CN113048626A - Building energy consumption optimization method and device and readable storage medium - Google Patents
Building energy consumption optimization method and device and readable storage medium Download PDFInfo
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/50—Control or safety arrangements characterised by user interfaces or communication
- F24F11/54—Control or safety arrangements characterised by user interfaces or communication using one central controller connected to several sub-controllers
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
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Abstract
The application discloses a building energy consumption optimization method and device and a readable storage medium. The building energy consumption optimization method comprises the following steps: performing network simulation calculation on the indoor environment parameter, the outdoor environment parameter, the initial air-conditioning parameter and the current time parameter according to the first offset ReLU network model to obtain a room temperature sequence parameter; performing network prediction processing on the indoor environment parameter, the outdoor environment parameter, the initial air-conditioning parameter and the current time parameter according to the second offset ReLU network model to obtain an energy consumption parameter; solving the room temperature sequence parameter and the energy consumption parameter according to an optimization algorithm to obtain an optimal air conditioner control sequence; and controlling and adjusting the air conditioner according to the optimal air conditioner control sequence. According to the building energy consumption optimization method, the air conditioning system is regulated and controlled through the optimal air conditioning control sequence, so that the comfort level of the indoor environment can be adjusted through the air conditioning system, and meanwhile, the energy consumption of the air conditioning system is reduced.
Description
Technical Field
The application relates to the technical field of energy consumption optimization, in particular to a building energy consumption optimization method and device and a readable storage medium.
Background
With the development of economy, the requirements of people on the quality of life are also improved, especially the requirements on the comfort level of indoor environment.
However, in order to ensure comfort of the indoor environment, the state of the indoor environment is regulated by the air conditioning system, and a large amount of energy consumption is caused. Because the current air conditioning system can not be adjusted by itself according to environmental factors, a large amount of unnecessary energy loss is caused.
Disclosure of Invention
The present application is directed to solving at least one of the problems in the prior art. Therefore, the building energy consumption optimization method and device and the readable storage medium provided by the application issue the optimal air conditioner control sequences to the air conditioning system one by one, so that the comfort level of the indoor environment is adjusted through the air conditioning system, and meanwhile, the energy consumption of the air conditioning system is reduced.
The first aspect of the embodiments of the present application provides a method for optimizing building energy consumption, including:
acquiring indoor environment parameters, outdoor environment parameters and initial air-conditioning parameters;
performing network simulation calculation on the indoor environment parameter, the outdoor environment parameter, the initial air conditioner parameter and the current time parameter according to a first offset ReLU network model to obtain a room temperature sequence parameter;
performing network prediction processing on the indoor environment parameter, the outdoor environment parameter, the initial air-conditioning parameter and the current time parameter according to a second offset ReLU network model to obtain an energy consumption parameter;
solving the room temperature sequence parameter, the indoor environment parameter, the outdoor environment parameter and the energy consumption parameter according to an optimization algorithm to obtain an optimal air conditioner control sequence;
and controlling and adjusting the air conditioner according to the optimal air conditioner control sequence.
The method for optimizing the building energy consumption in the embodiment of the application has the following technical effects: and solving through an optimization algorithm to obtain an optimal air conditioner control sequence, and regulating and controlling the air conditioning system according to the optimal air conditioner control sequence so as to reduce the energy consumption of the air conditioning system while regulating the comfort level of the indoor environment through the air conditioning system.
In some embodiments, the indoor environmental parameter comprises a current indoor temperature, the outdoor environmental parameter comprises an outdoor temperature parameter, an outdoor humidity parameter, a solar radiance, and the initial air conditioning parameter comprises an air conditioning operating parameter.
In some embodiments, the first biased ReLU network model further comprises: an input layer for receiving initial data;
the first virtual layer is connected with the input layer and is used for carrying out weight matrix mapping and batch normalization processing on the initial data to obtain first mapping data;
the first hidden layer is connected with the first virtual layer and used for carrying out offset processing on the first mapping data to obtain first offset mapping data;
the second virtual layer is connected with the first hidden layer and is used for carrying out weight matrix mapping and batch normalization processing on the first offset mapping data to obtain second mapping data;
the cascade network layer group is connected with the second virtual layer and used for carrying out data processing on the second mapping data to obtain secondary bias mapping data;
the output layer is connected with the cascade network layer group and used for carrying out weighted summation on the secondary bias mapping data to obtain output data;
the cascade network layer group is composed of hidden layers and virtual layers which are alternately arranged, and the data processing at least comprises one of bias processing, weight matrix mapping and batch normalization processing.
