Building body flexibility regulation potential prediction method

Abstract

A building body flexible regulation potential prediction method, based on a back propagation neural network, establishes a building group energy consumption model, each energy consumption system sub-model takes the characteristic data of the influence parameters of the energy consumption of each energy consumption system as input, takes the maximum power saving rate of the corresponding energy consumption system as the first level optimization target, and outputs the energy consumption prediction value of each energy consumption system in different time periods; an adaptive grid search particle swarm method improved by distribution entropy is used, the maximum power saving rate of the building group is taken as the second level optimization target, and the minimum energy consumption prediction value of each energy consumption system in different time periods is determined; the extreme value of each energy consumption system and the deviation of the power saving rate of the building group are used to update the minimum energy consumption prediction value; the minimum energy consumption prediction value of each energy consumption system in different time periods is spliced in time sequence as the building group energy consumption prediction value; the difference between the actual value of the building group energy consumption and the building group energy consumption prediction value is taken as the proportion of the actual value of the building group energy consumption, as the prediction result of the building body flexible regulation potential, and accurate prediction is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy operation assessment of high-density building complexes, and in particular relates to a method for predicting the flexible regulation potential of a building body. Background Art

[0002] Flexible regulation of building energy consumption is of great significance for alleviating the tension of energy use during peak periods, stabilizing building power supply and initial investment in energy flexible regulation equipment.

[0003] Existing techniques for assessing building flexibility potential require segmenting and analyzing electricity consumption data for various systems, such as air conditioning systems, lighting and office systems, and power equipment systems. Based on these statistical results, the electricity or energy consumption characteristics of these systems are analyzed over time or from the perspective of outdoor climate to predict the building's flexibility potential. Conducting extensive statistical analysis for a project can lead to significant time and investment costs. Inappropriate statistical methods can also lead to biased analysis targets. Therefore, many methods use a small number of statistical results to develop mathematical models. These methods quantitatively analyze the electricity or energy consumption characteristics of different building systems, or optimize overall building electricity or energy consumption data to predict building flexibility potential. A generalized regression neural network (GRNN) method optimized with a particle swarm optimization algorithm (GA-PSO) based on a genetic algorithm was used to develop a mathematical model, improving the accuracy of the GRNN model. While highly accurate models can better quantify the impact of relevant factors on building flexibility, models developed using GRNN methods are not able to optimize building energy consumption or assess building flexibility potential. Existing assessment techniques focus on quantifying the impact of subjectively identified factors on electricity or energy consumption, ignoring the impact of the coupling of these factors on energy consumption characteristics. These influencing factors are not considered comprehensively, making it difficult to objectively identify the primary factors through comparison. Furthermore, when inappropriate modeling methods are used, the excessive number of influencing factors can easily lead to large errors in the mathematical model. Furthermore, existing techniques focus on integrating different electrical equipment systems or studying electricity or energy consumption characteristics across the building as a whole to predict the building's potential for flexible control. These techniques fail to simultaneously optimize electricity consumption across different systems and the building as a whole, limiting their practical applicability. Existing techniques propose orthogonalization experiments and artificial neural network models, employing standard particle swarm optimization (PSO) to optimize the research subjects. The core control parameters of the PSO method include the inertia weight and the learning factor. The learning factor is set to a constant value, while the inertia weight uses a relatively simple linear decreasing update strategy. This relatively simplistic approach can limit the method's search efficiency, resulting in slow convergence and poor solution quality. Instead, an adaptive inertia weight adjustment mechanism based on distributed entropy effectively balances the method's global exploration and local development capabilities, significantly improving its search accuracy and efficiency. Summary of the Invention

[0004] In order to solve the deficiencies in the prior art, the present invention provides a method for predicting the flexible regulation potential of a building body, which characterizes the flexible regulation potential of a building body from the energy consumption regulation amount of a building complex, and can obtain the control parameters when the power saving rate of each energy consumption system is the largest, so as to facilitate the reasonable regulation of different energy consumption systems at different times, thereby more accurately predicting the energy consumption of the building complex and realizing the efficient prediction of the flexible regulation potential of the building body.

[0005] The present invention adopts the following technical solutions.

