Electric drive heat source optimization scheduling method based on building thermal inertia

Abstract

The invention relates to the technical field of energy scheduling, and discloses an electric drive heat source optimization scheduling method based on building thermal inertia, comprising: constructing a thermal load demand prediction model and a cost benefit analysis model; predicting initial heat energy required by the building at the current moment based on the heat load demand prediction model; utilizing a machine learning algorithm to predict and evaluate the thermal inertia of the current building, and optimizing and adjusting the initial heat energy; and evaluating the cost of meeting the required heat energy after adjustment in different electricity price periods, and dynamically adjusting the scheduling strategy of the electric drive heat source in combination with an evaluation result. Real-time climate data, building thermal inertia data and electricity price data can be collected and analyzed, advanced algorithms and models are used for predicting energy demands, optimizing energy distribution strategies and achieving optimal utilization of energy, in this way, the energy utilization efficiency can be improved, the energy consumption cost can be reduced, and the energy utilization efficiency can be improved. Greenhouse gas emission can be reduced, and contribution is made to environmental protection.

Description

Technical Field

[0001] The present invention relates to the technical field of energy scheduling, and in particular to an electric drive heat source optimization scheduling method based on thermal inertia of buildings. Background Art

[0002] Globally, with the rapid economic development and the continuous growth of population, the demand for energy is also rising. This trend has put tremendous pressure on the environment, and at the same time has promoted global attention to energy conservation, emission reduction and sustainable development. In this context, building energy conservation is particularly important, because buildings are not only places for people to live and work, but also major energy consumers. According to statistics, buildings consume about 40% of the world's energy and produce nearly 20% of greenhouse gas emissions. Therefore, improving building energy efficiency and reducing energy consumption are of vital importance to achieving sustainable development of global energy and environment.

[0003] In the field of building energy conservation, the optimization of energy management systems is key. Traditional building energy management systems often rely on empirical energy allocation strategies, which are usually formulated based on historical data and fixed rules. However, this approach lacks the ability to dynamically respond to real-time climate conditions, building thermal inertia, and electricity price fluctuations. Changes in real-time climate conditions, such as temperature, humidity, and wind speed, directly affect the building's heat load demand. The building's thermal inertia, that is, the building's resistance to temperature changes, also affects energy demand. In addition, the frequent and drastic fluctuations in electricity prices make energy costs an uncertain factor. Changes in these factors require real-time adjustment and optimization of the energy management system to achieve optimal allocation and use of energy.

[0004] Therefore, in order to improve building energy efficiency, reduce energy consumption, and reduce operating costs while taking into account the needs of environmental protection, it is necessary to develop a dynamic energy management method that can respond in real time to changes in climatic conditions, thermal inertia of buildings, and fluctuations in electricity prices. Summary of the invention

[0005] In view of the problems in the related technology, the present invention proposes an electric drive heat source optimization scheduling method based on the thermal inertia of buildings to overcome the above-mentioned technical problems existing in the existing related technology.

[0006] To this end, the specific technical solution adopted by the present invention is as follows:

[0007] A method for optimizing the scheduling of electric drive heat sources based on thermal inertia of buildings, comprising the following steps:

[0008] S1. Use historical climate data and building thermal data to build a heat load demand forecasting model, and combine electricity price data and the operating cost of electric drive heat sources to build a cost-benefit analysis model;

[0009] S2. Based on the heat load demand prediction model, the initial heat energy required by the building at the current moment is predicted by combining the real-time climate condition data and the building heat energy usage data at the previous moment;

[0010] S3. Use machine learning algorithms to predict and evaluate the thermal inertia of the current building, and optimize and adjust the initial thermal energy according to the thermal inertia of the building to obtain the adjusted required thermal energy;

[0011] S4. Use a cost-benefit analysis model to evaluate the cost of meeting the required thermal energy after adjustment during different electricity price periods. Based on the fluctuation range of indoor temperature, dynamically adjust the scheduling strategy of the electric drive heat source in combination with the evaluation results.

[0012] Preferably, the method of using historical climate condition data and building thermal energy data to construct a heat load demand forecasting model, and combining electricity price data and the operating cost of the electric drive heat source to construct a cost-benefit analysis model includes the following steps:

[0013] S11. Based on the historical database, obtain the climate condition data of the area where the building is located, the corresponding heat energy usage data, the electricity price data of the same period, and the operating cost data of the electric drive heat source, and pre-process the obtained data;

[0014] S12, constructing a heat load demand prediction model, and using the pre-processed historical climate conditions and building heat energy usage data for training and verification to obtain a trained heat load demand prediction model;

[0015] S13. Construct a cost-benefit analysis model, and use the pre-processed historical electricity price data and the operating cost data of the electric drive heat source for training and verification to obtain a trained cost-benefit analysis model.

[0016] Preferably, the construction of the cost-benefit analysis model includes:

[0017] Define costs and benefits: Determine costs including electricity, equipment depreciation and maintenance costs, and benefits including energy savings and environmental benefits.

[0018] Establish cost function: Establish cost function based on electricity price, heat demand and equipment efficiency.

[0019] Establish benefit function: Establish benefit function based on indoor temperature fluctuation range and heat source efficiency.

[0020] Preferably, the method of predicting the initial heat energy required by the building at the current moment based on the heat load demand prediction model in combination with the real-time climate condition data and the building heat energy usage data at the previous moment comprises the following steps:

[0021] S21, collecting real-time climate condition data of the area where the current building is located and the thermal energy usage data of the building at the last moment;

[0022] S22, performing data cleaning on the collected data, selecting features related to the heat load demand based on the cleaned data, and performing standardization processing on the selected feature data;

[0023] S23. Based on the standardized real-time climate condition data and the building heat energy usage data at the previous moment, a heat load demand prediction model is used to predict the initial heat energy required by the building at the current moment.

