Near-zero energy consumption building energy supply method and system based on multi-objective optimization

Abstract

The invention discloses a near-zero energy consumption building energy supply method and system based on multi-objective optimization, and relates to the technical field of building energy efficiency and renewable energy application, and the method comprises the steps: collecting energy consumption data in a building, and constructing a multi-objective evaluation system according to the energy consumption data; performing weight optimization on the multi-target evaluation system to obtain an optimal weight coefficient; combining the optimal weight coefficient with a solar photovoltaic system and a ground source heat pump system, and establishing a renewable energy supply strategy model; and outputting an energy supply scheduling instruction of the building in each time period based on the renewable energy energy supply strategy model, and adjusting operation parameters of a solar photovoltaic system and a ground source heat pump system. According to the invention, by adjusting the operation mode of the equipment in real time, the balance between the energy efficiency and the comfort level is ensured, the common problem of energy efficiency waste or insufficient comfort level in the traditional method is avoided, and by means of intelligent scheduling and optimization, the energy consumption of the building can be effectively reduced, and the sustainability of energy utilization of the building is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of building energy efficiency and renewable energy application, and in particular to a near-zero energy building energy supply method and system based on multi-objective optimization. Background Art

[0002] With the intensification of the global energy crisis and increasingly stringent environmental protection requirements, the optimization and innovation of energy utilization in the construction industry has become an important topic of concern in modern society. In particular, the concept of zero energy buildings (ZEBs) has gradually emerged and has become one of the directions of sustainable building design. Zero energy buildings achieve zero net energy exchange with external energy networks by optimizing the energy demand of buildings, improving energy efficiency and combining the supply of renewable energy. In order to achieve this goal, renewable energy technologies such as solar photovoltaic systems and ground source heat pump systems are increasingly used in the construction field. These technologies reduce the energy consumption of buildings to a certain extent by directly utilizing natural resources such as solar energy and geothermal energy, and realize a self-sufficient energy supply model for buildings. In addition, as an important part of building energy optimization, energy management systems (EMS) are gradually moving towards efficient and refined management with the help of intelligent sensing technology, data acquisition technology and control algorithms.

[0003] However, the existing energy supply methods for zero-energy buildings still have some shortcomings in terms of energy collection, storage and scheduling. Most current zero-energy buildings rely mainly on a single energy source when designing energy management systems, or simply match the use time of renewable energy with the load demand of the building, and fail to fully consider the synergy between different energy systems. In addition, the optimization algorithms in the existing technologies are usually guided by a single goal and fail to comprehensively consider the multi-dimensional needs of the building, such as comfort, energy efficiency, environmental impact, etc., resulting in uneven energy efficiency performance of the system under different operating modes. When faced with different climatic conditions, building structures and usage methods, the lack of flexible scheduling strategies can easily lead to insufficient or wasteful energy supply. Therefore, how to establish a multi-objective optimization energy supply strategy and accurately schedule different energy systems to improve the energy self-sufficiency and comfort of buildings is still a technical problem that needs to be solved in this field. Summary of the invention

[0004] In view of the problems existing in the prior art, the present invention proposes a near-zero energy building energy supply method and system based on multi-objective optimization.

[0005] Therefore, the present invention provides a near-zero energy building energy supply method based on multi-objective optimization, which can solve the problems mentioned in the background technology.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a near-zero energy building energy supply method based on multi-objective optimization, which includes:

[0008] Collecting energy consumption data in buildings and constructing a multi-objective evaluation system based on the energy consumption data;

[0009] Optimizing the weight of the multi-objective evaluation system to obtain an optimal weight coefficient;

[0010] The optimal weight coefficient is combined with a solar photovoltaic system and a ground source heat pump system to establish a renewable energy supply strategy model;

[0011] Based on the renewable energy supply strategy model, the energy supply scheduling instructions for the building in each time period are output to adjust the operating parameters of the solar photovoltaic system and the ground source heat pump system.

[0012] As a preferred solution of the near-zero energy building energy supply method based on multi-objective optimization described in the present invention, wherein: the energy consumption data includes temperature sensor data, humidity sensor data, light sensor data and power load data;

[0013] The temperature sensor data is collected from temperature sensors arranged in various indoor functional areas, corridors, stairwells and facades of the building;

[0014] The humidity sensor data is collected by humidity sensors arranged in various indoor functional areas and exterior facades of the building;

[0015] The light sensor data is collected by light sensors arranged in indoor and outdoor window areas and roof areas of the building;

[0016] The power load data is collected based on the building's power system.