In some embodiments, the input layer comprises a plurality of dimensions, the first virtual layer comprises a plurality of first virtual nodes;
wherein the number of dimensions corresponds to the number of first virtual nodes.
In some embodiments, the first hidden layer comprises a plurality of first hidden neurons;
the output of each of the first virtual nodes serves as the input of at least two of the first hidden neurons.
In some embodiments, the second virtual layer comprises a plurality of second virtual nodes; and the second virtual node is used for carrying out batch normalization processing on the input data.
In some embodiments, the second biased ReLU network model further comprises:
an input layer for receiving initial data;
the first virtual layer is connected with the input layer and is used for carrying out weight matrix mapping and batch normalization processing on the initial data to obtain first mapping data;
the first hidden layer is connected with the first virtual layer and used for carrying out offset processing on the first mapping data to obtain first offset mapping data;
the second virtual layer is connected with the first hidden layer and is used for carrying out weight matrix mapping and batch normalization processing on the first offset mapping data to obtain second mapping data;
the cascade network layer group is connected with the second virtual layer and used for carrying out data processing on the second mapping data to obtain secondary bias mapping data;
the output layer is connected with the cascade network layer group and used for carrying out weighted summation on the secondary bias mapping data to obtain output data;
the cascade network layer group is composed of hidden layers and virtual layers which are alternately arranged, and the data processing at least comprises one of bias processing, weight matrix mapping and batch normalization processing.
In some embodiments, the optimal air conditioning control sequence comprises a plurality of air conditioning control sub-signals, and the performing control adjustments on the air conditioner according to the optimal air conditioning control sequence comprises:
and sequentially sending the air conditioner control sub-signals to the air conditioning system so as to regulate and control the working state of the air conditioning system.
A second aspect of embodiments of the present application provides a computer-readable storage medium storing computer-executable instructions for: and executing the building energy consumption optimization method in any embodiment.
A third aspect of embodiments of the present application provides a computer-readable storage medium storing computer-executable instructions for: and executing the building energy consumption optimization method in any embodiment.
Additional aspects and advantages of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description.
Drawings
The present application is further described with reference to the following figures and examples, in which:
FIG. 1 is a flow chart of a method for optimizing building energy consumption according to an embodiment of the present disclosure;
FIG. 2 is a flowchart of a process for a first biased ReLU network model according to yet another embodiment of the present application;
FIG. 3 is a flowchart of a process for a first biased ReLU network model according to yet another embodiment of the present application;
FIG. 4 is an architecture diagram of an offset ReLU network model according to yet another embodiment of the present application;
FIG. 5 is a diagram illustrating an effect of an air conditioning setting sequence according to still another embodiment of the present application;
fig. 6 is an effect diagram of an air conditioner setting sequence according to still another embodiment of the present application.
Detailed Description
Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary only for the purpose of explaining the present application and are not to be construed as limiting the present application.
In the description of the present application, it is to be understood that the positional descriptions, such as the directions of up, down, front, rear, left, right, etc., referred to herein are based on the directions or positional relationships shown in the drawings, and are only for convenience of description and simplification of description, and do not indicate or imply that the referred device or element must have a specific direction, be constructed and operated in a specific direction, and thus, should not be construed as limiting the present application.
In the description of the present application, the meaning of a plurality is one or more, the meaning of a plurality is two or more, and the above, below, exceeding, etc. are understood as excluding the present number, and the above, below, within, etc. are understood as including the present number. If the first and second are described for the purpose of distinguishing technical features, they are not to be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated or implicitly indicating the precedence of the technical features indicated.
In the description of the present application, unless otherwise expressly limited, terms such as set, mounted, connected and the like should be construed broadly, and those skilled in the art can reasonably determine the specific meaning of the terms in the present application by combining the detailed contents of the technical solutions.
In the description of the present application, reference to the description of the terms "one embodiment," "some embodiments," "an illustrative embodiment," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
With the development of economy, the requirements of people on the quality of life are also improved, especially the requirements on the comfort level of indoor environment.