[0006] The present invention proposes a method for predicting the flexible regulation potential of a building body. The building complex includes a building body and multiple energy consumption systems, including:

[0007] Obtain characteristic data of parameters influencing energy consumption of each energy consumption system;

[0008] A building complex energy consumption model is established based on a back-propagation neural network. The building complex energy consumption model includes sub-models of various energy consumption systems. Each energy consumption system sub-model takes the characteristic data of the influencing parameters of the energy consumption of each energy consumption system as input and takes the maximum power saving rate of the corresponding energy consumption system as the first-level optimization goal. Each energy consumption system sub-model outputs the energy consumption prediction value of each energy consumption system in different time periods;

[0009] An adaptive grid search particle swarm optimization method improved by distributed entropy is used. With the maximum energy saving rate of the building complex as the second-level optimization goal, the minimum energy consumption forecast value of each energy consumption system in different time periods is determined from the energy consumption forecast values ​​of each energy consumption system in different time periods. When determining the minimum energy consumption forecast value, the deviation between the extreme value of each energy consumption system and the energy saving rate of the building complex is used to update the minimum energy consumption forecast value.

[0010] The minimum energy consumption forecast values ​​of each energy consumption system in different time periods are spliced ​​together in time sequence as the energy consumption forecast value of the building complex; the proportion of the difference between the actual energy consumption value of the building complex and the predicted energy consumption value of the building complex in the actual energy consumption value of the building complex is used as the prediction result of the flexible regulation potential of the building itself.

[0011] Preferably, obtaining characteristic data of parameters influencing energy consumption of each energy consumption system includes:

[0012] The historical maximum and minimum values ​​of each influencing parameter are used to construct the historical value range of each influencing parameter;

[0013] The orthogonal experimental method is used to design a simulation scheme for the energy consumption system. Based on the simulation scheme, the maximum and minimum simulation values ​​of each influencing parameter are obtained to construct the simulation value range of each influencing parameter.

[0014] The union of the historical value range and the simulation value range of each influencing parameter is used as the value range of each influencing parameter, and a basic data set is established based on the value ranges of multiple influencing parameters;

[0015] A convolutional neural network is used to extract characteristic data of parameters affecting energy consumption of each energy consumption system from the basic data set.

[0016] Preferably, the influencing parameters of the energy consumption of each energy consumption system include: indoor temperature, indoor humidity, outdoor temperature, outdoor humidity, indoor wind speed, outdoor wind speed, water temperature, building density, illumination, floor height and power equipment pressure.

[0017] Preferably, in the input layer, the characteristic data of the parameters affecting the energy consumption of each energy consumption system are normalized;

[0018] Each energy consumption system sub-model also includes: a hidden layer, and the hidden layer of each energy consumption system sub-model adopts the tansig function;

[0019] The output values ​​of each layer of each energy consumption system sub-model are all within the range of [-1,1].

[0020] Preferably, the control parameters of each energy consumption system in different time periods are determined according to the energy consumption prediction values ​​of each energy consumption system in different time periods output by each energy consumption system sub-model, including indoor temperature, indoor humidity, indoor wind speed, water temperature and power equipment pressure.

[0021] Preferably, when the control parameter is not within the value range of the corresponding influencing parameter, the control parameter is added to the basic data set.

[0022] Preferably, an adaptive grid search particle swarm method improved by distributed entropy is adopted, with the maximum energy saving rate of the building complex as the second-level optimization goal, and the minimum energy consumption forecast value of each energy consumption system in different time periods is determined from the energy consumption forecast values ​​of each energy consumption system in different time periods, including:

[0023] The energy consumption prediction value of each energy consumption system in different time periods is used as the population particle; at the operating time t, any population particle Y(t) is between two population particles Y i (t) and Y j The projection of the distance vector g(t) between (t) forms a set z(t), which satisfies the following relationship:

[0024] z(t)=g(t) T Y(t)

[0025] At the running time t, the distance vector g(t) is divided into N intervals according to the population size, and the number of particle projections in the nth interval is counted, which is recorded as h n (t), where n = 1, 2, ..., N;

[0026] Calculate the population distribution entropy E(t) at the running time t, which satisfies the following relationship:

[0027]

[0028] Where s n (t) = h n (t)N;

[0029] Calculate the inertia weight w of the population distribution entropy at runtime t, which satisfies the following relationship:

