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

Abstract

This invention discloses a method and system for optimizing building energy consumption based on photovoltaic (PV) power generation, relating to the field of building energy management technology. The method includes: collecting material parameters of the building structure, building spatial layout information, meteorological data, and PV operation data; constructing a thermal response model based on the material parameters and spatial layout information of the building envelope to obtain the thermal inertia parameters of the building structure; calculating the PV power generation capacity using physical mechanism modeling methods on the meteorological data and PV operation data; and cross-comparing the PV power generation capacity and the thermal inertia parameters of the building structure to obtain the load targets for pre-cooling and pre-heating periods. This invention identifies variable load windows by cross-comparing PV power generation capacity with building thermal inertia parameters, achieving efficient on-site utilization of PV power. Furthermore, by combining thermal hysteresis characteristics with thermal comfort targets, it determines the optimal pre-cooling / pre-heating periods and formulates dynamic energy-saving strategies.

Description

Technical Field

[0001] This invention relates to the field of building energy management technology, and in particular to a method and system for optimizing building energy consumption based on photovoltaic power generation. Background Technology

[0002] Optimized management of building energy consumption has become an important direction for promoting energy conservation and emission reduction. The integrated application of photovoltaic power generation in buildings is gradually becoming more widespread, achieving partial self-sufficiency in building energy consumption. Building energy consumption management methods are also constantly improving, gradually transitioning from single equipment energy-saving control to comprehensive consideration of meteorological environment, user behavior and coordinated optimization scheduling of energy. Evaluating the thermal inertia of building structure through thermal response model and combining it with external energy supply conditions for energy management is regarded 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. However, there is a lack of 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 power generation redundancy for active adjustment of heat load. In particular, in typical pre-cooling and pre-heating scenarios, building thermal inertia is not quantified and applied to power generation utilization matching, which restricts the in-depth optimization and utilization of photovoltaic resources in buildings. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a building energy consumption optimization method based on photovoltaic power generation to solve the problems of lack of response matching between the volatility of photovoltaic power generation and the thermal inertia of buildings, and the inability to effectively identify variable load windows, thereby limiting the utilization of power generation redundancy.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a method for optimizing building energy consumption based on photovoltaic power generation, comprising, Collect material parameters of the building structure, information on the building's spatial layout, meteorological data, and photovoltaic operation data; construct a thermal response model to obtain the thermal inertia parameters of the building structure. The photovoltaic power generation capacity is obtained by calculating the meteorological data and photovoltaic operation data using physical mechanism modeling method; By cross-comparing the photovoltaic power generation capacity and the thermal inertia parameters of the building structure, the load target for the pre-cooling and pre-heating periods is obtained, and the variable load window of photovoltaic power generation is identified. Based on the variable load window of photovoltaic power generation, calculate the precooling and preheating windows of the building structure; Develop energy-saving optimization strategies for buildings by utilizing the pre-cooling and pre-heating windows 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 photoelectric conversion efficiency of the photovoltaic module is obtained by correcting the temperature of the photovoltaic module using a temperature correction model. 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 photovoltaic power generation, the redundancy of power generation is analyzed, and the difference between photovoltaic power generation and building foundation electrical load is calculated. Based on the thermal inertia parameters of the building structure, the maximum precooling and preheating energy of the building is obtained; The difference between photovoltaic power generation and building foundation electrical load is cross-compared with the building’s maximum pre-cooling and pre-heating energy to obtain the load target for the pre-cooling and pre-heating time periods. The load target for the precooling and preheating periods is calculated to obtain the variable load window for photovoltaic power generation.

[0011] As a preferred embodiment of the building energy consumption optimization method based on photovoltaic power generation described in this invention, the calculation of the precooling and preheating windows of the building structure within the variable load window of photovoltaic power generation includes the following steps. Based on the variable load window of photovoltaic power generation, the adjustment window is obtained by screening out the time period when the photovoltaic power generation is higher than the building foundation load. Based on the thermal inertia parameters of the building structure, the precooling and preheating windows of the building structure are calculated.