[0017] As a preferred solution of the near-zero energy building energy supply method based on multi-objective optimization described in the present invention, wherein: the multi-objective evaluation system includes indoor comfort index, energy consumption index and lighting index;

[0018] The indoor comfort index includes a temperature and humidity deviation, and the indoor comfort index is calculated according to the temperature and humidity deviation;

[0019] The temperature and humidity deviation is calculated by comparing the temperature sensor data with a preset temperature comfort range, and comparing the humidity sensor data with a preset relative humidity comfort range to obtain a temperature and humidity deviation value;

[0020] The power load data is standardized according to the building use area to obtain the power consumption per unit area, and compared with the benchmark value specified in the building energy-saving design standard to generate the energy consumption index;

[0021] The indoor daylighting coefficient is calculated according to the light sensor data, and the daylighting coefficient is compared with the minimum requirement value specified in the building daylighting standard to generate a daylighting index; wherein the daylighting coefficient is the percentage of the indoor natural daylighting illuminance to the total natural illuminance of the outdoor horizontal surface at the same time.

[0022] As a preferred solution of the near-zero energy building energy supply method based on multi-objective optimization described in the present invention, the temperature and humidity deviation value is calculated as shown in the following formula:

[0023]

[0024] Where T is the measured temperature value, T set is the set temperature value, H is the measured humidity value, H set is the set humidity value, ΔT max and ΔH max are the maximum allowable temperature and humidity deviations, respectively, and α and β are weight coefficients;

[0025] The indoor comfort index is calculated as follows:

[0026]

[0027] Among them, P i is the power consumption in time period i, t i is the duration of time period i, A is the building area, E0 is the benchmark energy consumption value, λ is the temperature correction coefficient, and T0 is the outdoor design temperature;

[0028] The calculation of the lighting index is shown in the following formula:

[0029]

[0030] Among them, E in is the indoor natural lighting illumination, E out is the total natural illumination on the outdoor horizontal surface, θ is the solar altitude angle, d is the distance from the measuring point to the window, d0 is the reference distance, and μ and ρ are correction coefficients.

[0031] As a preferred solution of the near-zero energy building energy supply method based on multi-objective optimization described in the present invention, wherein: the optimal weight coefficient is obtained by optimizing the objective function of the multi-objective evaluation system using a multi-objective particle swarm optimization algorithm;

[0032] The multi-objective particle swarm optimization algorithm includes particle velocity update, particle position update and fitness function;

[0033] The particle velocity update is expressed as follows:

[0034] V id (t+1)=ω·V id (t)+c1·r1·[P id (t)-X id (t)]+c2·r2·[P gd (t)-X id (t)]+c3·r3·(1-∑w i )

[0035] Among them, V id is the velocity of the ith particle in dimension d, X id is the position of the ith particle in d dimension, P id (t) is the optimal position of the individual, P gd is the global optimal position, ω is the inertia weight, c1, c2, c3 are learning factors, r1, r2, r3 are random numbers between [0,1];

[0036] The particle position update is expressed by the following formula:

[0037]

[0038] Among them, σ is the adaptive adjustment coefficient, and F(t) is the objective function value of the current iteration.

[0039] As a preferred solution of the near-zero energy building energy supply method based on multi-objective optimization described in the present invention, the fitness function is shown as follows:

[0040]

[0041] Among them, η is the gradient penalty factor, θ is the weight balance factor, F is the objective function of the multi-objective evaluation system, w1, w2, w3 are the weight coefficients in the objective function of the multi-objective evaluation system;

[0042] During the iterative process of the multi-objective particle swarm optimization algorithm, the weight combination obtained in each iteration is evaluated. When the change in the objective function value of a preset number of consecutive iterations is less than a first preset threshold and the maximum particle speed is less than a second preset threshold, the weight combination corresponding to the current global optimal particle is output as the optimal weight coefficient.

[0043] As a preferred solution of the near-zero energy building energy supply method based on multi-objective optimization described in the present invention, the construction of the renewable energy supply strategy model includes:

[0044] Collecting the operating data of the solar photovoltaic system and the ground source heat pump system to construct an equipment performance characteristic model;

[0045] Constructing the renewable energy supply strategy model according to the optimal parameters and the equipment performance characteristic model;

[0046] The renewable energy supply strategy model further includes optimizing the renewable energy supply strategy model according to the neural network model and the loss function, and outputting the optimal supply strategy;

[0047] The equipment performance characteristic model includes the power generation efficiency of the solar photovoltaic system and the performance coefficient of the ground source heat pump system.

[0048] As a preferred solution of the near-zero energy building energy supply method based on multi-objective optimization described in the present invention, the objective function of the renewable energy supply strategy model is expressed by the following formula:

[0049] S(t)=arg min{L+μ1[P PV (t)-P PV,max (t)] + +μ2[Q HP (t)-Q HP,max (t)] +

[0050] +μ3∑δ(t)}

[0051] Among them, P PV (t) and Q HP (t) are the current solar photovoltaic power generation and the actual energy load of the ground source heat pump at the current moment, P PV,max (t) and Q HP,max (t) are the maximum power generation limit of the solar photovoltaic system and the maximum energy supply load limit of the ground source heat pump system, δ(t) is the equipment start and stop penalty term, μ1, μ2, μ3 are weight coefficients, S(t) is the optimization objective function value of the energy supply strategy, and L is the loss function.