[0030]

[0031] Calculate the first learning factor and the second learning factor to satisfy the following relationship:

[0032]

[0033] In the formula, c1 is the first learning factor, c 1,ini 、c 1,fin are the initial and final values ​​of the first learning factor c1, c2 is the second learning factor, c 2,ini 、c 2,fin are the initial and final values ​​of the second learning factor c1, K max is the maximum number of iterations, k is the current number of iterations;

[0034] On the basis of the adaptive grid search particle swarm method, based on the first learning factor and the second learning factor, the particle velocity determined by the inertia weight based on the population distribution entropy is linearly updated using the extreme value of the particle position deviation and the extreme value of the population position deviation, and the particle position is linearly updated using the updated particle velocity, respectively satisfying the following relationships:

[0035]

[0036] Where, is the velocity of the particle at the kth iteration, w is the inertia weight of the population distribution entropy, c1 is the first learning factor, c2 is the second learning factor, r1 and r2 are both random numbers distributed between [0,1]. is the extreme value of the particle position at the kth iteration, is the extreme value of the population position at the kth iteration, is the position of the particle at the kth iteration, and t is the running time;

[0037] The optimal energy consumption prediction value of each energy consumption system is determined based on the obtained particle position, and the power saving rate of the building group at the k-th iteration is calculated. When the error between the power saving rate of the building group at the k-th iteration and the power saving rate of the building group at the k-1-th iteration is not greater than the set threshold, the iteration is terminated. Otherwise, the first learning factor and the second learning factor are recalculated, and the particle position is updated.

[0038] Preferably, is the extreme value of particle position deviation, representing the extreme value of each energy consumption system; is the extreme value of population position deviation, representing the deviation of the energy saving rate of the building group; Characterize the minimum value of energy consumption prediction;

[0039] Based on the adaptive grid search particle swarm method, the first and second learning factors are used to linearly update the particle velocity determined by the inertia weight based on the population distribution entropy, using the extreme values ​​of each energy consumption system and the deviation of the building group's energy saving rate. The particle position is then linearly updated using the updated particle velocity to obtain the updated minimum energy consumption prediction value.

[0040] At the end of the iteration, the minimum energy consumption of each energy consumption system at different times under the building complex power saving rate and the control parameters corresponding to the minimum energy consumption are output.

[0041] Preferably, the threshold is set to no more than 0.1.

[0042] Preferably, the prediction result of the building's flexible control potential satisfies the following relationship:

[0043]

[0044] Where, E potential For the flexible control potential of the building itself, E pre is the actual energy consumption of the building complex, E opt is the predicted value of energy consumption of the building complex.

[0045] The beneficial effects of the present invention are as follows: compared with the prior art, the basic data set proposed in the present invention is not sample data that affects the parameters, but the allowable value range of the influencing data, which enables the basic data set to be adaptively oriented to different building complex operation scenarios; based on the method of linearly decreasing inertia weight, the inertia weight of the population distribution entropy is dynamically updated, which effectively balances the global exploration and local development capabilities of the method, thereby significantly improving the search accuracy and efficiency of the method. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 This is a flow chart of a method for predicting the flexible regulation potential of a building body proposed by the present invention. DETAILED DESCRIPTION

[0047] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. The embodiments described in this application are only part of the embodiments of the present invention, not all of them. Based on the spirit of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0048] The present invention proposes a method for predicting the flexible regulation potential of a building. A building complex includes a building complex and multiple energy consumption systems. The flexible regulation potential of the building complex is determined based on the energy consumption regulation characteristic of the building complex. In a non-limiting preferred embodiment, the energy consumption systems include but are not limited to air conditioning systems, water pump systems, heat storage systems, and cold storage systems. Figure 1 As shown, the method includes:

[0049] Step 1: Obtain characteristic data of parameters influencing energy consumption of each energy consumption system.