However, in order to ensure comfort of the indoor environment, the state of the indoor environment is regulated by the air conditioning system, and a large amount of energy consumption is caused. Because the current air conditioning system can not be adjusted by itself according to environmental factors, a large amount of unnecessary energy loss is caused.
Based on the problems, the building energy consumption optimization method is provided, the building system is accurately modeled by using the link hyperplane neural network model, and the model output is optimized by adopting a genetic algorithm, so that the optimization of the building energy consumption is realized.
Referring to fig. 1, in some embodiments, a method for optimizing building energy consumption includes: s100, acquiring indoor environment parameters, outdoor environment parameters and initial air conditioner parameters; s200, performing network simulation calculation on indoor environment parameters, outdoor environment parameters, initial air conditioner parameters and current time parameters according to the first offset ReLU network model to obtain room temperature sequence parameters; step S300, performing network prediction processing on the indoor environment parameter, the outdoor environment parameter, the initial air-conditioning parameter and the current time parameter according to the second offset ReLU network model to obtain an energy consumption parameter; s400, solving the room temperature sequence parameter, the indoor environment parameter, the outdoor environment parameter and the energy consumption parameter according to an optimization algorithm to obtain an optimal air conditioner control sequence; and S500, controlling and adjusting the air conditioner according to the optimal air conditioner control sequence.
And detecting the indoor environment and the outdoor environment through a preset sensor to obtain an indoor environment parameter, an outdoor environment parameter and an initial air conditioner parameter. And representing the corresponding building energy consumption state through the indoor environment parameters, the outdoor environment parameters and the initial air-conditioning parameters to construct a preliminary building energy consumption model. The optimization algorithm may be a genetic algorithm.
Further, the indoor environment parameter, the outdoor environment parameter, the initial air-conditioning parameter and the current time parameter are subjected to network simulation calculation through the first offset ReLU network model to obtain a room temperature value at the next moment, and cyclic iteration processing is performed according to the room temperature value at the next moment to obtain room temperature values corresponding to all the moments in the continuous time period, namely room temperature sequence parameters.
Meanwhile, the indoor environment parameter, the outdoor environment parameter, the initial air-conditioning parameter and the current time parameter are used as the input of a second offset ReLU network model, and the second offset ReLU network model carries out network prediction processing on input transmission to obtain the energy consumption parameter. Wherein the energy consumption parameter at least comprises energy consumption data at the current moment. And taking the energy consumption parameter as an optimized objective function, solving the room temperature sequence parameter and the energy consumption parameter through an optimization algorithm to obtain an optimal air conditioner control sequence for regulating and controlling the air conditioning system, and regulating and controlling the working state of the air conditioner according to the optimal air conditioner control sequence.
In some embodiments, the building energy consumption optimization method, the indoor environment parameter comprises a current indoor temperature, the outdoor environment parameter comprises an outdoor temperature parameter, an outdoor humidity parameter and a solar radiance, and the initial air conditioning parameter comprises an air conditioning operating parameter.
It can be understood that the current indoor temperature, the outdoor temperature parameter, the outdoor humidity parameter, the solar radiance, the air conditioner working parameter and the current time parameter are used as the input of the first offset ReLU network model and the second offset ReLU network model, and the corresponding room temperature sequence parameter and the optimal air conditioner control sequence are obtained. The room temperature change state in a period of time in the future is represented by the room temperature sequence parameters, and the energy consumption parameters are optimally solved by the optimal algorithm to obtain the optimal solution of the energy consumption parameters, so that the energy consumption of the air conditioning system is reduced while the comfort level of the indoor environment is effectively ensured.
Referring to fig. 2 and 4, in some embodiments, the first biased ReLU network model includes: an input layer for receiving initial data; the first virtual layer is connected with the input layer and is used for carrying out weight matrix mapping and batch normalization processing on the initial data to obtain first mapping data; the first hidden layer is connected with the first virtual layer and used for carrying out bias processing on the first mapping data to obtain first bias mapping data; the second virtual layer is connected with the first hidden layer and used for carrying out weight matrix mapping and batch normalization on the first bias mapping data to obtain second mapping data; the cascade network layer group is connected with the second virtual layer and used for carrying out bias processing on the second mapping data to obtain secondary bias mapping data; and the output layer is connected with the cascade network layer group and used for carrying out weighted summation on the secondary bias mapping data to obtain output data. The cascade network layer group is composed of hidden layers and virtual layers which are alternately arranged, and the data processing at least comprises one of bias processing, weight matrix mapping and batch normalization processing.