[0052] In a second aspect, an embodiment of the present invention provides a near-zero energy building energy supply system based on multi-objective optimization, which includes:

[0053] A data collection module, used to collect energy consumption data in the building and build a multi-objective evaluation system based on the energy consumption data;

[0054] A parameter optimization module is used to optimize the weight of the multi-objective evaluation system to obtain the optimal weight coefficient;

[0055] A model building module, used to combine the optimal weight coefficient with the solar photovoltaic system and the ground source heat pump system to establish a renewable energy supply strategy model;

[0056] The control module is used to output energy supply scheduling instructions for the building in each time period based on the renewable energy supply strategy model, and adjust the operating parameters of the solar photovoltaic system and the ground source heat pump system.

[0057] The beneficial effect of the present invention is to collect temperature, humidity, light and power load data inside and outside the building, and combine the particle swarm optimization algorithm to optimize the weights of the three goals of indoor comfort, energy consumption and natural lighting, and establish a renewable energy supply strategy model based on a multi-objective evaluation system. Through in-depth analysis of the real-time operation data of the solar photovoltaic system and the ground source heat pump system, combined with the optimal weight coefficient, the model can accurately calculate the energy supply demand in each period and output the corresponding scheduling instructions. This scheduling strategy can optimize the operating parameters of the solar photovoltaic and ground source heat pump systems and maximize the efficiency of energy utilization while ensuring indoor temperature and humidity comfort and natural lighting. Especially in the face of dynamically changing meteorological conditions, power loads and system operating conditions, this solution ensures the balance between energy efficiency and comfort by adjusting the equipment operation mode in real time, avoiding the common problems of energy efficiency waste or insufficient comfort in traditional methods. Finally, through intelligent scheduling and optimization, this method can effectively reduce the energy consumption of buildings and improve the sustainability of their energy utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0059] Figure 1 A flowchart of a near-zero energy building energy supply method based on multi-objective optimization. DETAILED DESCRIPTION

[0060] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0061] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0062] 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 term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0063] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0064] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0065] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0066] Example 1

[0067] Reference Figure 1 , which is the first embodiment of the present invention, and provides a near-zero energy building energy supply method based on multi-objective optimization, including:

[0068] S1: Collecting temperature sensor data, humidity sensor data, light sensor data and power load data in a building, and storing the temperature sensor data, humidity sensor data, light sensor data and power load data in a database;

[0069] Temperature sensors, humidity sensors and light sensors are arranged in the interior and exterior areas of the building, wherein the temperature sensors are arranged in the indoor functional areas, corridors, stairwells and exterior facades of the building, the humidity sensors are arranged in the indoor functional areas and exterior facades of the building, and the light sensors are arranged in the indoor and outdoor window areas and roof areas of the building; the temperature sensor is used to collect indoor and outdoor temperature data, the humidity sensor is used to collect indoor and outdoor relative humidity data, the light sensor is used to collect indoor and outdoor light intensity data, and the real-time power load data collected by the building power system is recorded at the same time; the temperature sensor data, the humidity sensor data, the light sensor data and the power load data are transmitted to a data processing unit through a wired communication network or a wireless communication network, and the temperature sensor data, the humidity sensor data, the light sensor data and the power load data received by the data processing unit are stored in a database in time series.

[0070] S2: Establishing an indoor comfort index according to the temperature sensor data and the humidity sensor data, establishing an energy consumption index according to the power load data, and establishing a natural lighting index according to the light sensor data, thereby constructing a multi-objective evaluation system;

[0071] The temperature sensor data and the humidity sensor data are retrieved from the database, the temperature sensor data is compared with the preset temperature comfort range of 18°C-26°C, the humidity sensor data is compared with the preset relative humidity comfort range of 40%-70%, and the temperature and humidity deviation value is calculated. The temperature and humidity deviation can be expressed by the following formula:

[0072]

[0073] Where T is the measured temperature value, T set is the set temperature value (18℃-26℃), H is the measured humidity value, H set To set the humidity value (40%-70%), ΔT max and ΔH max are the maximum allowable temperature and humidity deviations, respectively, α and β are weight coefficients and α+β=1;

[0074] Substitute the temperature and humidity deviation value into the preset PMV-PPD human comfort model to obtain the indoor comfort index, as shown in the following formula:

[0075]

[0076] Where M is the metabolic rate of the human body, A Du is the human body surface area, and γ is the correction coefficient.