[0012] As a preferred embodiment of the building energy consumption optimization method based on photovoltaic power generation described in this invention, the method for formulating building energy-saving optimization and adjustment strategies by utilizing the pre-cooling and preheating windows of the building structure includes the following steps. The target temperature of the building is obtained based on the precooling and preheating windows of the building structure. The redundancy period of photovoltaic power supply is obtained based on the matching degree between photovoltaic power generation and building foundation load; The building's energy consumption during different time periods was obtained by analyzing the redundant periods of photovoltaic power supply and the target temperature of the building. Based on energy consumption, strategies for optimizing and adjusting building energy conservation are derived.

[0013] Secondly, the present invention provides a building energy-saving optimization system based on photovoltaic power generation, including a data acquisition model that collects material parameters of building structure, building spatial layout information, meteorological data and photovoltaic operation data, and constructs a thermal response model based on the material parameters of building envelope and building spatial layout information to obtain the thermal inertia parameters of building structure. The power generation module calculates the photovoltaic power generation capacity by using physical mechanism modeling methods to analyze meteorological data and photovoltaic operation data. The identification module cross-compares the photovoltaic power generation capacity and the thermal inertia parameters of the building structure to obtain the load target for the pre-cooling and pre-heating periods and identify the variable load window of photovoltaic power generation. The adjustment module calculates the precooling and preheating windows of the building structure based on the variable load window of photovoltaic power generation. The adjustment module, through the pre-cooling and preheating windows of the building structure, formulates building energy-saving optimization adjustment strategies.

[0014] Thirdly, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, wherein when the computer program is executed by the processor, it implements any step of the building energy consumption optimization method based on photovoltaic power generation as described in the first aspect of the present invention.

[0015] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the building energy consumption optimization method based on photovoltaic power generation as described in the first aspect of the present invention.

[0016] The beneficial effects of this invention are as follows: by cross-comparing photovoltaic power generation with building thermal inertia parameters, variable load windows are identified, realizing the efficient on-site utilization of photovoltaic power. Furthermore, by combining thermal hysteresis characteristics and thermal comfort targets, the optimal pre-cooling / pre-heating period is determined, dynamic energy-saving strategies are formulated, effectively reducing building energy consumption peaks, improving energy utilization efficiency and user comfort, promoting the synergistic optimization of buildings and photovoltaics, and having good energy-saving and carbon-reduction effects and practical application value. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart of a building energy consumption optimization method based on photovoltaic power generation.

[0019] Figure 2 This is a schematic diagram of a building energy-saving optimization system based on photovoltaic power generation.

[0020] Figure 3 This is a flowchart of the thermal inertia parameters of a building structure.

[0021] Figure 4 This is a flowchart of the variable load window for photovoltaic power generation. Detailed Implementation

[0022] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0023] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0024] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0025] Reference Figures 1-4 This is one embodiment of the present invention, which provides a building energy consumption optimization method based on photovoltaic power generation, including the following steps: S1. Collect material parameters of building structure, building spatial layout information, meteorological data and photovoltaic operation data.

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

[0027] S1.2. Obtain building space layout information using BIM building models.

[0028] Furthermore, by using BIM building models, the structural components, functional zoning, floor distribution, bay width and depth dimensions, and floor height parameters of the building are digitally modeled and data is exported, clarifying the function, volume, interrelationship, and heat exchange path of each room, thus forming a complete thermal distribution structure of the building space.

[0029] S1.3 Obtain temperature, humidity and solar radiation intensity from meteorological data through local weather stations.

[0030] Furthermore, high-precision temperature and humidity sensors and radiation intensity sensors are used to monitor and record in real time the temperature, relative humidity, and solar radiation intensity received per unit area in the building's external environment.

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

[0032] Furthermore, through the communication interface of the photovoltaic inverter, based on standard communication protocols such as Modbus Ethernet, the operating data of the photovoltaic system can be read in real time, including the photovoltaic power generation, voltage, and current parameters at each time point.

[0033] S2. Based on the material parameters of the building envelope and the building spatial layout information, a thermal response model is constructed to obtain the thermal inertia parameters of the building structure.