It is to be understood that the first biased ReLU network model includes multiple layers of neural network layers. The first layer of neural network layer is an input layer and is used for receiving initial data and mapping the initial data to the first virtual layer; the first virtual layer performs weight matrix mapping and batch normalization processing according to the output data of the input layer to output first mapping data; the first hidden layer carries out bias processing according to the output data of the first virtual layer to obtain first bias mapping data; the second virtual layer performs weight matrix mapping and batch normalization processing on the output data (first bias mapping data) of the first hidden layer to obtain second mapping data.
The cascade network layer group is formed by connecting a plurality of hidden layers and virtual layers, each hidden layer performs bias processing according to output data of a previous virtual layer to obtain bias mapping data, each virtual layer performs bias processing and batch normalization on the output of the previous hidden layer, and the obtained data is used as the input of a next neural network layer. And the output layer is used for weighting and summing the secondary mapping data of the cascaded network layer group to obtain corresponding output data.
In some embodiments, the input layer comprises a plurality of dimensions of input, the first virtual layer comprises a plurality of first virtual nodes, each first virtual node for batch normalization of the input data; the output of each dimension is at least used as the input of two first virtual nodes, and the output of each virtual layer node is at least used as the input of two first hidden neurons; wherein the dimension of the dimension corresponds to the number of first virtual nodes.
As shown, the input layer includes a plurality of dimensions for receiving an indoor environment parameter, an outdoor environment parameter, and an initial air conditioning parameter, respectively. Further, the input layer maps the received data to the first virtual layer respectively, and performs batch normalization processing to serve as input of the first virtual node.
The output of each virtual layer node serves as the input of at least two first hidden neurons. It can be understood that, according to the relationship among the indoor environment parameter, the outdoor environment parameter, the initial air-conditioning parameter, and the time parameter, the relevant parameter is mapped into the layer node of the first virtual layer to perform the batch normalization process.
In some embodiments, the first hidden layer comprises a plurality of dimensions of input; the output of each first virtual node serves as the input of at least two first hidden neurons.
Further, the first hidden layer includes a plurality of first hidden neurons; the output of each first virtual node serves as the input of at least two first hidden neurons. I.e. the first virtual layer inputs the first mapping data to at least two first hidden neurons. And the first hidden neurons in the first hidden layer perform bias processing on the plurality of first mapping data according to preset parameters to obtain first bias mapping data.
It is to be understood that the output of each virtual node in the first biased ReLU network model corresponds to the input of a plurality of neurons.
The input and output of the neurons in two adjacent neural network layers are mutually staggered, and each output of the neural network layer of the layer is connected with all the neurons of the next neural network layer.
In some embodiments, the second virtual layer comprises a plurality of second virtual nodes.
That is, the second mapping data output by the second virtual layer is obtained by mapping the weight matrix of the first hidden neurons of the previous level by the second virtual nodes.
Referring to fig. 3 and 4, in some embodiments, the second biased ReLU network model further includes: an input layer for receiving initial data; the first virtual layer is connected with the input layer and is used for carrying out weight matrix mapping on the initial data and carrying out batch normalization processing to obtain first mapping data; the first hidden layer is connected with the first virtual layer and used for carrying out bias processing on the first mapping data to obtain first bias mapping data; the second virtual layer is connected with the first hidden layer and used for carrying out weight matrix mapping and batch normalization on the first bias mapping data to obtain second mapping data; the cascade network layer group is connected with the second virtual layer and is used for carrying out bias processing on the second mapping data to obtain secondary bias mapping data; and the output layer is connected with the cascade network layer group and used for carrying out weighted summation on the secondary bias mapping data to obtain output data. The cascade network layer group is composed of hidden layers and virtual layers which are alternately arranged, and the data processing at least comprises one of bias processing, weight matrix mapping and batch normalization processing.