[0024] Preferably, the method of using a machine learning algorithm to predict and evaluate the thermal inertia of the current building, and optimizing and adjusting the initial thermal energy according to the thermal inertia of the building to obtain the adjusted required thermal energy comprises the following steps:

[0025] S31. Build a thermal inertia prediction model based on historical data and optimize the thermal inertia prediction model using quantum genetic algorithm;

[0026] S32, using the optimized thermal inertia prediction model and combining characteristic parameters of the current building to predict thermal inertia data of the current building;

[0027] S33, based on immune optimization algorithm, combined with the thermal inertia data of the current building, real-time climate data and electricity price data, solve the optimal thermal energy required by the current building;

[0028] S34. Optimize and adjust the initial thermal energy according to the optimal thermal energy required by the current building to obtain the thermal energy required by the current building.

[0029] Preferably, the step of constructing a thermal inertia prediction model based on historical data and optimizing the thermal inertia prediction model using a quantum genetic algorithm comprises the following steps:

[0030] S311, collecting historical characteristic parameters and corresponding thermal inertia data of the building and performing preprocessing, wherein the characteristic parameters include building material data, enclosure structure data and building orientation data;

[0031] S312, constructing a grey neural network model, and optimizing and adjusting the grey neural network model using a quantum genetic algorithm to obtain an optimized grey neural network model;

[0032] S313, using the pre-processed historical characteristic parameters of the building and the corresponding thermal inertia data to train the optimized grey neural network model to obtain a trained grey neural network model.

[0033] Preferably, the step of constructing a grey neural network model and optimizing and adjusting the grey neural network model using a quantum genetic algorithm to obtain an optimized grey neural network model comprises the following steps:

[0034] S3121. According to the number of input and output variables, design the hierarchical structure of the grey neural network, the number of neurons in each layer, the number and size of hidden layers, and construct a grey neural network model;

[0035] S3122. Randomly initialize the parameters of the grey neural network, including weights and thresholds, and determine the population size, number of quantum bits, and rotation angle of the quantum genetic algorithm;

[0036] S3123, using quantum bits to encode the parameters of the gray neural network, and defining the mean square error as a fitness function, wherein the probability amplitude of each quantum bit represents a potential value of a parameter;

[0037] S3124. Randomly generate an initial population, wherein each individual in the population represents a set of parameters of a gray neural network, and the quantum bit encoding of each individual represents a set of parameter values;

[0038] S3125. Based on the fitness function, perform fitness evaluation on each individual in the initial population, and record the fitness value and corresponding parameters of each individual;

[0039] S3126. Select excellent individuals for reproduction according to the fitness value, perform crossover operation on the selected individuals to generate new individuals, and perform quantum revolving door operation on the new individuals to achieve mutation;

[0040] S3127, evaluate the fitness of each individual in the new population, and update the best individual and the best fitness value;

[0041] S3128, determine whether the preset number of iterations or fitness threshold is reached, if not, return to step S3126, if yes, execute the next step;

[0042] S3129. Obtain optimal parameters based on the final population, and reconstruct the grey neural network model using the optimal parameters obtained by the quantum genetic algorithm to obtain an optimized grey neural network model.

[0043] As a preferred embodiment, the expression for performing quantum rotating door operation on the new individual to achieve mutation is:

[0044]

[0045] In the formula, α i and β i Represent the two complex coefficients of the original quantum bit state; θ irepresents the rotation angle associated with the i-th quantum bit; α i ′ and β i ′ represent the two complex coefficients of the new quantum bit state after the quantum gate operation.

[0046] Preferably, the method of solving the optimal heat energy required by the current building based on the immune optimization algorithm and combining the thermal inertia data, real-time climate data and electricity price data of the current building includes the following steps:

[0047] S331, obtaining thermal inertia data, real-time climate data and electricity price data of the current building, and performing cleaning, standardization and normalization processing;

[0048] S332. Based on the thermal inertia of the building, real-time climate and electricity price data, define minimizing energy consumption costs as the optimization goal, and use the comfort requirements inside the building, energy usage restrictions and electricity price fluctuations as constraints;

[0049] S333, encoding the heat energy supply scheme into antibodies, each antibody representing a heat energy allocation strategy, and designing an antibody affinity evaluation function for evaluating the quality of the heat energy allocation strategy according to the optimization goal and constraints;

[0050] S334. Design a clonal mutation operator for generating new antibodies and a concentration suppression operator for maintaining population diversity, and select antibodies with high affinity and low concentration;

[0051] S335, randomly generating a preset number of antibodies to form an initial population;

[0052] S336. Clone and mutate antibodies in the population to produce new antibodies;

[0053] S337, calculating the affinity of the newly generated antibodies using an antibody affinity evaluation function, and selecting a new generation population based on the affinity and concentration;

[0054] S338. Determine whether the predetermined number of iterations, affinity threshold or time limit is reached. If not, return to step S336. If so, output the antibody with the highest affinity to obtain the optimal heat energy allocation strategy, and use the result of the optimal heat energy allocation strategy as the optimal heat energy required for the current building.

[0055] Preferably, the antibody affinity evaluation function is expressed as:

[0056] F=ω 1 ·(C·ΣE i )+ω 2 ·[f(T avg ,H avg )]+ω 3 ·max[0,(Emax -∑E i )]

[0057] Where, F represents the value of the antibody affinity evaluation function; ω 1 ,ω 2 ,ω 3 They represent energy cost, comfort index, and weight of constraint conditions respectively; C represents unit energy cost; E i represents the energy consumption in the i-th time period; f(T avg ,H avg ) represents the comfort index; T avg Indicates the average temperature; H avg Indicates average humidity; max(0,E max -∑E i ) represents the penalty term, E max Indicates the upper limit of energy usage.