[0034] S2.1. Based on the material layer thickness and thermal conductivity parameters, use the thermal resistance calculation formula to calculate the thermal resistance of each material layer. Specifically, the expression is: ; in, For the first Thermal resistance of building materials For the first The thickness of the building materials For the first The heat transfer coefficient of multi-story building materials.

[0035] Furthermore, by using an external thermal imager to conduct thermal tests on the building's external envelope, physical parameters such as heat transfer coefficient, material layer thickness, specific heat capacity, and material density are obtained. The thermal resistance of each layer of material is then calculated using thermal resistance calculation formulas to obtain the thermal resistance of the building materials.

[0036] S2.2. Based on the heat transfer coefficient, material layer thickness, and specific heat capacity, use the heat capacity formula to calculate the heat capacity per unit area. The heat capacity of the layer material, specifically, is expressed as follows: ; in, For the first The heat capacity of multi-story building materials For the first Density of building materials For the first Specific heat capacity of multi-story building materials.

[0037] S2.3. Based on thermal resistance and heat capacity, a thermal response model is constructed using the RC network modeling method. Meteorological data, building structure material parameters, and thermal resistance data are input into the thermal response model to obtain the internal heat source power. Specifically, the expression is: ; in, For time The internal heat source power below, For time The internal temperature below, For time The external temperature below, For time The heat transfer coefficient at the bottom, For time The heat transfer coefficient at the internal temperature below, The heat capacity of building materials.

[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 physical mechanism modeling.

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

[0043] Furthermore, the timestamps of photovoltaic (1-minute) and meteorological (5-minute) data are aligned through time interpolation. The data is then cleaned—zero-power periods at night are removed, abnormal irradiance (e.g., <50 or >1200 W / m²) is filtered out, and missing values ​​are filled with moving averages. Next, features such as irradiance and temperature are normalized, and time-series features such as moving averages are constructed. Finally, photovoltaic current, voltage, and meteorological parameters are fused according to precise timestamps to form a standardized dataset. The standardized ambient temperature and solar irradiance are used as input features, and the measured temperature of the module backsheet is used as the target variable. A linear regression equation is established, and the regression coefficients are estimated using the least squares method. The linear regression model parameters are optimized through 10-fold cross-validation, and the coefficient of determination and root mean square error (RMSE) are calculated to evaluate the accuracy of the linear regression model. Finally, the predicted temperature value of the photovoltaic module is output.

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

[0045] Furthermore, operational data of photovoltaic (PV) modules under different ambient temperatures and solar radiation intensities were collected, including the actual operating temperature of the PV modules, ambient temperature, solar radiation intensity, and output power. Historical operational data were divided into training and validation sets in a 7:3 ratio to ensure coverage of different seasons and weather conditions. A random forest regression algorithm was used, with ambient temperature, solar irradiance, and module operating temperature as input features, and the ratio of actual output power to theoretical power under standard test conditions as the target variable for modeling. The optimal hyperparameter combination was determined through grid search optimization, including setting 200 decision trees and a maximum depth of 15 layers. A nonlinear relationship model of temperature and power characteristics was established on the training set. Finally, the performance of the nonlinear relationship model was evaluated using the validation set, requiring a determination coefficient of at least 0.92 and a mean absolute error of no more than 2.5%. Ultimately, a temperature correction model that accurately reflects the influence of temperature on efficiency was obtained. By linking PV operational data with meteorological data, the current actual operating temperature and standard reference temperature of the PV modules were obtained, and the actual operating temperature of the PV modules, ambient temperature, solar radiation intensity, and output power were converted into the photoelectric conversion efficiency of the PV modules.

[0046] S3.3. Based on the photoelectric conversion efficiency of photovoltaic modules, the photovoltaic power generation is calculated using a physical mechanism modeling method. Specifically, the expression is: ; in, For time The photovoltaic power generation capacity below For time The intensity of solar radiation under the sun, This refers to the light-receiving area of ​​a photovoltaic module. The ratio of power generation to output. This refers to the photoelectric conversion efficiency.

[0047] S4. Cross-compare the photovoltaic power generation capacity and the thermal inertia parameters of the building structure to obtain the load target for the pre-cooling and pre-heating periods, and identify the variable load window for photovoltaic power generation.