[0077] The power load data is retrieved from the database, and the power load data is standardized according to the building use area to obtain the power consumption per unit area, and compared with the benchmark value specified in the building energy-saving design standard to generate an energy consumption index, as shown in the following formula:

[0078]

[0079] Among them, P i is the power consumption in time period i, t i is the duration of time period i, A is the building area, E0 is the benchmark energy consumption value, λ is the temperature correction coefficient, and T0 is the outdoor design temperature.

[0080] The light sensor data is retrieved from the database to calculate the indoor daylighting factor, where the daylighting factor is the percentage of the indoor natural daylighting illuminance to the total natural illuminance of the outdoor horizontal surface at the same time, and the daylighting factor is compared with the minimum requirement value of 2% specified in the building daylighting standard to generate a natural light index, as shown in the following formula:

[0081]

[0082] Among them, E in is the indoor natural lighting illumination, E out is the total natural illumination on the outdoor horizontal surface, θ is the solar altitude angle, d is the distance from the measuring point to the window, d0 is the reference distance, and μ and ρ are correction coefficients.

[0083] The indoor comfort index, the energy consumption index and the natural lighting index are combined in a linear weighted manner to construct an objective function of a multi-objective evaluation system.

[0084] F=w1·I c +w2·(1-I e )+w3·I l

[0085] Among them, w1, w2, w3 are weight coefficients and satisfy w1+w2+w3=1.

[0086] S3: Optimizing the weights of the indoor comfort index, the energy consumption index and the natural lighting index by using a particle swarm algorithm to obtain an optimal weight coefficient;

[0087] Based on the indoor comfort index, the energy consumption index and the natural lighting index, an improved multi-objective particle swarm optimization algorithm is used to optimize the weight coefficient, wherein the improved multi-objective particle swarm optimization algorithm sets the initial population size to 50, the maximum number of iterations to 200, the learning factors to 2.05 and 2.05 respectively, and the inertia weight ranges from 0.4 to 0.9 and decreases linearly with the number of iterations.

[0088] In the iterative process of the particle swarm optimization algorithm, each particle contains weight information of three dimensions, and the sum of the weights of each dimension is 1. The particle speed update adopts an adaptive inertia weight strategy;

[0089] More specifically, each particle is represented by a weight vector P(w1, w2, w3), and the particle velocity update formula is:

[0090] V id (t + 1) = ω·V id (t)+c1·r1·[P id (t)-X id (t)]+c2·r2·[P gd (t)-X id (t)]+c3·r3·(1-∑w i )

[0091] Among them, V id is the velocity of the ith particle in dimension d, X id is the position of the ith particle in d dimension, P id (t) is the optimal position of the individual, P gd is the global optimal position, ω is the inertia weight, c1, c2, c3 are learning factors, and r1, r2, r3 are random numbers between [0,1].

[0092] The particle position update introduces a contraction factor and adds a weight balance penalty term to the fitness function;

[0093] Then the particle position is updated as follows:

[0094]

[0095] Among them, σ is the adaptive adjustment coefficient, and F(t) is the objective function value of the current iteration.

[0096] Its fitness function is shown as follows:

[0097]

[0098] Among them, η is the gradient penalty factor, θ is the weight balance factor;

[0099] The weight combination obtained in each iteration is evaluated. When the change in the objective function value of 20 consecutive iterations is less than the preset threshold value of 0.001 and the maximum particle speed is less than the preset threshold value of 0.0001, the weight combination corresponding to the current global optimal particle is output as the optimal weight coefficient, that is:

[0100] |F(t)-F(t-1)|≤ε and max(|Vid |)≤δ

[0101] The final output is the optimal weight coefficient combination (w1*, w2*, w3*) that satisfies the fitness function as described above.

[0102] S4: combining the optimal weight coefficient with the power generation of the solar photovoltaic system and the cooling capacity of the ground source heat pump system to establish a renewable energy supply strategy model;

[0103] The operating parameters such as battery module temperature, solar radiation intensity, power generation, module surface dust coverage, and module aging coefficient are collected in real time from the solar photovoltaic system; the operating parameters such as soil temperature field distribution, system inlet and outlet water temperature, cooling / heating power, compressor frequency, and heat exchanger efficiency are collected in real time from the ground source heat pump system; based on these parameters, an equipment performance characteristic model considering multiple influencing factors is established, among which the photovoltaic system efficiency model considers the coupled influence of temperature effect, radiation intensity effect, incident angle effect, dust attenuation effect, and aging effect; the ground source heat pump performance coefficient model considers the combined effect of temperature difference effect, soil temperature effect, partial load characteristics, and heat exchanger efficiency.

[0104] Furthermore, an energy supply strategy model is established based on the optimal weight coefficients (w1*, w2*, w3*). First, an equipment performance characteristic equation is established, including the power generation efficiency of the solar photovoltaic system and the performance coefficient of the ground source heat pump system.