[0058] Compared with the prior art, the present invention provides an optimized scheduling method for electric drive heat sources based on thermal inertia of buildings, which has the following beneficial effects:

[0059] (1) The present invention can collect and analyze real-time climate data, building thermal inertia data, and electricity price data, and use advanced algorithms and models to predict energy demand, optimize energy allocation strategies, and achieve optimal energy utilization. In this way, it can not only improve energy utilization efficiency and reduce energy consumption costs, but also reduce greenhouse gas emissions and contribute to environmental protection.

[0060] (2) The present invention can significantly reduce heating costs within the range permitted by room temperature. Through precise prediction and real-time adjustment, it can ensure indoor temperature comfort while making maximum use of heating during periods of low electricity prices. During peak electricity price periods, it can reduce energy demand by releasing the thermal inertia of the building, thereby optimizing energy consumption. This refined energy management helps reduce unnecessary energy waste, improve energy utilization efficiency, and thus reduce overall operating costs.

[0061] (3) The present invention can not only significantly improve energy utilization efficiency through accurate heat load prediction and dynamic thermal energy optimization and adjustment, but also respond to changes in climate conditions and fluctuations in electricity prices in real time, improve the flexibility and adaptability of energy management, and at the same time combine cost-benefit analysis to minimize energy consumption costs, thereby effectively reducing the energy consumption costs of buildings, improving energy utilization efficiency, and enhancing the environmental adaptability and intelligent management level of buildings, which is of great significance for promoting the sustainable development of the construction industry.

[0062] (4) The present invention optimizes the model by using a quantum genetic algorithm, thereby improving the prediction accuracy and generalization ability of the model, ensuring that accurate prediction results can be provided under different environments and conditions. In addition, by using an immune optimization algorithm in combination with thermal inertia data, real-time climate data and electricity price data to solve the optimal thermal energy required by the current building, complex constraints and multi-objective optimization problems can be effectively handled to ensure that the thermal energy allocation strategy with the lowest energy consumption cost is found under the constraints of satisfying the building's internal comfort, energy usage restrictions and electricity price fluctuations. This can not only improve energy utilization efficiency, but also reduce operating costs, while enhancing the robustness and adaptability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0064] Figure 1 The present invention is a flowchart of an electric drive heat source optimization scheduling method based on building thermal inertia according to an embodiment of the present invention. DETAILED DESCRIPTION

[0065] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0066] According to an embodiment of the present invention, a method for optimizing the scheduling of electric drive heat sources based on thermal inertia of a building is provided.

[0067] The present invention is further described with reference to the accompanying drawings and specific embodiments. Figure 1 As shown, according to one embodiment of the present invention, a method for optimizing the scheduling of electric drive heat sources based on thermal inertia of a building is provided, comprising the following steps:

[0068] S1. Use historical climate data and building thermal data to build a heat load demand forecasting model, and combine electricity price data and the operating cost of electric drive heat sources to build a cost-benefit analysis model;

[0069] The method of using historical climate data and building thermal energy data to build a heat load demand forecasting model and combining electricity price data and the operating cost of the electric drive heat source to build a cost-benefit analysis model includes the following steps:

[0070] S11. Based on the historical database, obtain the climate condition data (including temperature, humidity, wind speed, etc.) of the area where the building is located, the corresponding heat energy usage data (including indoor temperature records, heat energy consumption, etc.), the electricity price data of the same period (different time periods, such as changes in electricity prices during peak hours and valley hours) and the operating cost data of the electric drive heat source (including electricity charges, equipment depreciation, maintenance costs, etc.), and pre-process the obtained data;

[0071] Data preprocessing includes:

[0072] Data cleaning: Clean the collected data and remove invalid or abnormal data.

[0073] Data standardization: Standardize data from different sources into a unified format to ensure data consistency and comparability.

[0074] Feature engineering: Extracting features from raw data that are useful for predictive models.

[0075] S12, constructing a heat load demand prediction model, and using the pre-processed historical climate conditions and building heat energy usage data for training and verification to obtain a trained heat load demand prediction model;

[0076] Specifically, the heat load demand prediction model in this embodiment can be any one of linear regression, time series analysis, and machine learning models (such as random forest, gradient boosting machine, etc.).

[0077] S13. Construct a cost-benefit analysis model, and use the pre-processed historical electricity price data and the operating cost data of the electric drive heat source for training and verification to obtain a trained cost-benefit analysis model.

[0078] Specifically, the construction of the cost-benefit analysis model includes:

[0079] Define costs and benefits: Determine costs including electricity, equipment depreciation and maintenance costs, and benefits including energy savings and environmental benefits.

[0080] Establish cost function: Establish cost function based on electricity price, heat demand and equipment efficiency.

[0081] The cost function Y can include electricity costs, equipment depreciation and maintenance costs. Assuming E is the heat demand, p is the electricity price, t is the time, D is the equipment depreciation cost, M is the maintenance cost, and η is the equipment efficiency, the cost function can be expressed as:

[0082] Y(t)=[E(t) / η]×p(t)+D+M;

[0083] Establish benefit function: Establish benefit function based on indoor temperature fluctuation range and heat source efficiency.

[0084] The benefit function Z can include energy saving and environmental benefits. Assume that S is the amount of energy saving, S(t) = a×[E max -E(t)], V is the value of environmental benefits, V(t) = b × [T set -T actual (t)] 2 , T set is the set indoor temperature, T actual is the actual indoor temperature, then the benefit function can be expressed as:

[0085] Z(t)=a×[E max -E(t)]+b×[T set -T actual (t)] 2 +V(t);

[0086] In the formula, a is the value coefficient of energy saving, b is the value coefficient of environmental benefit, T actual (t) is the actual indoor temperature at time t.