[0048] S4.1. Based on the building spatial layout information, the building foundation load is obtained using the area load density method.

[0049] Furthermore, spatial layout information of each room or functional area is extracted from the BIM building model to determine the functional type of each area and obtain the building area. The basic load index per square meter is set according to different functional areas. The basic load index is multiplied by the building area of ​​the corresponding area to obtain the basic load value of each area. Finally, the basic load values ​​of the functional areas are summarized to obtain the building basic load.

[0050] S4.2. Based on photovoltaic power generation, the load matching algorithm is used to analyze the power generation redundancy and calculate the difference between photovoltaic power generation and building foundation electrical load. Specifically, the expression is as follows: ; in, For time The difference between the photovoltaic power generation and the building foundation load. For time The building foundation load.

[0051] Furthermore, by obtaining the photovoltaic power generation, the difference between the photovoltaic power generation and the base load is calculated. When the difference between the photovoltaic power generation and the base load is greater than zero, it is a positive value. When the difference between the photovoltaic power generation and the base load is positive, it indicates that there is redundant power available for energy storage. When the difference between the photovoltaic power generation and the base load is less than zero, it is a negative value. When the difference between the photovoltaic power generation and the base load is negative, it indicates that the building's base load exceeds the photovoltaic supply capacity and external grid supplementation is required.

[0052] S4.3. Based on the thermal inertia parameters of the building structure, the maximum precooling and preheating energy of the building is obtained using the thermal energy balance method.

[0053] Furthermore, based on the heat capacity parameters, the energy storage capacity corresponding to a unit temperature change in the building envelope is determined. The target temperature range for pre-cooling is set from 30℃ to 25℃. Combining the current internal and external temperature trends, the heat that the building envelope can store or release within this temperature range is calculated. By integrating the changes in heat input, heat output, and internal heat sources using the heat balance formula, the maximum pre-cooling energy and maximum pre-heating energy per unit time are obtained. The expressions are as follows: ; in, For maximum precooling energy, For the heat input of the external envelope, As an internal heat source, For the heat output of the external envelope structure; ; in, This is the maximum preheating energy; S4.4 Cross-compare the difference between photovoltaic power generation and building foundation electrical load with the building’s maximum pre-cooling and pre-heating energy to obtain the load target for the pre-cooling and pre-heating time periods.

[0054] Furthermore, raw data on total building power consumption and photovoltaic power generation are collected minute by minute from smart meters and photovoltaic inverters. Outliers and null values ​​are removed through data cleaning. Then, using the same timestamp as a reference, the building's basic electrical load (fixed loads such as lighting, sockets, and equipment obtained through the BMS system) is subtracted from the photovoltaic power generation to obtain the net power difference per minute. Next, the difference data is filtered using a moving average (with a 5-minute window) to eliminate instantaneous fluctuations. Finally, the processed continuous minute-level difference data is stored in a time-series database, forming a data set including timestamps and photovoltaic power generation. The standard time-series curves of four fields—base load, net difference—are used to identify photovoltaic surplus periods where the power difference is greater than zero. Then, the available surplus power for each photovoltaic surplus period is matched with the building's thermal demand. When the surplus power exceeds the pre-cooling demand threshold, the pre-cooling mode is activated; when it exceeds the pre-heating demand threshold, the pre-heating mode is activated. Simultaneously, the building's thermal inertia time constant is considered to ensure that the operating period matches the temperature response delay. Finally, through an iterative optimization algorithm, under the condition of satisfying indoor comfort constraints, the optimal pre-cooling / pre-heating load target values ​​for each period are determined, resulting in the load targets for the pre-cooling and pre-heating time periods. It should also be noted that the pre-cooling demand threshold is set as follows: Based on the thermal capacity characteristics of the building envelope, the total heat that the building needs to remove during the target temperature range (e.g., from 28°C to 26°C) is first determined, and then divided by the length of the pre-cooling operation period (e.g., 2 hours) to obtain the minimum pre-cooling power requirement per unit time. Simultaneously, 70% of the rated power is considered as an upper limit, and the smaller of the two values ​​is ultimately taken as the pre-cooling demand threshold. Preheating demand threshold setting: By analyzing the building's heat loss coefficient, the minimum heating power required to maintain the target temperature (e.g., from 16℃ to 20℃) is calculated. Combining historical heating data, 60% of the building's heat load under typical winter conditions is taken as the baseline value, and a 5%-10% redundancy is added to cope with temperature fluctuations, ultimately determining the preheating demand threshold.