[0105] Among them, the power generation efficiency of the solar photovoltaic system can be expressed by the following formula:

[0106]

[0107] Among them, η0 is the standard working efficiency, β is the temperature coefficient, T c is the component temperature, T ref is the reference temperature 25°C, G is the radiation intensity, G0 is the standard radiation intensity 1000W / m 2 , θ is the incident angle, κ and ψ are correction coefficients;

[0108] The calculation of the performance coefficient of the ground source heat pump system is shown as follows:

[0109]

[0110] Among them, COP r is the rated performance coefficient, ΔT is the inlet and outlet water temperature difference, ΔT r is the rated temperature difference, T s is the soil temperature, T sr is the soil reference temperature, Q is the actual load, Q r is the rated load, β1, α2 and ξ are correction factors.

[0111] Furthermore, the deep neural network prediction model is used to predict the predicted values ​​of equipment operating parameters for the next 48 time periods, including photovoltaic system power generation, ground source heat pump cooling / heating power, system total efficiency, etc. It adopts a 5-layer deep neural network structure, the input layer contains weather forecast data (temperature, humidity, radiation, etc.), load forecast data (cold load, heat load, electric load), equipment status data (efficiency, COP, etc.) and optimal weight coefficients; the three hidden layers are set with 64, 32, and 16 neurons respectively, using the ReLU activation function, and adding a Dropout layer between each layer to prevent overfitting. The output of the neuron in the lth layer of the neural network is:

[0112]

[0113] Among them, W (l) is the weight matrix, b (l) is the bias vector, f is the ReLU activation function, φ (l) is the inter-layer weight adjustment coefficient, h (l) is the output of the lth layer of the neural network, h (l-1) is the output of the l-1th layer of the neural network, I c is the comfort index, I e is the energy consumption index, I l It is the lighting indicator.

[0114] Furthermore, the loss function of the energy supply strategy model is designed, which contains four parts: (1) prediction error term, which measures the deviation between the predicted value and the actual value; (2) time series smoothing term, which ensures the time continuity of the prediction result; (3) equipment constraint term, which ensures that the prediction result meets the equipment operation limit; (4) multi-objective balance term, which coordinates the three objectives of indoor comfort, energy efficiency and natural lighting according to the optimal weight coefficient. The three items are balanced by adaptive weight coefficients, and the weight coefficients are dynamically adjusted during the training process. As shown in the following formula:

[0115]

[0116] Among them, y t is the collected system output value, is the output value predicted by the model, λ1, λ2, λ3 are balance coefficients, I i,ref is the reference value of the i-th indicator, L is the loss function of the energy supply strategy model, η PV,t is the solar photovoltaic efficiency at the current moment, η PV,t-1 is the solar photovoltaic efficiency at the previous moment, COP t is the coefficient of performance of the ground source heat pump at the current moment, COP t-1 is the performance coefficient of the ground source heat pump at the previous moment, I i,tis the actual value of the i-th indicator at the current moment.

[0117] And adopt a 48-hour rolling time domain, update the prediction model every hour, and receive the latest meteorological data, load data and equipment operation data in real time; in each prediction cycle, firstly obtain the predicted value of equipment operation parameters based on deep neural network, and then consider the actual operation constraints such as equipment start-stop constraints, climbing constraints, capacity constraints, etc., and use mixed integer programming method to solve the optimal operation strategy; in order to improve the optimization efficiency, divide the 48-hour optimization cycle into 6 8-hour sub-cycles, and use the segmented optimization method. The final functional strategy model can be shown as follows:

[0118] S(t)=arg min{L+μ1[P PV (t)-P PV,max (t)] + +μ2[Q HP (t)-Q HP,max (t)] +

[0119] +μ3∑δ(t)}

[0120] Among them, P PV (t) and Q HP (t) are the current solar photovoltaic power generation and the actual energy load of the ground source heat pump at the current moment, P PV,max (t) and Q HP,max (t) are the maximum power generation limit of the solar photovoltaic system and the maximum energy supply load limit of the ground source heat pump system, δ(t) is the equipment start and stop penalty term, μ1, μ2, μ3 are weight coefficients, S(t) is the optimization objective function value of the energy supply strategy, and L is the loss function.

[0121] S5: Outputting energy supply scheduling instructions for the building in each time period based on the renewable energy supply strategy model, and adjusting operating parameters of the solar photovoltaic system and the ground source heat pump system.