[0087] In this embodiment, the relationship between the heat load demand prediction model and the cost-benefit analysis model is as follows:

[0088] The heat load demand forecast model and the cost-benefit analysis model are interdependent. The heat load forecast provides an estimate of energy demand, while the cost-benefit analysis provides an economic evaluation under different energy use strategies. For example, if the forecast model predicts that the demand for heat energy will be low during a certain period of time, the cost-benefit analysis model will recommend reducing energy use during this period to reduce costs. Conversely, if the forecast model predicts that the demand for heat energy will be high, the cost-benefit analysis model will recommend increasing energy reserves during periods of low electricity prices to reduce long-term costs.

[0089] S2. Based on the heat load demand prediction model, the initial heat energy required by the building at the current moment is predicted by combining the real-time climate condition data and the building heat energy usage data at the previous moment;

[0090] The method of predicting the initial heat energy required by the building at the current moment based on the heat load demand prediction model and combining the real-time climate condition data and the building heat energy usage data at the previous moment includes the following steps:

[0091] S21, collecting real-time climate condition data of the area where the current building is located and the thermal energy usage data of the building at the last moment;

[0092] S22, performing data cleaning on the collected data, selecting features related to the heat load demand based on the cleaned data, and performing standardization processing on the selected feature data;

[0093] S23. Based on the standardized real-time climate condition data and the building heat energy usage data at the previous moment, a heat load demand prediction model is used to predict the initial heat energy required by the building at the current moment.

[0094] S3. Use machine learning algorithms to predict and evaluate the thermal inertia of the current building, and optimize and adjust the initial thermal energy according to the thermal inertia of the building to obtain the adjusted required thermal energy;

[0095] Specifically, the thermal inertia of a building refers to the building's ability to resist temperature changes, that is, its thermal stability when absorbing or releasing heat. Thermal inertia has an important impact on the energy efficiency and indoor comfort of a building. The following are some important factors related to the thermal inertia of a building:

[0096] Building Materials: Thermal conductivity, specific heat capacity and density of building materials are key factors affecting thermal inertia. For example, materials such as concrete, brick and stone have high thermal inertia, while wood and lightweight materials have low thermal inertia.

[0097] Structural envelope: The thickness of a building’s walls, roof and floors, and the design and construction quality of its insulation all affect its thermal inertia.

[0098] Building orientation: Building orientation and window shading design can affect the absorption of solar radiation and therefore thermal inertia.

[0099] The method of using a machine learning algorithm to predict and evaluate the thermal inertia of the current building, and optimizing and adjusting the initial thermal energy according to the thermal inertia of the building to obtain the adjusted required thermal energy includes the following steps:

[0100] S31. Build a thermal inertia prediction model based on historical data and optimize the thermal inertia prediction model using quantum genetic algorithm;

[0101] Specifically, the method of constructing a thermal inertia prediction model based on historical data and optimizing the thermal inertia prediction model using a quantum genetic algorithm includes the following steps:

[0102] S311, collecting historical characteristic parameters and corresponding thermal inertia data of the building and performing preprocessing, wherein the characteristic parameters include building material data, enclosure structure data and building orientation data;

[0103] S312, constructing a grey neural network model, and optimizing and adjusting the grey neural network model using a quantum genetic algorithm to obtain an optimized grey neural network model; said constructing a grey neural network model, and optimizing and adjusting the grey neural network model using a quantum genetic algorithm to obtain an optimized grey neural network model comprises the following steps:

[0104] S3121. According to the number of input and output variables, design the hierarchical structure of the grey neural network, the number of neurons in each layer, the number and size of hidden layers, and construct a grey neural network model;

[0105] Grey Neural Network (GNN) is a model that combines grey system theory and neural network theory, and is used to deal with problems with partially known information and partially unknown information (i.e. grey information). When designing the hierarchical structure of a grey neural network, it is necessary to consider the number of input and output variables, the complexity of the problem, and the characteristics of the data. The following is a simplified step to guide how to design the hierarchical structure of a grey neural network:

[0106] 1) Determine the input and output variables

[0107] Input variables: Determine which variables are used as network inputs based on the specific situation of the problem. For example, in this embodiment, these variables are the real-time climate condition data of the area where the current building is located and the thermal energy usage data of the building at the last moment.

[0108] Output variables: Similarly, determine the variables that the network needs to predict or output. For example, in this embodiment, these variables are the initial thermal energy required by the building at the current moment.

[0109] 2) Design network hierarchy

[0110] Input layer: The number of neurons in the input layer is the same as the number of input variables.

[0111] Hidden layers: The number and size of hidden layers depends on the complexity of the problem. Generally, one or two hidden layers can handle most problems. The number of neurons in a hidden layer is usually between the input layer and the output layer.

[0112] Output layer: The number of neurons in the output layer is the same as the number of output variables.

[0113] 3) Determine the size of the hidden layer

[0114] Empirical formula: You can use empirical formulas to estimate the number of neurons in the hidden layer, such as Where N h is the number of neurons in the hidden layer, N i is the number of neurons in the input layer, N o is the number of neurons in the output layer, and m is a constant between 0 and 1.

[0115] Trial and error: By experimenting with different hidden layer sizes, the network architecture that provides the best performance is selected.

[0116] 4) Constructing grey neural network model

[0117] Gray Generator: A gray generator, such as the Accumulative Generator (AGO) or the Subtractive Generator (AGO), is introduced into the network to process the gray information.

[0118] Connection weights: Initialize the connection weights and thresholds of the network.

[0119] Train the network: Use the training data to train the network, adjust the weights and thresholds until the network performance meets the requirements.