[0055] S4.5. The load target for the precooling and preheating periods is calculated using the load-generation difference method to obtain the variable load window for photovoltaic power generation. Specifically, the expression is as follows: ; in, For photovoltaic power generation variable load window, For time Maximum precooling and maximum preheating energy, For pre-cooling energy, To preheat energy, For load targets.

[0056] S5. Calculate the precooling and preheating windows of the building structure based on the variable load window of photovoltaic power generation.

[0057] S5.1 Based on the variable load window of photovoltaic power generation, the adjustment window is obtained by screening out the time period when the photovoltaic power generation is higher than the building foundation load.

[0058] Furthermore, based on the photovoltaic power generation and building foundation load values, the power difference between the photovoltaic power generation and the foundation load is obtained, and all time periods that satisfy the condition that the difference between the photovoltaic power generation and the foundation load is greater than zero are identified. The set of consecutive positive difference time periods is defined as the photovoltaic redundancy adjustable window.

[0059] S5.2 Calculate the precooling and preheating windows of the building structure using the thermal hysteresis characteristic analysis method. Specifically, the expression is: ; in, For precooling and preheating windows of building structures, For time Under the heat load, For photovoltaic redundant power, For pre-cooling windows of building structures, For preheating windows of building structures.

[0060] S6. Develop building energy-saving optimization and adjustment strategies by utilizing the pre-cooling and preheating windows of the building structure.

[0061] S6.1 Based on the precooling and preheating windows of the building structure, the target temperature of the building is obtained through the thermal comfort zone inversion calculation method.

[0062] Furthermore, the pre-cooling / preheating operation time window is determined based on the duration during which photovoltaic power generation exceeds the base load. The maximum heat energy that can be stored / released during this period is calculated by combining the building envelope's thermal capacity parameters (such as wall thermal inertia index). Then, based on the ASHRAE 55 thermal comfort standard, with the predicted average voting index PMV±0.5 as a constraint, the heat balance equation is solved inversely. After considering factors such as occupant activity patterns, clothing thermal resistance, and indoor radiant temperature, the temperature adjustment range that meets comfort requirements is calculated. Finally, based on the matching degree between the building's energy storage capacity and the remaining photovoltaic power, the achievable target temperature for the building is selected within this range (e.g., a summer pre-cooling target of 26±0.5℃ and a winter pre-heating target of 20±0.5℃).

[0063] S6.2. Based on the matching degree between photovoltaic power generation and building foundation load, the photovoltaic power supply redundancy period is obtained.

[0064] Furthermore, by comparing the difference between photovoltaic power generation and building foundation load in the same time period, the matching degree between photovoltaic power generation and building foundation load is obtained. The time period when photovoltaic power generation exceeds building foundation load is analyzed, and the redundant power during the photovoltaic power generation period is identified. When the photovoltaic power generation is greater than the building foundation load, this excess power is regarded as redundant energy supply, indicating that there is additional power available during the redundant photovoltaic energy supply period.

[0065] S6.3. Use time series energy consumption balance analysis to analyze the redundant period of photovoltaic power supply and the target temperature of the building to obtain the energy consumption of the building in different time periods.

[0066] Furthermore, real-time acquisition of photovoltaic inverter output power and building sub-meter data is used to calculate the net difference between power generation and base load at 1-minute intervals. A sliding time window algorithm (30-minute window length) is employed to identify periods of continuous positive differences, marking them as photovoltaic power supply redundancy periods. Based on building thermal zoning parameters derived from the BIM model, combined with target temperature curves and measured indoor and outdoor temperature and humidity, a dynamic heat balance equation is established: a 15-minute time step is set in the EnergyPlus simulation platform, and thermal property parameters of the building envelope, HVAC system COP curve, and occupancy density schedule are input. By simultaneously solving the conduction, convection, and radiation heat transfer equations, the actual energy consumption generated by wall heat storage, air conditioning cooling, and lighting equipment at each time period is quantified to obtain the actual energy consumption.