[0050] Specifically, step 1 includes:

[0051] Step 1.1, using the historical maximum and minimum values ​​of each influencing parameter to construct the historical value range of each influencing parameter;

[0052] In a non-limiting, preferred embodiment, to more comprehensively assess the potential for flexible energy regulation in high-density building complexes, the range of influencing parameters must encompass daily parameter variations. Parameters influencing energy consumption in each energy-consuming system include, but are not limited to, indoor temperature, indoor humidity, outdoor temperature, outdoor humidity, indoor wind speed, outdoor wind speed, water temperature, building density, illumination, floor height, and power equipment pressure. The range of each influencing parameter is constructed by selecting the historical maximum and minimum values ​​from the historical data for each influencing parameter. This ensures that the range covers as many possible scenarios as possible, paving the way for predicting the flexible regulation potential of the building itself.

[0053] Step 1.2: Design a simulation scheme for the energy consumption system using an orthogonal experimental method. Based on the simulation scheme, obtain the maximum and minimum simulation values ​​of each influencing parameter to construct a simulation value range for each influencing parameter.

[0054] In a non-limiting preferred embodiment, an orthogonal experimental method is used to design a basic data set covering the value ranges of multiple influencing parameters. Those skilled in the art can use other methods to obtain a basic data set covering the ranges of multiple influencing parameters.

[0055] In a non-limiting preferred embodiment, each scheme includes at least 11 parameters. The 11 parameters are arranged and combined according to traditional methods, and the number of simulation schemes involved is very large. The simulation scheme designed based on the orthogonal experimental method can not only cover the impact of the 11 parameters on energy consumption, but also greatly reduce the number of simulation schemes. In this embodiment, the simulation scheme is designed based on the orthogonal experimental method and implemented using SPSS software. The simulation maximum value and simulation minimum value of the influencing parameters are obtained through each simulation scheme to construct the simulation value range of each influencing parameter.

[0056] Step 1.3: Using the union of the historical value range and the simulation value range of each influencing parameter as the value range of each influencing parameter, a basic data set is established based on the value ranges of multiple influencing parameters;

[0057] The basic data set proposed in the present invention is not sample data that affects parameters, but the allowable value range of the data, which enables the basic data set to be adaptively oriented to different building complex operation scenarios.

[0058] In step 1.4, a convolutional neural network is used to extract characteristic data of the influencing parameters of the energy consumption of each energy consumption system from the basic data set.

[0059] Specifically, a feature extraction model is established based on the convolution kernel of the convolutional neural network, and the characteristic data corresponding to the influencing parameters of the energy consumption of each energy consumption system are extracted from the basic data set to reduce the impact of invalid information in the data on the performance of the energy consumption characteristic model, so as to avoid the complexity of the model established by the mathematical method due to the large number of parameters. By reducing the interference of the implicit information entropy of the data corresponding to the influencing parameters, the larger the characteristic value of the influencing parameter, the more effective information is covered.

[0060] Step 2: Establish a building complex energy consumption model based on the back propagation neural network. The building complex energy consumption model includes sub-models of various energy consumption systems. Each energy consumption system sub-model uses the same input layer, so that each energy consumption system sub-model uses the characteristic data of the influencing parameters of the energy consumption of each energy consumption system as input and the maximum power saving rate of the corresponding energy consumption system as the first-level optimization goal. Each energy consumption system sub-model outputs the energy consumption forecast value of each energy consumption system in different time periods.

[0061] The energy-saving rate of an energy-consuming system is a measure of its energy-saving effectiveness under specific conditions. The energy-saving rate is the ratio of the difference in energy consumption before and after energy conservation to the energy consumption before energy conservation. Maximizing the energy-saving rate of each energy-consuming system is the first-level optimization goal, and energy consumption is reduced locally. However, even when each energy-consuming system is at its lowest energy consumption, the building complex may not necessarily reach its lowest energy consumption. Therefore, it is necessary to further determine the minimum energy consumption forecast for the building complex based on the energy consumption forecast values ​​for each energy-consuming system at different time periods output by each energy-consuming system submodel.

[0062] Specifically, a building complex energy consumption model was established based on a backpropagation neural network (BPNN) to characterize the relationship between various influencing parameters and the predicted energy consumption values ​​of the corresponding energy-consuming systems. The building complex energy consumption model includes BPNN sub-models for the air conditioning system, the water pump system, the thermal storage system, and the cold storage system.

[0063] All BPNN sub-models are connected to the same input neuron, so each BPNN sub-model has the same input data, that is, the influencing parameters of each energy consumption system are used as input. Each BPNN sub-model takes the maximum power saving rate of the corresponding energy consumption system as the first-level optimization goal, and each sub-model outputs the energy consumption prediction value of each energy consumption system in different time periods.