[0120] S3122. Randomly initialize the parameters of the grey neural network, including weights and thresholds, and determine the population size, number of quantum bits, rotation angle and other parameters of the quantum genetic algorithm;

[0121] When designing a gray neural network and using a quantum genetic algorithm (QGA) for parameter optimization, it is necessary to initialize the network's weights and thresholds and determine the relevant parameters of the QGA. The following is a simplified step to guide how to set these parameters:

[0122] 1) Randomly initialize the parameters of the gray neural network

[0123] Weights and thresholds: Usually, the weights and thresholds of a neural network are randomly assigned small values ​​during initialization, usually between -1 and 1. For example, the weights can be initialized using a uniform distribution U(-1,1).

[0124] 2) Determine the parameters of the quantum genetic algorithm

[0125] Population size: The population size P usually depends on the complexity of the problem. A common starting point is to choose a small population, such as P = 20 to P = 50.

[0126] Number of qubits: N q should match the number of parameters of the gray neural network. For example, if the network has N w weights and N b threshold, then

[0127] N q =N w +N b .

[0128] Rotation Angle: The rotation angle θ controls the strength of the quantum rotation gate operation. A common practice is to use a small angle, such as θ∈[0.01,0.1] radians.

[0129] S3123, using quantum bits to encode the parameters of the gray neural network, and defining the mean square error as a fitness function, wherein the probability amplitude of each quantum bit represents a potential value of a parameter;

[0130] In the quantum genetic algorithm (QGA), when quantum bits are used to encode the parameters of the gray neural network, the probability amplitude of each quantum bit can represent the potential value of a parameter. In this case, the mean square error (MSE) is defined as the fitness function to evaluate the performance of the network parameters.

[0131] Suppose there is a gray neural network whose parameters include weights w i and threshold b j , where i = 1,…,N w and j = 1,…,N b The network has N training samples, y k is the true output of the kth sample, is the output of the network prediction.

[0132] In the quantum bit encoding, each parameter w i or b j The probability amplitude α of a quantum bit can be i or β j Indicates that, where α i ,β j is a complex number, satisfying |α i ∣ 2 +|β j ∣ 2 =1.

[0133] The expression of the fitness function can be defined as:

[0134]

[0135] in:

[0136] F(α,β) is the fitness function, which depends on the probability amplitudes α and β of all qubits.

[0137] ·y k is the true output of the kth sample.

[0138] · is the network prediction output for the kth sample, which can be calculated using the parameters encoded in the qubits.

[0139] Specifically for the parameters of quantum bit encoding, the network output It can be expressed as:

[0140]

[0141] Among them, f is the activation function of the network, x ki is the value of the i-th input in the k-th sample.

[0142] In practical applications, since the probability amplitudes of quantum bits are complex numbers, we need to convert them into real number parameters w i and b j To compute the network output, this typically involves a quantum measurement process in which probability amplitudes are converted into actual values.

[0143] S3124. Randomly generate an initial population, wherein each individual in the population represents a set of parameters of a gray neural network, and the quantum bit encoding of each individual represents a set of parameter values;

[0144] S3125. Based on the fitness function, perform fitness evaluation on each individual in the initial population, and record the fitness value and corresponding parameters of each individual;

[0145] S3126. Select excellent individuals for reproduction according to the fitness value (roulette, tournament selection, etc. may be used), perform crossover operation on the selected individuals to generate new individuals, and perform quantum revolving door operation on the new individuals to achieve mutation;

[0146] The expression for performing quantum rotating door operation on the new individual to achieve mutation is:

[0147]

[0148] In the formula, α i and β i Represent the two complex coefficients of the original quantum bit state; θ i represents the rotation angle associated with the i-th quantum bit; α i ′ and β i ′ represent the two complex coefficients of the new quantum bit state after the quantum gate operation.

[0149] S3127, evaluate the fitness of each individual in the new population, and update the best individual and the best fitness value;

[0150] S3128, determining whether a preset number of iterations (for example, the preset number of iterations is 100 to 500) or a fitness threshold (for example, the fitness threshold is 98% of the expected minimum cost) is reached, if not, returning to step S3126, if yes, executing the next step;

[0151] S3129. Obtain optimal parameters based on the final population, and reconstruct the grey neural network model using the optimal parameters obtained by the quantum genetic algorithm to obtain an optimized grey neural network model.

[0152] S313, using the pre-processed historical characteristic parameters of the building and the corresponding thermal inertia data to train the optimized grey neural network model to obtain a trained grey neural network model.

[0153] S32, using the optimized thermal inertia prediction model and combining characteristic parameters of the current building to predict thermal inertia data of the current building;

[0154] S33, based on immune optimization algorithm, combined with the thermal inertia data of the current building, real-time climate data and electricity price data, solve the optimal thermal energy required by the current building;

[0155] Specifically, the method of solving the optimal heat energy required by the current building based on the immune optimization algorithm and combining the thermal inertia data of the current building, the real-time climate data and the electricity price data includes the following steps:

[0156] S331, obtaining thermal inertia data, real-time climate data and electricity price data of the current building, and performing cleaning, standardization and normalization processing;

[0157] S332. Based on the thermal inertia of the building, real-time climate and electricity price data, define minimizing energy consumption costs as the optimization goal, and use the comfort requirements inside the building, energy usage restrictions and electricity price fluctuations as constraints;

[0158] Specifically, based on the thermal inertia of the building, real-time climate and electricity price data, the following optimization objectives and constraints are defined:

[0159] Optimization goal: Minimize energy consumption costs

[0160] The optimization objective can be expressed as minimizing the total energy cost, which is usually related to the electricity bill. t represents the energy consumption (e.g., electrical energy) at time t, p t represents the electricity price at time t, T is the total length of time considered (e.g., the number of hours in a day or the number of days in a week), then the optimization objective can be expressed as:

[0161]

[0162] Constraints:

[0163] 1) Building interior comfort requirements:

[0164] Assume T min and T max are the minimum and maximum temperatures allowed inside the building, T i (t) is the temperature of the i-th room at time t, then the comfort constraint can be expressed as:

[0165]

[0166] 2) Energy usage restrictions:

[0167] Assumption E max is the maximum energy consumption allowed at any time t, then the energy usage limit can be expressed as:

[0168]

[0169] 3) Electricity price fluctuations:

[0170] Electricity price fluctuations are usually out of our control, but we need to take it into account during the optimization process. This condition is usually not directly expressed in mathematical form, but by using actual electricity price data p t to affect the optimization goal.