[0067] S6.4. Based on energy consumption, a rolling optimization algorithm is used to obtain a building energy-saving optimization and adjustment strategy.

[0068] Furthermore, based on the energy consumption data of lighting equipment obtained from building energy consumption, a rolling time-domain optimization framework is used to update the system status every 15 minutes based on the latest sensor data, and solve for the optimal control sequence for the next 4 hours. The load flexibility adjustment coefficient is introduced to quantify the interruptibility characteristics of the equipment, and robust optimization is used to handle the uncertainty of photovoltaic power generation. By dynamically adjusting the HVAC operation curve and time-zone lighting strategy, the daily average energy consumption is reduced by 12%-18% while ensuring that the indoor PMV index fluctuation does not exceed ±0.5, thus achieving a building energy-saving optimization and adjustment strategy.

[0069] This embodiment also provides a building energy-saving optimization system based on photovoltaic power generation, including: a data acquisition model, which collects material parameters of the building structure, building spatial layout information, meteorological data and photovoltaic operation data, and constructs 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 power generation module calculates the photovoltaic power generation capacity by using physical mechanism modeling methods to analyze meteorological data and photovoltaic operation data. The identification module cross-compares the photovoltaic power generation capacity and the thermal inertia parameters of the building structure to obtain the load target for the pre-cooling and pre-heating periods and identify the variable load window of photovoltaic power generation. The adjustment module calculates the precooling and preheating windows of the building structure based on the variable load window of photovoltaic power generation. The adjustment module, through the pre-cooling and preheating windows of the building structure, formulates building energy-saving optimization adjustment strategies.

[0070] This embodiment also provides a computer device applicable to the building energy consumption optimization method based on photovoltaic power generation, including: 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, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0072] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the building energy consumption optimization method based on photovoltaic power generation as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0073] In summary, this invention identifies variable load windows by cross-comparing photovoltaic power generation with building thermal inertia parameters, enabling efficient on-site utilization of photovoltaic power. Furthermore, by combining thermal hysteresis characteristics with thermal comfort objectives, it determines the optimal pre-cooling / pre-heating period, formulates dynamic energy-saving strategies, effectively reduces building energy consumption peaks, improves energy efficiency and user comfort, and promotes synergistic optimization between buildings and photovoltaics. It possesses significant energy-saving and carbon-reduction effects and practical application value. It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for optimizing building energy consumption based on photovoltaic power generation, characterized in that: include, Collect material parameters of the building structure, information on the building's spatial layout, meteorological data, and photovoltaic operation data; construct a thermal response model to obtain the thermal inertia parameters of the building structure. The photovoltaic power generation capacity is obtained by calculating the meteorological data and photovoltaic operation data using physical mechanism modeling method; By cross-comparing the photovoltaic power generation capacity and the thermal inertia parameters of the building structure, the load target for the pre-cooling and pre-heating periods is obtained, and the variable load window of photovoltaic power generation is identified. The load target for the precooling and preheating periods is calculated using the load-generation difference method to obtain the variable load window for photovoltaic power generation. Specifically, the expression is as follows: ； in, For photovoltaic power generation variable load window, For time Maximum precooling and maximum preheating energy, For pre-cooling energy, To preheat energy, For time The photovoltaic power generation capacity below For load targets; Based on the variable load window of photovoltaic power generation, calculate the precooling and preheating windows of the building structure; Based on the variable load window of photovoltaic power generation, the adjustment window is obtained by screening out the time period when the photovoltaic power generation is higher than the building foundation load. Based on the photovoltaic power generation and building foundation load values, the power difference between the photovoltaic power generation and the foundation load is obtained, and all time periods that satisfy the condition that the difference between the photovoltaic power generation and the foundation load is greater than zero are identified. The set of consecutive positive difference time periods is defined as the photovoltaic redundancy adjustable window. The precooling and preheating windows of a building structure are calculated using the thermal hysteresis characteristic analysis method. Specifically, the expression is as follows: ； in, For precooling and preheating windows of building structures, For time Under the heat load, For photovoltaic redundant power, For pre-cooling windows of building structures, For preheating windows of building structures; Develop energy-saving optimization strategies for buildings by utilizing the pre-cooling and pre-heating windows of the building structure.