[0064] Specifically, each energy consumption system sub-model uses the same input layer, in which the characteristic data of the influencing parameters of the energy consumption of each energy consumption system are normalized.

[0065] Each energy consumption system sub-model also includes: hidden layer and output layer; the building complex energy consumption model is as follows:

[0066] (a) Construct the hidden layer using the following relationship:

[0067]

[0068] Where H j is the transfer function of the jth hidden layer, w ij is the weight connecting the i-th input layer to the j-th hidden layer, e i,j is the feature data from the i-th input layer to the j-th hidden layer, b j is the threshold of the hidden layer, n is the number of input layers; s j is the tangent sigmoid function of the j-th hidden layer.

[0069] (b) Construct the output layer using the following relationship:

[0070] Y=s j w jk +b k (3)

[0071] Where Y is the output of the output layer, i.e., the energy consumption prediction value, w jk is the weight connecting the jth hidden layer to the kth output layer, b k is the threshold of the k-th output layer.

[0072] In the embodiment, the input data is normalized and then input into each BPNN sub-model at the same time. The hidden layer of each BPNN sub-model adopts the tansig function, and the output values ​​of each layer of each BPNN sub-model are within the range of [-1,1], so that the output results of each BPNN sub-model can be fused and spliced ​​in time sequence.

[0073] In the present invention, the influencing parameters of each energy consumption system are used as the input of each sub-model, and each sub-model is established based on the back-propagation neural network, which ensures the degree of coupling between the energy consumption prediction values ​​of each energy consumption system in different time periods output by each sub-model, making the prediction results more accurate and reliable.

[0074] In addition, based on the energy consumption forecast values ​​of each energy consumption system in different time periods output by each energy consumption system sub-model, the control parameters of each energy consumption system in different time periods are determined; among them, the control parameters are influencing parameters that can be adjusted, including indoor temperature, indoor humidity, indoor wind speed, water temperature and power equipment pressure.

[0075] When the control parameter is not within the value range of the corresponding influencing parameter, the control parameter is added to the basic data set.

[0076] Step 3: Adopting the adaptive grid search particle swarm method improved by distributed entropy, taking the maximum energy saving rate of the building complex as the second-level optimization goal, the minimum energy consumption forecast value of each energy consumption system in different time periods is determined from the energy consumption forecast values ​​of each energy consumption system in different time periods; wherein, when determining the minimum energy consumption forecast value, the deviation between the extreme value of each energy consumption system and the energy saving rate of the building complex is used to update the minimum energy consumption forecast value.

[0077] In step 2, energy consumption has been reduced locally. In order to achieve the lowest energy consumption for the building complex when each energy consumption system is at or close to the lowest energy consumption, that is, to balance global exploration and local development, the present invention proposes an adaptive grid search particle swarm method using improved distributed entropy, with the maximum energy saving rate of the building complex as the second-level optimization goal, and the minimum energy consumption forecast for each energy consumption system at different time periods is determined from the energy consumption forecast values ​​of each energy consumption system at different time periods. However, in the process of balancing global exploration and local development, the problem of sacrificing local resources cannot occur. For example, if any energy consumption system is large or even exceeds the energy consumption forecast value determined in step 2, but the energy consumption values ​​of the other energy consumption systems are extremely small, the energy consumption of the building complex is relatively low. However, at this time, the control parameters of each energy consumption system are not optimal parameters and need to be eliminated. Therefore, self-learning is also required for such local energy consumption extremes to eliminate interference.

[0078] Specifically, step 3 includes:

[0079] Step 3.1: The energy consumption prediction value of each energy consumption system in different time periods is used as the population particle; at the running time t, any population particle Y(t) is between two population particles Y i (t) and Y j The projection of the distance vector g(t) between (t) forms a set z(t), which satisfies the following relationship:

[0080] z(t)=g(t) T Y(t) (4)

[0081] Step 3.2, at running time t, divide the distance vector g(t) into N intervals according to the population size, and count the number of particle projections in the nth interval, denoted as h n (t), where n = 1, 2, ..., N;

[0082] Step 3.3, calculate the population distribution entropy E(t) at the running time t, which satisfies the following relationship:

[0083]

[0084] Where s n (t) = h n (t)N.

