comparison of the photovoltaic power generation power and the thermal inertia parameters of the building structure is performed to obtain the load target of the pre-cooling and pre-heating time period, and the photovoltaic power generation variable load window is identified; Based on the photovoltaic power generation variable load window, the pre-cooling and pre-heating window of the building structure is calculated; Through the pre-cooling and pre-heating window of the building structure, the building energy-saving optimization adjustment strategy is formulated. 2.The building energy consumption optimization method based on photovoltaic power generation of claim 1, wherein: Collecting material parameters of building structure, building space layout information, meteorological data and photovoltaic operation data, comprising the following steps, The heat transfer coefficient, material layer thickness, specific heat capacity and material density parameters of the material parameters of the building structure are obtained by an infrared thermal imager; The building space layout information is obtained by using a BIM building model; The temperature, humidity and solar radiation intensity in the meteorological data are obtained by a local meteorological station; The photovoltaic operation data is obtained through the communication interface of the photovoltaic inverter. 3.The building energy consumption optimization method based on photovoltaic power generation of claim 2, wherein: Based on the material parameters of the building envelope and the building space layout information, a thermal response model is constructed to obtain the thermal inertia parameters of the building structure Comprising the following steps, Based on the material layer thickness and the material thermal conductivity coefficient parameters, the thermal resistance of each layer of material is calculated using the thermal resistance calculation formula; Based on the heat transfer coefficient, material layer thickness and specific heat capacity, the heat capacity of each layer of material is calculated using the heat capacity formula; Based on the thermal resistance and the heat capacity, a thermal response model is constructed, the meteorological data, the material parameters of the building structure, the thermal melting thermal resistance data are input into the thermal response model, and the internal heat source power is obtained; Based on the internal heat source power, the thermal inertia parameters of the building structure are obtained. Through physical mechanism modeling method, the meteorological data and the photovoltaic operation data are calculated to obtain the photovoltaic power generation power comprising the following steps, 4.The building energy consumption optimization method based on photovoltaic power generation of claim 3, wherein: Based on the operation data of the photovoltaic and the meteorological data, the temperature of the photovoltaic component is obtained using a linear regression algorithm; The temperature of the photovoltaic component is corrected by a temperature correction model to obtain the photoelectric conversion efficiency of the photovoltaic component; Based on the photoelectric conversion efficiency of the photovoltaic component, the photovoltaic power generation power is calculated by using the physical mechanism modeling method. Cross comparison of the photovoltaic power generation power and the thermal inertia parameters of the building structure is performed to obtain the load target of the pre-cooling and pre-heating time period, and the photovoltaic power generation variable load window is identified comprising the following steps, 5.The building energy consumption optimization method based on photovoltaic power generation of claim 4, wherein: Based on the building space layout information, the building basic load is obtained; Based on the photovoltaic power generation power, the condition of power generation redundancy is analyzed, and the difference between the photovoltaic power generation power and the building basic electrical load is calculated; Based on the thermal inertia parameters of the building structure, the maximum pre-cooling and pre-heating energy of the building is obtained; The difference between the photovoltaic power generation power and the building basic electrical load is cross compared with the maximum pre-cooling and pre-heating energy of the building to obtain the load target of the pre-cooling and pre-heating time period; The photovoltaic power generation variable load window is obtained by calculating the load target of the pre-cooling and pre-heating time period. Based on the photovoltaic power generation variable load window, the pre-cooling and pre-heating window of the building structure is calculated comprising the following steps, 6.The building energy consumption optimization method based on photovoltaic power generation of claim 5, wherein: Based on the photovoltaic power generation variable load window, the adjustment window is selected by filtering out the time period when the photovoltaic power generation power is higher than the building basic load; ​ Based on the thermal inertia parameters of the building structure, the pre-cooling and pre-heating windows of the building structure are calculated. 7.The building energy consumption optimization method based on photovoltaic power generation of claim 6, wherein: Based on the pre-cooling and pre-heating windows of the building structure, the building energy-saving optimization adjustment strategy is formulated. Based on the pre-cooling and pre-heating windows of the building structure, the target temperature of the building is obtained. According to the matching degree of photovoltaic power generation power and building basic load, the photovoltaic energy supply redundant period is obtained. According to the photovoltaic energy supply redundant period and the target temperature of the building, the energy consumption of the building in different time periods is obtained. Based on the energy consumption, the building energy-saving optimization adjustment strategy is obtained.

8. The building energy saving optimization system based on photovoltaic power generation according to any one of claims 1 to 7, characterized in that: It comprises, The data acquisition model acquires the material parameters of the building structure, the building space layout information, the meteorological data and the photovoltaic operation data, constructs a thermal response model based on the material parameters of the building envelope and the building space layout information, and obtains the thermal inertia parameters of the building structure. The power generation power module calculates the photovoltaic power generation power by physical mechanism modeling method based on the meteorological data and the photovoltaic operation data. The identification module cross-compares the photovoltaic power generation power and the thermal inertia parameters of the building structure to obtain the load target of the pre-cooling and pre-heating time period, and identifies the photovoltaic power generation variable load window. The adjustment module calculates the pre-cooling and pre-heating windows of the building structure based on the photovoltaic power generation variable load window. The adjustment module calculates the pre-cooling and pre-heating windows of the building structure based on the photovoltaic power generation variable load window. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is characterized in that: The processor executes the computer program to realize the steps of the building energy consumption optimization method based on photovoltaic power generation according to any one of claims 1-7.

10. A computer readable storage medium having stored thereon a computer program, characterized in that: The computer program is executed by the processor to realize the steps of the building energy consumption optimization method based on photovoltaic power generation according to any one of claims 1-7.

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