2. The building energy consumption optimization method based on photovoltaic power generation as described in claim 1, characterized in that: Collecting material parameters of the building structure, information on the building's spatial layout, meteorological data, and photovoltaic operation data includes the following steps. 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.

3. The building energy consumption optimization method based on photovoltaic power generation as described in claim 2, characterized in that: A thermal response model is constructed 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. Includes the following steps, 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.

4. The building energy consumption optimization method based on photovoltaic power generation as described in claim 3, characterized in that: The calculation of photovoltaic power generation using physical mechanism modeling methods based on 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.

5. The building energy consumption optimization method based on photovoltaic power generation as described in claim 4, characterized in that: By cross-comparing photovoltaic power generation capacity and the thermal inertia parameters of the building structure, the load targets for the pre-cooling and pre-heating periods are obtained, and the variable load window for photovoltaic power generation is identified, including the following steps. Based on the building spatial layout information, the building foundation load is obtained; Based on photovoltaic power generation, the redundancy of power generation is analyzed, and the difference between photovoltaic power generation and building foundation electrical load is calculated. Based on the thermal inertia parameters of the building structure, the maximum precooling and preheating energy of the building is obtained; The difference between photovoltaic power generation and building foundation electrical load is cross-compared with the building’s maximum pre-cooling and pre-heating energy to obtain the load target for the pre-cooling and pre-heating time periods. The load target for the precooling and preheating periods is calculated to obtain the variable load window for photovoltaic power generation.

6. The building energy consumption optimization method based on photovoltaic power generation as described in claim 5, characterized in that: Calculating the precooling and preheating windows of a building structure based on the variable load window of photovoltaic power generation includes the following steps. Based on the variable load window of photovoltaic power generation, the adjustment window is obtained by screening out the time period when the photovoltaic power generation is higher than the building foundation load. Based on the thermal inertia parameters of the building structure, the precooling and preheating windows of the building structure are calculated.

7. The building energy consumption optimization method based on photovoltaic power generation as described in claim 6, characterized in that: Developing building energy efficiency optimization strategies by utilizing the precooling and preheating windows of building structures includes the following steps. The target temperature of the building is obtained based on the precooling and preheating windows of the building structure. The redundancy period of photovoltaic power supply is obtained based on the matching degree between photovoltaic power generation and building foundation load; The building's energy consumption during different time periods was obtained by analyzing the redundant periods of photovoltaic power supply and the target temperature of the building. Based on energy consumption, strategies for optimizing and adjusting building energy conservation are derived.

8. A building energy-saving optimization system based on photovoltaic power generation, based on the building energy consumption optimization method based on photovoltaic power generation as described in any one of claims 1 to 7, characterized in that: include, The data acquisition model collects material parameters of the building structure, building spatial layout information, meteorological data, and photovoltaic operation data. Based on the material parameters of the building envelope and the building spatial layout information, a thermal response model is constructed to obtain the thermal inertia parameters of the building structure. The power generation module calculates the photovoltaic power generation capacity by using physical mechanism modeling methods to analyze meteorological data and photovoltaic operation data. The identification module cross-compares the photovoltaic power generation capacity and the thermal inertia parameters of the building structure to obtain the load target for the pre-cooling and pre-heating periods and identify the variable load window of photovoltaic power generation. The adjustment module calculates the precooling and preheating windows of the building structure based on the variable load window of photovoltaic power generation. The adjustment module, through the pre-cooling and preheating windows of the building structure, formulates building energy-saving optimization adjustment strategies.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the building energy consumption optimization method based on photovoltaic power generation as described in any one of claims 1 to 7.

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

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