[0085] Population distribution entropy is a key metric for measuring the degree of dispersion of a particle swarm within the search space. In the initial stages of an adaptive grid search, the particles are widely dispersed, resulting in a high distribution entropy. This promotes the breadth of the global search and helps discover potential optimal solution regions. As the search progresses, particles gradually aggregate, and the distribution entropy decreases, allowing the method to focus on local, refined search and improving local development capabilities. Therefore, by dynamically adjusting the population distribution entropy, the method achieves contextual awareness based on the population distribution entropy, balancing the needs of global exploration and local development.

[0086] Therefore, it is necessary to further determine the minimum energy consumption forecast value of the building complex based on the energy consumption forecast values ​​of each energy consumption system in different time periods output by each energy consumption system sub-model.

[0087] Step 3.4, calculate the inertia weight w of the population distribution entropy at the running time t, which satisfies the following relationship:

[0088]

[0089] Step 3.5: Calculate the first learning factor and the second learning factor to satisfy the following relationship:

[0090]

[0091] In the formula, c1 is the first learning factor, c 1,ini 、c 1,finare the initial and final values ​​of the first learning factor c1, c2 is the second learning factor, c 2,ini 、c 2,fin are the initial and final values ​​of the second learning factor c1, K max is the maximum number of iterations, and k is the current number of iterations.

[0092] In step 3.6, based on the adaptive grid search particle swarm method, the particle velocity determined by the inertia weight based on the population distribution entropy is linearly updated based on the first learning factor and the second learning factor, using the extreme value of the particle position deviation and the extreme value of the population position deviation. The particle position is linearly updated using the updated particle velocity, satisfying the following relationships respectively:

[0093]

[0094] Where, is the velocity of the particle at the kth iteration, w is the inertia weight of the population distribution entropy, c1 is the first learning factor, c2 is the second learning factor, r1 and r2 are both random numbers distributed between [0,1]. is the extreme value of the particle position at the kth iteration, is the extreme value of the population position at the kth iteration, is the position of the particle at the kth iteration, and t is the running time;

[0095] In the embodiment, the running time t is set to 1.

[0096] is the extreme value of particle position deviation, representing the extreme value of each energy consumption system; is the extreme value of population position deviation, representing the deviation of the energy saving rate of the building group; Characterize the minimum value of energy consumption prediction; based on the adaptive grid search particle swarm method, based on the first learning factor and the second learning factor, using the extreme values ​​of each energy consumption system and the deviation of the power saving rate of the building group, the particle velocity determined by the inertia weight based on the population distribution entropy is linearly updated, and the particle position is linearly updated using the updated particle velocity to obtain the updated minimum value of energy consumption prediction.

[0097] The adaptive grid search particle swarm method improved based on distributed entropy retains the basic principle of the adaptive grid search particle swarm method. Through the asynchronous update of the first learning factor and the second learning factor in each iteration, the particle velocity update strategy is adjusted in real time according to the local energy consumption extreme value. This further enhances the adaptability of the method to complex problems, avoids the problem of sacrificing local conditions in the process of balancing global exploration and local development, and ultimately effectively guides the particle swarm to search for the optimal solution, eliminating the interference of local energy consumption extreme values.

[0098] In step 3.7, the optimal energy consumption prediction value of each energy consumption system is determined based on the particle position obtained in step 3.6, and the power saving rate of the building group at the kth iteration is calculated. When the error between the power saving rate of the building group at the kth iteration and the power saving rate of the building group at the k-1th iteration is not greater than the set threshold, the iteration is terminated, otherwise return to step 3.5.

[0099] In the embodiment, the threshold is set to be no more than 0.1.

[0100] In the embodiment, when the power saving rate of the building complex is the highest, through step 3, the control parameters corresponding to the minimum energy consumption and the minimum energy consumption of the air-conditioning system, the water pump system, the heat storage system and the cold storage system at different times are predicted, and used as the regulation parameters of each energy consumption system at different times to improve the convenience of operation.