[0171] 4) Thermal inertia constraints:

[0172] Assuming that τ is the thermal inertia parameter of the building and ΔT(t) is the indoor and outdoor temperature difference at time t, the thermal inertia constraint can be expressed as:

[0173]

[0174] This means that the indoor temperature cannot vary more than the thermal inertia of the building allows.

[0175] 5) Other constraints:

[0176] According to actual conditions, other constraints may also exist in this embodiment, such as operation restrictions of equipment, maintenance plans, etc.

[0177] S333, encoding the heat energy supply scheme into antibodies, each antibody representing a heat energy allocation strategy, and designing an antibody affinity evaluation function for evaluating the quality of the heat energy allocation strategy according to the optimization goal and constraints;

[0178] The expression of the antibody affinity evaluation function is:

[0179] F=ω 1 ·(C·ΣE i )+ω 2 ·[f(T avg ,H avg )]+ω 3 ·max[0,(E max -∑E i )]

[0180] Where, F represents the value of the antibody affinity evaluation function; ω 1 ,ω 2 ,ω 3They represent energy cost, comfort index, and weight of constraint conditions respectively; C represents unit energy cost; E i represents the energy consumption in the i-th time period; f(T avg ,H avg ) represents the comfort index; T avg Indicates the average temperature; H avg Indicates average humidity; max(0,E max -∑E i ) represents the penalty term, E max Indicates the upper limit of energy usage.

[0181] S334. Design clonal mutation operators for generating new antibodies, such as exchanging certain elements in the encoding, and design concentration suppression operators for maintaining population diversity, selecting antibodies with high affinity and low concentration;

[0182] S335, randomly generating a preset number of antibodies to form an initial population;

[0183] S336. Clone and mutate antibodies in the population to produce new antibodies;

[0184] S337, calculating the affinity of the newly generated antibodies using an antibody affinity evaluation function, and selecting a new generation population based on the affinity and concentration;

[0185] Specifically, designing a clonal mutation operator for generating new antibodies and a concentration suppression operator for maintaining population diversity are key steps in the immune algorithm (IA). The immune algorithm is a search algorithm that simulates the biological immune system and is used to solve optimization problems. Here are the steps for designing these operators:

[0186] 1) Clone mutation operator

[0187] The clonal mutation operator is used to generate new antibodies by exchanging certain elements in the coding to simulate the antibody mutation process in the biological immune system.

[0188] Selection: Select antibodies with high affinity from the population for cloning.

[0189] Cloning: The selected antibody is cloned to produce multiple copies.

[0190] Mutation: Mutate the cloned antibody. Mutation can be random or targeted. For example, certain elements in the coding can be randomly selected to be swapped, or a specific strategy can be used to guide the mutation.

[0191] Re-evaluation: The mutated antibodies are re-evaluated to determine their affinity.

[0192] 2) Concentration suppression operator

[0193] The concentration suppression operator is used to maintain population diversity and prevent the algorithm from converging to a local optimal solution too early.

[0194] Calculate concentration: For each antibody in the population, calculate its concentration, which is the number of antibodies similar to that antibody.

[0195] Selection for inhibition: Selecting antibodies with high concentrations for inhibition. This can be done by random selection or based on a strategy (e.g. selecting the antibody with the highest concentration).

[0196] Replacement: Replace the suppressed antibodies with new antibodies, which can be generated randomly or through other operators such as crossover and mutation.

[0197] 3) Choose antibodies with high affinity and low concentration

[0198] When selecting new stocks, antibodies with high affinity and low concentration should be selected to ensure the diversity and search ability of the population.

[0199] Affinity calculation: The affinity of each antibody is calculated, which is usually done by evaluating the performance of the antibody in the problem domain.

[0200] Concentration Calculation: Calculate the concentration of each antibody.

[0201] Selection: Antibodies with high affinity and low concentration are selected to form a new population.

[0202] In practical applications, the specific implementation of these operators may be more complicated and need to be adjusted according to the characteristics of the specific problem. By simulating the mechanism of the biological immune system, the immune algorithm can effectively search for the optimal solution in a complex and dynamic environment.

[0203] S338. Determine whether the predetermined number of iterations (for example, 1000-1500 iterations), affinity threshold (for example, affinity threshold is 90% of the optimal solution) or time limit (for example, 2-5 hours) is reached. If not, return to step S336. If so, output the antibody with the highest affinity to obtain the optimal heat energy allocation strategy, and use the result of the optimal heat energy allocation strategy as the optimal heat energy required for the current building.

[0204] S34. Optimize and adjust the initial thermal energy according to the optimal thermal energy required by the current building to obtain the thermal energy required by the current building.

[0205] Specifically, the initial heat energy is replaced by the optimal heat energy currently required by the building to obtain the heat energy currently required by the building.

[0206] S4. Use a cost-benefit analysis model to evaluate the cost of meeting the required thermal energy after adjustment during different electricity price periods. Based on the fluctuation range of indoor temperature, dynamically adjust the scheduling strategy of the electric drive heat source in combination with the evaluation results.