It is understood that the first biased ReLU network model and the second biased ReLU network model have similar network architectures. The first offset ReLU network model is used for carrying out network simulation calculation according to the indoor environment parameters, the outdoor environment parameters, the initial air-conditioning parameters and the current time parameters to obtain room temperature sequence parameters, and the second offset ReLU network model is used for carrying out network prediction according to the indoor environment parameters, the outdoor environment parameters, the initial air-conditioning parameters and the current time parameters to obtain energy consumption parameters.
Parameters or weights of the internal neurons are adjusted adaptively, so that the biased ReLU network model outputs different values.
And further, optimally solving the control signal of the air conditioning system according to the output of the first offset ReLU network model and the second offset ReLU network model, and obtaining an optimal air conditioning control sequence comprising a plurality of air conditioning control signals.
It is to be understood that the first biased ReLU network model includes multiple layers of neural network layers. The first layer of neural network layer is an input layer and is used for receiving initial data and mapping the initial data to the first virtual layer; the first virtual layer performs weight matrix mapping and batch normalization processing according to the output data of the input layer to output first mapping data; the first hidden layer carries out bias processing according to the output data of the first virtual layer to obtain first bias mapping data; the second virtual layer performs weight matrix mapping on output data (first bias mapping data) of the first hidden layer to obtain second mapping data.
The cascade network layer group is formed by connecting a plurality of virtual layers and hidden layers, each virtual layer carries out bias processing and normalization on the output of the previous neural network layer and uses the obtained data as the input of the next neural network layer. And the output layer is used for carrying out weighted summation on the secondary mapping data of the cascade network layer group to obtain a corresponding output parameter. In some embodiments, the optimal air conditioning control sequence includes a plurality of air conditioning control sub-signals, and the performing control adjustments on the air conditioner according to the optimal air conditioning control sequence includes: and sequentially sending the air conditioner control sub-signals to an air conditioning system so as to regulate and control the state parameters of the air conditioner.
And sending the air conditioner control sub-signals in the optimal air conditioner control sequence to the air conditioning system one by one so as to reduce the energy consumption of the air conditioning system while adjusting the comfort level of the indoor environment through the air conditioning system.
The air conditioner control sub-signal for controlling the working state of the air conditioning system is obtained according to the indoor environment parameter, the outdoor environment parameter and the initial air conditioning parameter, and the working state of the air conditioning system is adjusted in real time by sending the air conditioner control sub-signal, so that the requirements of indoor comfort and low energy consumption are met at the same time.
Referring to fig. 5 and 6, the solid line curve represents the outdoor temperature, and the dotted line curve represents the reference value of the air conditioner setting randomly set between 22 and 26 degrees. Fig. 5 is an air conditioner setting sequence effect diagram corresponding to an actually optimized air conditioner setting point, and fig. 6 is an air conditioner setting sequence effect diagram corresponding to a randomly set air conditioner setting point.
The comparison of the graphs shows that the air conditioning system is regulated and controlled by using the actually optimized air conditioning set point, and when the outdoor temperature is lower, the temperature of the air conditioning set point is lower, so that the pre-cooling effect is achieved; when the outdoor temperature rises, the temperature of the set point of the air conditioner gradually becomes higher to effectively reduce the energy consumption of the air conditioning system.
If the air conditioning system is regulated and controlled by using the randomly set air conditioner setting point, the temperature of the air conditioner is set to be a fixed value or a random value, and the technical effect of the technical scheme provided by the application cannot be achieved. The building energy consumption optimization method can save energy consumption by 4% -10%, and reduces modeling cost. Meanwhile, the accuracy of modeling can be guaranteed and the energy-saving effect can be achieved by carrying out a building model through the network prediction model.
In some embodiments, a computer-readable storage medium stores computer-executable instructions for: performing the building energy consumption optimization method of any one of the preceding embodiments.
In some embodiments, an apparatus, comprising: a processor; a memory having stored thereon a computer program operable on the processor; wherein the computer program when executed by the processor implements the steps of the building energy consumption optimization method as in any of the embodiments described above.
The embodiments of the present application have been described in detail with reference to the drawings, but the present application is not limited to the embodiments, and various changes can be made within the knowledge of those skilled in the art without departing from the gist of the present application. Furthermore, the embodiments and features of the embodiments of the present application may be combined with each other without conflict.