[0122] Based on the renewable energy supply strategy model, the deep neural network model is first used to predict the building energy supply demand and equipment operating parameters in each future period, including the power generation power of the solar photovoltaic system and the cooling / heating power of the ground source heat pump, and the operating status of the equipment is adjusted in real time in combination with the actual operation data, weather forecast data and load forecast data. Through the optimized energy supply strategy model, considering the multi-objective balance of indoor comfort, energy efficiency and natural lighting, the model calculates the optimal solar photovoltaic system power generation efficiency and ground source heat pump performance coefficient in each period, and then determines its optimal operating parameters. The energy supply scheduling instructions output by the model will adjust the working state of the solar photovoltaic components (such as adjusting the component temperature, radiation intensity, etc.) and the operating mode of the ground source heat pump system (such as adjusting the inlet and outlet water temperature difference, load regulation, etc.) in real time according to the actual factors such as the maximum power limit of the current system, the equipment start-stop constraints, the temperature difference, and the load, to ensure that each system meets the energy demand of the building with the best efficiency in each period, and achieves a comprehensive balance between efficient energy utilization and building comfort.

[0123] Furthermore, this embodiment also provides a near-zero energy building energy supply system based on multi-objective optimization, including:

[0124] A data collection module, used to collect energy consumption data in the building and build a multi-objective evaluation system based on the energy consumption data;

[0125] A parameter optimization module is used to optimize the weight of the multi-objective evaluation system to obtain the optimal weight coefficient;

[0126] A model building module, used to combine the optimal weight coefficient with the solar photovoltaic system and the ground source heat pump system to establish a renewable energy supply strategy model;

[0127] The control module is used to output energy supply scheduling instructions for the building in each time period based on the renewable energy supply strategy model, and adjust the operating parameters of the solar photovoltaic system and the ground source heat pump system.

[0128] This embodiment also provides a computer device, which is suitable for the case of a near-zero energy building energy supply method based on multi-objective optimization, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the near-zero energy building energy supply method based on multi-objective optimization as proposed in the above embodiment.

[0129] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used 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 the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0130] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for supplying energy to a near-zero-energy building based on multi-objective optimization as proposed in the above embodiment is implemented.

[0131] The storage medium proposed in this embodiment and the data storage method proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be found in the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0132] Example 2

[0133] This is the second embodiment of the present invention, which provides a near-zero-energy building energy supply method based on multi-objective optimization. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0134] This embodiment selects a 25-story smart office building located in a provincial capital city as the experimental object, with a total construction area of ​​about 45,000 square meters. Before the experiment began, the building was first fully equipped with sensors. The temperature sensor uses a PT100 platinum resistance temperature sensor with a measurement accuracy of ±0.1°C. A total of 375 measuring points are arranged in various functional areas inside the building, and 48 measuring points are arranged on the facade; the relative humidity sensor uses a high-precision capacitive humidity sensor with a measurement range of 0-100%RH and an accuracy of ±2%RH. 280 measuring points are arranged indoors and 32 measuring points are arranged on the facade; the light sensor uses a silicon photocell illuminometer with a measurement range of 0-200000lux and an accuracy of ±3%. 156 measuring points are arranged in the indoor and outdoor window areas, and 24 measuring points are arranged in the roof area. All sensors are connected to the data acquisition device via the RS485 bus, using the Modbus-RTU communication protocol, and the sampling frequency is 1 minute / time. At the same time, a power quality analyzer is installed in the building's distribution room to monitor the power load data of each area in real time.

[0135] In order to verify the innovation and practicality of the present invention, the experiment was carried out in three stages: the first stage adopted the traditional building control scheme, relying only on simple thermostats and timing control strategies; the second stage applied the basic scheme of the present invention, introduced a multi-objective evaluation system and particle swarm algorithm optimization; the third stage further optimized the parameter configuration of the energy supply strategy model on the basis of the second stage. The experiment lasted for three months and covered different weather conditions and building usage. During the data collection process, special attention was paid to the system operation status in different scenarios such as weekdays and weekends, sunny days and cloudy days. The solar photovoltaic system uses polycrystalline silicon photovoltaic modules with a total installed capacity of 500kWp. The ground source heat pump system includes 200 vertical buried pipe heat exchangers with a drilling depth of 120 meters. The total cooling capacity of the system is 2000kW.

[0136] During the implementation of the experiment, the training of the deep neural network prediction model used the historical data of the first two months as the training set, and the data of the last month as the test set. The Adam optimizer was used in the model training process, the initial value of the learning rate was set to 0.001, and the learning rate decay strategy was used, decaying to 0.9 times the original value every 50 epochs. To prevent overfitting, the early stopping strategy was used in the training process, and the training was stopped when the validation set loss did not improve for 10 consecutive epochs. The prediction accuracy of the model reached more than 90% on the test set, meeting the requirements of engineering applications. Finally, the experimental data shown in Table 1 below was obtained:

[0137] Table 1 Experimental data of multi-objective optimization of intelligent buildings

[0138]

[0139]

[0140] Through the analysis of experimental data, it can be clearly seen that the scheme of the present invention has significant advantages over the traditional control scheme, and the optimized scheme further improves the system performance. The specific analysis is as follows:

[0141] Under the traditional control scheme, the indoor temperature is generally high (26.8℃-27.5℃), and the relative humidity is low (35%-38%), which is at the edge of the human comfort zone. The solution of the present invention accurately controls the indoor temperature between 23.5℃-24.2℃, and maintains the relative humidity in the ideal range of 52%-58%. The optimized solution further stabilizes the temperature within the optimal range of 23.1℃-23.4℃. This shows that the present invention achieves more accurate indoor environment control through a multi-objective evaluation system and a deep learning prediction model.