[0207] For example, while meeting the comfort range accepted by users, the room temperature is allowed to fluctuate within ±1°C. The cost-benefit analysis model is used to evaluate the cost of meeting the adjusted thermal energy requirements during different electricity price periods. During periods of low electricity prices, heating is carried out in advance to increase the indoor temperature and use the thermal inertia of the building to store thermal energy. During periods of high electricity prices, the operation of heat sources is reduced, the indoor temperature is lowered, and the thermal inertia of the building is used to release thermal energy to reduce operating costs.

[0208] To sum up, with the help of the above-mentioned technical scheme of the present invention, the present invention can collect and analyze real-time climate data, thermal inertia data of buildings and electricity price data, and use advanced algorithms and models to predict energy demand, optimize energy allocation strategies, and achieve optimal utilization of energy. In this way, not only can energy utilization efficiency be improved and energy consumption costs be reduced, but also greenhouse gas emissions can be reduced, thus contributing to environmental protection.

[0209] In addition, the present invention can significantly reduce heating costs within the range permitted by room temperature. Through precise prediction and real-time adjustment, it can ensure indoor temperature comfort while maximizing the use of heating during periods of low electricity prices. During periods of peak electricity prices, it can reduce energy demand by releasing the thermal inertia of the building, thereby optimizing energy consumption. This refined energy management helps to reduce unnecessary energy waste, improve energy utilization efficiency, and thus reduce overall operating costs.

[0210] In addition, the present invention can not only significantly improve energy utilization efficiency through accurate heat load prediction and dynamic thermal energy optimization adjustment, but also respond to changes in climate conditions and electricity price fluctuations in real time, improve the flexibility and adaptability of energy management, and at the same time combine cost-benefit analysis to minimize energy consumption costs, thereby effectively reducing the energy consumption costs of buildings, improving energy utilization efficiency, and enhancing the environmental adaptability and intelligent management level of buildings, which is of great significance for promoting the sustainable development of the construction industry.

[0211] In addition, the present invention optimizes the model by using a quantum genetic algorithm, thereby improving the prediction accuracy and generalization ability of the model, ensuring that accurate prediction results can be provided under different environments and conditions. In addition, by using an immune optimization algorithm in combination with thermal inertia data, real-time climate data and electricity price data to solve the optimal thermal energy required by the current building, complex constraints and multi-objective optimization problems can be effectively handled to ensure that the thermal energy allocation strategy with the lowest energy consumption cost is found under the constraints of satisfying the building's internal comfort, energy usage restrictions and electricity price fluctuations, thereby not only improving energy utilization efficiency, but also reducing operating costs, while enhancing the robustness and adaptability of the system.

[0212] The technical features of the above-mentioned embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification. A person of ordinary skill in the art can understand that all or part of the steps in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a program. The program can be stored in a computer-readable storage medium. When the program is executed, it includes the steps described in the above method, and the storage medium, such as ROM / RAM, a disk, an optical disk, etc.

[0213] The above-mentioned embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A method for optimizing the scheduling of electric drive heat sources based on thermal inertia of buildings, characterized in that: The following steps are involved: S1. Use historical climate data and building thermal data to build a heat load demand forecasting model, and combine electricity price data and the operating cost of electric drive heat sources to build a cost-benefit analysis model; S2. Based on the heat load demand prediction model, the initial heat energy required by the building at the current moment is predicted by combining the real-time climate condition data and the building heat energy usage data at the previous moment; S3. Use machine learning algorithms to predict and evaluate the thermal inertia of the current building, and optimize and adjust the initial thermal energy according to the thermal inertia of the building to obtain the adjusted required thermal energy; S4. Use a cost-benefit analysis model to evaluate the cost of meeting the required thermal energy after adjustment during different electricity price periods. Based on the fluctuation range of indoor temperature, dynamically adjust the scheduling strategy of the electric drive heat source in combination with the evaluation results.

2. The method for optimizing the scheduling of electric drive heat sources based on thermal inertia of buildings according to claim 1 is characterized in that: The method of using historical climate data and building thermal energy data to construct a heat load demand forecasting model, and combining electricity price data and the operating cost of an electric drive heat source to construct a cost-benefit analysis model includes the following steps: S11. Based on the historical database, obtain the climate condition data of the area where the building is located, the corresponding heat energy usage data, the electricity price data of the same period, and the operating cost data of the electric drive heat source, and pre-process the obtained data; S12, constructing a heat load demand prediction model, and using the pre-processed historical climate conditions and building heat energy usage data for training and verification to obtain a trained heat load demand prediction model; S13. Construct a cost-benefit analysis model, and use the pre-processed historical electricity price data and the operating cost data of the electric drive heat source for training and verification to obtain a trained cost-benefit analysis model.

3. The method for optimizing the scheduling of electric drive heat sources based on thermal inertia of buildings according to claim 1 is characterized in that: The construction of the cost-benefit analysis model includes: Define costs and benefits: Determine costs including electricity, equipment depreciation and maintenance costs, and benefits including energy savings and environmental benefits; Establish cost function: Establish cost function based on electricity price, heat demand and equipment efficiency; Establish benefit function: Establish benefit function based on indoor temperature fluctuation range and heat source efficiency.

4. The method for optimizing the scheduling of electric drive heat sources based on thermal inertia of buildings according to claim 1 is characterized in that: The method of predicting the initial heat energy required by the building at the current moment based on the heat load demand prediction model and combining the real-time climate condition data and the building heat energy usage data at the previous moment comprises the following steps: S21, collecting real-time climate condition data of the area where the current building is located and the thermal energy usage data of the building at the last moment; S22, performing data cleaning on the collected data, selecting features related to the heat load demand based on the cleaned data, and performing standardization processing on the selected feature data; S23. Based on the standardized real-time climate condition data and the building heat energy usage data at the previous moment, a heat load demand prediction model is used to predict the initial heat energy required by the building at the current moment.