[0101] Step 4: Concatenate the predicted optimal energy consumption values ​​of each energy consumption system in different time periods in chronological order as the predicted energy consumption value of the building complex; take the proportion of the difference between the actual energy consumption value of the building complex and the predicted energy consumption value of the building complex in the actual energy consumption value of the building complex as the estimation result of the flexible regulation potential of the building itself.

[0102] In the present invention, the energy consumption prediction value of the building complex is characterized by splicing the optimal energy consumption prediction value of the air-conditioning system, the optimal energy consumption prediction value of the water pump system, the optimal energy consumption prediction value of the heat storage system and the optimal energy consumption prediction value of the cold storage system in different time periods in time, thereby improving the degree of coupling between the optimal energy consumption prediction value of each energy consumption system and the energy consumption prediction value of the building complex.

[0103] The prediction results of the building's flexible regulation potential satisfy the following relationship:

[0104]

[0105] Where, E potential For the flexible control potential of the building itself, E pre is the actual energy consumption of the building complex, E opt is the predicted value of energy consumption of the building complex.

[0106] The present disclosure may be a system, method and / or computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0107] A computer-readable storage medium can be a tangible device that can hold and store instructions for use by an instruction execution device. A computer-readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination thereof. As used herein, a computer-readable storage medium is not to be construed as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through an electrical wire.

[0108] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions to be stored in the computer-readable storage medium in each computing / processing device.

[0109] The computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, and conventional procedural programming languages ​​such as "C" language or similar programming languages. Computer-readable program instructions may be executed entirely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., utilizing an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be personalized by utilizing the state information of the computer-readable program instructions. The electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0110] Finally, 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 above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A method for predicting the flexible regulation potential of a building, wherein a building complex includes a building and multiple energy consumption systems, characterized in that: include: Obtain characteristic data of parameters influencing energy consumption of each energy consumption system, including: The historical maximum and minimum values ​​of each influencing parameter are used to construct the historical value range of each influencing parameter; an orthogonal experimental method is used to design a simulation scheme for the energy consumption system, and based on the simulation scheme, the simulation maximum and minimum values ​​of each influencing parameter are obtained to construct the simulation value range of each influencing parameter; the union of the historical value range and the simulation value range of each influencing parameter is used as the value range of each influencing parameter, and a basic data set is established with the value ranges of multiple influencing parameters; a convolutional neural network is used to extract characteristic data of the influencing parameters of the energy consumption of each energy consumption system from the basic data set; wherein, the influencing parameters of the energy consumption of each energy consumption system include: indoor temperature, indoor humidity, outdoor temperature, outdoor humidity, indoor wind speed, outdoor wind speed, water temperature, building density, illumination, floor height, and power equipment pressure; A building complex energy consumption model is established based on a back-propagation neural network. The building complex energy consumption model includes sub-models of various energy consumption systems. Each energy consumption system sub-model takes the characteristic data of the influencing parameters of the energy consumption of each energy consumption system as input and takes the maximum power saving rate of the corresponding energy consumption system as the first-level optimization goal. Each energy consumption system sub-model outputs the energy consumption prediction value of each energy consumption system in different time periods; An adaptive grid search particle swarm optimization method improved by distributed entropy is used. With the maximum energy saving rate of the building complex as the second-level optimization goal, the minimum energy consumption forecast value of each energy consumption system in different time periods is determined from the energy consumption forecast values ​​of each energy consumption system in different time periods. When determining the minimum energy consumption forecast value, the deviation between the extreme value of each energy consumption system and the energy saving rate of the building complex is used to update the minimum energy consumption forecast value. The minimum energy consumption forecast values ​​of each energy consumption system in different time periods are spliced ​​together in time sequence as the energy consumption forecast value of the building complex; the proportion of the difference between the actual energy consumption value of the building complex and the predicted energy consumption value of the building complex in the actual energy consumption value of the building complex is used as the prediction result of the flexible regulation potential of the building itself.

2. The method for predicting the building's flexibility control potential according to claim 1, characterized in that: In the input layer, the characteristic data of the parameters affecting the energy consumption of each energy consumption system are normalized; Each energy consumption system sub-model also includes: a hidden layer, and the hidden layer of each energy consumption system sub-model adopts a tansig function; The output values ​​of each layer of each energy consumption system sub-model are all within the range of [-1,1].