[0142] Under similar outdoor illumination conditions, the solution of the present invention significantly improves the indoor lighting effect. Taking a sunny working day as an example, the indoor illumination of the traditional solution is only 320 lux, while the solution of the present invention reaches 460 lux, and the optimized solution is increased to 485 lux, an increase of 51.6%. Even under cloudy conditions, the solution of the present invention can maintain a good indoor illumination level, thanks to the precise control of the natural lighting index and deep neural network prediction model introduced by the present invention.

[0143] The energy consumption per unit area shows that the traditional solution consumes 0.185 kWh / m2 on a working day. 2 The solution of the present invention reduces it to 0.142kWh / m 2 , the energy saving effect reached 23.2%. The optimized solution further reduced the energy consumption to 0.135kWh / m 2 , saving 27% energy overall compared to traditional solutions. At the same time, the solution of the present invention significantly improves the operating efficiency of the renewable energy system: the photovoltaic power generation efficiency is increased from 15.2% to 17.8% (under sunny conditions), and after optimization, it reaches a maximum of 18.4%, an increase of 21.1%; the heat pump COP value is increased from 3.2 to 4.1, and after optimization, it reaches a maximum of 4.5, an increase of 40.6%. These improvements are due to the improved particle swarm algorithm used in the present invention to optimize the system operating parameters in real time, and the energy supply strategy model to accurately control the operating status of the equipment.

[0144] The data show that the solution of the present invention can maintain a stable control effect under different weather conditions and building usage modes. Taking the indoor temperature as an example, the temperature fluctuation range under sunny and cloudy conditions is 0.7℃ and 0.4℃ respectively, while the temperature fluctuation of the traditional solution reaches 1.2℃. This shows that the present invention has successfully achieved adaptive optimization of building environment control through a multi-objective evaluation system and a deep learning prediction model.

[0145] In summary, the experimental data fully proves the significant advantages of the present invention in improving the quality of indoor building environment and improving energy efficiency. Through the innovative multi-objective evaluation system, improved particle swarm algorithm and prediction model based on deep learning, the present invention realizes the precision, intelligence and energy saving of building environment control, which has important practical value and promotion significance.

[0146] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A near-zero energy building energy supply method based on multi-objective optimization, characterized by: include: Collecting energy consumption data in buildings and constructing a multi-objective evaluation system based on the energy consumption data; Optimizing the weight of the multi-objective evaluation system to obtain an optimal weight coefficient; The optimal weight coefficient is combined with a solar photovoltaic system and a ground source heat pump system to establish a renewable energy supply strategy model; Based on the renewable energy supply strategy model, the energy supply scheduling instructions for the building in each time period are output to adjust the operating parameters of the solar photovoltaic system and the ground source heat pump system.

2. The near-zero energy building energy supply method based on multi-objective optimization according to claim 1, characterized in that: The energy consumption data includes temperature sensor data, humidity sensor data, light sensor data and power load data; The temperature sensor data is collected from temperature sensors arranged in various indoor functional areas, corridors, stairwells and facades of the building; The humidity sensor data is collected by humidity sensors arranged in various indoor functional areas and exterior facades of the building; The light sensor data is collected by light sensors arranged in indoor and outdoor window areas and roof areas of the building; The power load data is collected based on the building's power system.

3. The near-zero energy building energy supply method based on multi-objective optimization as claimed in claim 2 is characterized by: The multi-objective evaluation system includes indoor comfort index, energy consumption index and lighting index; The indoor comfort index includes a temperature and humidity deviation, and the indoor comfort index is calculated according to the temperature and humidity deviation; The temperature and humidity deviation is calculated by comparing the temperature sensor data with a preset temperature comfort range, and comparing the humidity sensor data with a preset relative humidity comfort range to obtain a temperature and humidity deviation value; The power load data is standardized according to the building use area to obtain the power consumption per unit area, and compared with the benchmark value specified in the building energy-saving design standard to generate the energy consumption index; The indoor daylighting coefficient is calculated according to the light sensor data, and the daylighting coefficient is compared with the minimum requirement value specified in the building daylighting standard to generate a daylighting index; wherein the daylighting coefficient is the percentage of the indoor natural daylighting illuminance to the total natural illuminance of the outdoor horizontal surface at the same time.