5. The method for optimizing the scheduling of electric drive heat sources based on thermal inertia of buildings according to claim 1, characterized in that: The method of using a machine learning algorithm to predict and evaluate the thermal inertia of the current building, and optimizing and adjusting the initial thermal energy according to the thermal inertia of the building to obtain the adjusted required thermal energy includes the following steps: S31. Build a thermal inertia prediction model based on historical data and optimize the thermal inertia prediction model using quantum genetic algorithm; S32, using the optimized thermal inertia prediction model and combining characteristic parameters of the current building to predict thermal inertia data of the current building; S33, based on immune optimization algorithm, combined with the thermal inertia data of the current building, real-time climate data and electricity price data, solve the optimal thermal energy required by the current building; S34. Optimize and adjust the initial thermal energy according to the optimal thermal energy required by the current building to obtain the thermal energy required by the current building.

6. The method for optimizing the scheduling of electric drive heat sources based on thermal inertia of buildings according to claim 5, characterized in that: The method of constructing a thermal inertia prediction model based on historical data and optimizing the thermal inertia prediction model using a quantum genetic algorithm comprises the following steps: S311, collecting historical characteristic parameters and corresponding thermal inertia data of the building and performing preprocessing, wherein the characteristic parameters include building material data, enclosure structure data and building orientation data; S312, constructing a grey neural network model, and optimizing and adjusting the grey neural network model using a quantum genetic algorithm to obtain an optimized grey neural network model; S313, using the pre-processed historical characteristic parameters of the building and the corresponding thermal inertia data to train the optimized grey neural network model to obtain a trained grey neural network model.

7. The method for optimizing the scheduling of electric drive heat sources based on thermal inertia of buildings according to claim 6 is characterized in that: The grey neural network model is constructed and optimized by using a quantum genetic algorithm to obtain the optimized grey neural network model, which includes the following steps: S3121. According to the number of input and output variables, design the hierarchical structure of the grey neural network, the number of neurons in each layer, the number and size of hidden layers, and construct a grey neural network model; S3122. Randomly initialize the parameters of the grey neural network, including weights and thresholds, and determine the population size, number of quantum bits, and rotation angle of the quantum genetic algorithm; S3123, using quantum bits to encode the parameters of the gray neural network, and defining the mean square error as a fitness function, wherein the probability amplitude of each quantum bit represents a potential value of a parameter; S3124. Randomly generate an initial population, wherein each individual in the population represents a set of parameters of a gray neural network, and the quantum bit encoding of each individual represents a set of parameter values; S3125. Based on the fitness function, perform fitness evaluation on each individual in the initial population, and record the fitness value and corresponding parameters of each individual; S3126. Select excellent individuals for reproduction according to the fitness value, perform crossover operation on the selected individuals to generate new individuals, and perform quantum revolving door operation on the new individuals to achieve mutation; S3127, evaluate the fitness of each individual in the new population, and update the best individual and the best fitness value; S3128, determine whether the preset number of iterations or fitness threshold is reached, if not, return to step S3126, if yes, execute the next step; S3129. Obtain optimal parameters based on the final population, and reconstruct the grey neural network model using the optimal parameters obtained by the quantum genetic algorithm to obtain an optimized grey neural network model.

8. The method for optimizing the scheduling of electric drive heat sources based on thermal inertia of buildings according to claim 7, characterized in that: The expression for performing quantum rotating door operation on the new individual to achieve mutation is: In the formula, α i and β i The two complex coefficients representing the original quantum bit state respectively; θ i represents the rotation angle associated with the i-th quantum bit; α i ′ and β i ′ represent the two complex coefficients of the new quantum bit state after the quantum gate operation.

9. The method for optimizing the scheduling of electric drive heat sources based on thermal inertia of buildings according to claim 5, characterized in that: The method of solving the optimal heat energy required by the current building based on the immune optimization algorithm and combining the thermal inertia data of the current building, the real-time climate data and the electricity price data includes the following steps: S331, obtaining thermal inertia data, real-time climate data and electricity price data of the current building, and performing cleaning, standardization and normalization processing; S332. Based on the thermal inertia of the building, real-time climate and electricity price data, define minimizing energy consumption costs as the optimization goal, and use the comfort requirements inside the building, energy usage restrictions and electricity price fluctuations as constraints; S333, encoding the heat energy supply scheme into antibodies, each antibody representing a heat energy allocation strategy, and designing an antibody affinity evaluation function for evaluating the quality of the heat energy allocation strategy according to the optimization goal and constraints; S334. Design a clonal mutation operator for generating new antibodies and a concentration suppression operator for maintaining population diversity, and select antibodies with high affinity and low concentration; S335, randomly generating a preset number of antibodies to form an initial population; S336. Clone and mutate antibodies in the population to produce new antibodies; S337, calculating the affinity of the newly generated antibodies using an antibody affinity evaluation function, and selecting a new generation population based on the affinity and concentration; S338. Determine whether the predetermined number of iterations, affinity threshold or time limit is reached. If not, return to step S336. If so, output the antibody with the highest affinity to obtain the optimal heat energy allocation strategy, and use the result of the optimal heat energy allocation strategy as the optimal heat energy required for the current building.

10. The method for optimizing the scheduling of electric drive heat sources based on thermal inertia of buildings according to claim 9, characterized in that: The expression of the antibody affinity evaluation function is: F=ω1·(C·∑E i )+ω2·[f(T avg ,H avg )]+ω3·max[0,(E max -∑E i )] In the formula, F represents the value of the antibody affinity evaluation function; ω1, ω2, and ω3 represent the weights of energy cost, comfort index, and constraint conditions, respectively; C represents unit energy cost; E i represents the energy consumption in the i-th time period; f(T avg ,H avg ) represents the comfort index; T avg represents the average temperature; H avg Indicates average humidity; max(0,E max -∑E i ) represents a penalty term; E max Indicates the upper limit of energy usage.

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