3. The method for predicting the building's flexibility control potential according to claim 1, characterized in that: According to the energy consumption prediction values ​​of each energy consumption system in different time periods output by each energy consumption system sub-model, the control parameters of each energy consumption system in different time periods are determined, including indoor temperature, indoor humidity, indoor wind speed, water temperature and power equipment pressure.

4. The method for predicting the building's flexibility control potential according to claim 3, characterized in that: When the control parameter is not within the value range of the corresponding influencing parameter, the control parameter is added to the basic data set.

5. The method for predicting building body flexibility control potential according to claim 1, characterized in that: Adopting the adaptive grid search particle swarm method improved by distributed entropy, taking the maximum energy saving rate of the building complex as the second-level optimization goal, the minimum energy consumption forecast value of each energy consumption system in different time periods is determined from the energy consumption forecast values ​​of each energy consumption system in different time periods, including: The energy consumption prediction value of each energy consumption system in different time periods is used as the population particle; at the operating time t, any population particle Y(t) is between two population particles Y i (t) and Y j The projection of the distance vector g(t) between (t) forms a set z(t), which satisfies the following relationship: z(t)=g(t) T Y(t) At the running time t, the distance vector g(t) is divided into N intervals according to the population size, and the number of particle projections in the nth interval is counted, which is recorded as h n (t), where n = 1, 2, ..., N; Calculate the population distribution entropy E(t) at the running time t, which satisfies the following relationship: Where s n (t) = h n (t)N; Calculate the inertia weight w of the population distribution entropy at runtime t, which satisfies the following relationship: Calculate the first learning factor and the second learning factor to satisfy the following relationship: In the formula, c1 is the first learning factor, c 1,ini 、c 1,fin are the initial and final values ​​of the first learning factor c1, c2 is the second learning factor, c 2,ini 、c 2,fin are the initial and final values ​​of the second learning factor c1, K max is the maximum number of iterations, k is the current number of iterations; On the basis of the adaptive grid search particle swarm method, based on the first learning factor and the second learning factor, the particle velocity determined by the inertia weight based on the population distribution entropy is linearly updated using the extreme value of the particle position deviation and the extreme value of the population position deviation, and the particle position is linearly updated using the updated particle velocity, respectively satisfying the following relationships: Where, is the velocity of the particle at the kth iteration, w is the inertia weight of the population distribution entropy, c1 is the first learning factor, c2 is the second learning factor, r1 and r2 are both random numbers distributed between [0,1]. is the extreme value of the particle position at the kth iteration, is the extreme value of the population position at the kth iteration, is the position of the particle at the kth iteration, and t is the running time; The optimal energy consumption prediction value of each energy consumption system is determined based on the obtained particle position, and the power saving rate of the building group at the k-th iteration is calculated. When the error between the power saving rate of the building group at the k-th iteration and the power saving rate of the building group at the k-1-th iteration is not greater than the set threshold, the iteration is terminated. Otherwise, the first learning factor and the second learning factor are recalculated, and the particle position is updated.

6. The method for predicting the building's flexibility control potential according to claim 5, characterized in that: is the extreme value of particle position deviation, representing the extreme value of each energy consumption system; is the extreme value of population position deviation, representing the deviation of the energy saving rate of the building group; Characterize the minimum value of energy consumption prediction; Based on the adaptive grid search particle swarm method, the first and second learning factors are used to linearly update the particle velocity determined by the inertia weight based on the population distribution entropy, using the extreme values ​​of each energy consumption system and the deviation of the building group's energy saving rate. The particle position is then linearly updated using the updated particle velocity to obtain the updated minimum energy consumption prediction value.

7. The method for predicting building body flexibility control potential according to claim 5, characterized in that: Set the threshold to no more than 0.

1.

8. The method for predicting building body flexibility control potential according to claim 6, characterized in that: At the end of the iteration, the minimum energy consumption of each energy consumption system at different times under the building complex power saving rate and the control parameters corresponding to the minimum energy consumption are output.

9. The method for predicting building body flexibility control potential according to claim 1, characterized in that: The prediction results of the building's flexible regulation potential satisfy the following relationship: Where, E potential For the flexible control potential of the building itself, E pre is the actual energy consumption of the building complex, E opt is the predicted value of energy consumption of the building complex.

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