4. The near-zero energy building energy supply method based on multi-objective optimization as claimed in claim 3 is characterized by: The temperature and humidity deviation value is calculated as follows: Where T is the measured temperature value, T set is the set temperature value, H is the measured humidity value, H set is the set humidity value, ΔT max and ΔH max are the maximum allowable temperature and humidity deviations, respectively, and α and β are weight coefficients; The indoor comfort index is calculated as follows: Among them, P i is the power consumption in time period i, t i is the duration of time period i, A is the building area, E0 is the benchmark energy consumption value, λ is the temperature correction coefficient, and T0 is the outdoor design temperature; The calculation of the lighting index is shown in the following formula: Among them, E in is the indoor natural lighting illumination, E out is the total natural illumination on the outdoor horizontal surface, θ is the solar altitude angle, d is the distance from the measuring point to the window, d0 is the reference distance, and μ and ρ are correction coefficients.

5. The near-zero energy building energy supply method based on multi-objective optimization according to claim 4 is characterized in that: The optimal weight coefficient is obtained by optimizing the objective function of the multi-objective evaluation system using a multi-objective particle swarm optimization algorithm; The multi-objective particle swarm optimization algorithm includes particle velocity update, particle position update and fitness function; The particle velocity update is expressed as follows: V id (t+1)=ω·V id (t)+c1·r1·[P id (t)-X id (t)]+c2·r2·[P gd (t)-X id (t)]+c3·r3·(1-∑w i ) Among them, V id is the velocity of the ith particle in dimension d, X id is the position of the ith particle in d dimension, P id (t) is the optimal position of the individual, P gd is the global optimal position, ω is the inertia weight, c1, c2, c3 are learning factors, r1, r2, r3 are random numbers between [0,1]; The particle position update is expressed by the following formula: Among them, σ is the adaptive adjustment coefficient, and F(t) is the objective function value of the current iteration.

6. The near-zero energy building energy supply method based on multi-objective optimization according to claim 5 is characterized in that: The fitness function is shown in the following formula: Among them, η is the gradient penalty factor, θ is the weight balance factor, F is the objective function of the multi-objective evaluation system, w1, w2, w3 are the weight coefficients in the objective function of the multi-objective evaluation system; During the iterative process of the multi-objective particle swarm optimization algorithm, the weight combination obtained in each iteration is evaluated. When the change in the objective function value of a preset number of consecutive iterations is less than a first preset threshold and the maximum particle speed is less than a second preset threshold, the weight combination corresponding to the current global optimal particle is output as the optimal weight coefficient.

7. The near-zero energy building energy supply method based on multi-objective optimization according to claim 6 is characterized in that: The construction of the renewable energy supply strategy model includes: Collecting the operating data of the solar photovoltaic system and the ground source heat pump system to construct an equipment performance characteristic model; Constructing the renewable energy supply strategy model according to the optimal parameters and the equipment performance characteristic model; The renewable energy supply strategy model further includes optimizing the renewable energy supply strategy model according to the neural network model and the loss function, and outputting the optimal supply strategy; The equipment performance characteristic model includes the power generation efficiency of the solar photovoltaic system and the performance coefficient of the ground source heat pump system.

8. The near-zero energy building energy supply method based on multi-objective optimization according to claim 7 is characterized in that: The objective function of the renewable energy supply strategy model is expressed as follows: S(t)=arg min{L+μ1[P PV (t)-P PV,max (t)] + +μ2[Q HP (t)-Q HP,max (t)] + +μ3∑δ(t)} Among them, P PV (t) and Q HP (t) are the current solar photovoltaic power generation and the actual energy load of the ground source heat pump at the current moment, P PV,max (t) and Q HP,max (t) are the maximum power generation limit of the solar photovoltaic system and the maximum energy supply load limit of the ground source heat pump system, δ(t) is the equipment start and stop penalty term, μ1, μ2, μ3 are weight coefficients, S(t) is the optimization objective function value of the energy supply strategy, and L is the loss function.

9. A near-zero energy building energy supply system based on multi-objective optimization, based on the near-zero energy building energy supply method based on multi-objective optimization according to any one of claims 1 to 8, characterized in that: include: A data collection module, used to collect energy consumption data in the building and build a multi-objective evaluation system based on the energy consumption data; A parameter optimization module is used to optimize the weight of the multi-objective evaluation system to obtain the optimal weight coefficient; A model building module, used to combine the optimal weight coefficient with the solar photovoltaic system and the ground source heat pump system to establish a renewable energy supply strategy model; The control module is used to output energy supply scheduling instructions for the building in each time period based on the renewable energy supply strategy model, and adjust the operating parameters of the solar photovoltaic system and the ground source heat pump system.

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