Claims (10)
1. The building energy consumption optimization method is characterized by comprising the following steps:
acquiring indoor environment parameters, outdoor environment parameters and initial air-conditioning parameters;
performing network simulation calculation on the indoor environment parameter, the outdoor environment parameter, the initial air conditioner parameter and the current time parameter according to a first offset ReLU network model to obtain a room temperature prediction sequence;
performing network prediction processing on the indoor environment parameter, the room temperature prediction sequence, the outdoor environment parameter, the initial air-conditioning parameter and the current time parameter according to a second offset ReLU network model to obtain an energy consumption parameter;
solving the room temperature sequence parameter, the indoor environment parameter, the outdoor environment parameter and the energy consumption parameter according to an optimization algorithm to obtain an optimal air conditioner control sequence;
and controlling and adjusting the air conditioning system according to the optimal air conditioning control sequence.
2. The method for optimizing building energy consumption according to claim 1, wherein the indoor environment parameters include current indoor temperature, the outdoor environment parameters include outdoor temperature parameters, outdoor humidity parameters, solar radiance, and the initial air conditioning parameters include air conditioning operating parameters.
3. The method for building energy consumption optimization according to claim 2, wherein the first biased ReLU network model comprises:
an input layer for receiving initial data;
the first virtual layer is connected with the input layer and is used for carrying out weight matrix mapping and batch normalization processing on the initial data to obtain first mapping data;
the first hidden layer is connected with the first virtual layer and used for carrying out offset processing on the first mapping data to obtain first offset mapping data;
the second virtual layer is connected with the first hidden layer and is used for carrying out weight matrix mapping and batch normalization processing on the first offset mapping data to obtain second mapping data;
the cascade network layer group is connected with the second virtual layer and used for carrying out data processing on the second mapping data to obtain secondary bias mapping data;
the output layer is connected with the cascade network layer group and used for carrying out weighted summation on the secondary bias mapping data to obtain output data;
the cascade network layer group is composed of hidden layers and virtual layers which are alternately arranged, and the data processing at least comprises one of bias processing, weight matrix mapping and batch normalization processing.
4. The building energy consumption optimization method according to claim 3, wherein the input layer comprises a plurality of dimensions, and the first virtual layer comprises a plurality of first virtual nodes;
wherein the number of dimensions corresponds to the number of first virtual nodes.
5. The building energy consumption optimization method according to claim 2, wherein the first hidden layer comprises a plurality of first hidden neurons;
the output of each of the first virtual nodes serves as the input of at least two of the first hidden neurons.
6. The building energy consumption optimization method according to claim 2, wherein the second virtual layer comprises a plurality of second virtual nodes, and the second virtual nodes are used for performing batch normalization processing on input data.
7. The method for building energy consumption optimization according to claim 2, wherein the second biased ReLU network model further comprises:
an input layer for receiving initial data;
the first virtual layer is connected with the input layer and is used for carrying out weight matrix mapping and batch normalization processing on the initial data to obtain first mapping data;
the first hidden layer is connected with the first virtual layer and used for carrying out offset processing on the first mapping data to obtain first offset mapping data;
the second virtual layer is connected with the first hidden layer and is used for carrying out weight matrix mapping and batch normalization processing on the first offset mapping data to obtain second mapping data;
the cascade network layer group is connected with the second virtual layer and used for carrying out data processing on the second mapping data to obtain secondary bias mapping data;
the output layer is connected with the cascade network layer group and used for carrying out weighted summation on the secondary bias mapping data to obtain output data;
the cascade network layer group is composed of hidden layers and virtual layers which are alternately arranged, and the data processing at least comprises one of bias processing, weight matrix mapping and batch normalization processing.
8. The method for optimizing building energy consumption according to claim 1, wherein the optimal air conditioning control sequence comprises a plurality of air conditioning control sub-signals, and the performing control adjustment on the air conditioning system according to the optimal air conditioning control sequence comprises:
and sequentially sending the air conditioner control sub-signals to the air conditioning system so as to regulate and control the working state of the air conditioning system.
9. A computer-readable storage medium storing computer-executable instructions for: performing the building energy consumption optimization method of any one of claims 1 to 8.
10. An apparatus, characterized in that it comprises: a processor; a memory having stored thereon a computer program operable on the processor; wherein the computer program, when executed by the processor, implements the steps of the building energy consumption optimization method according to any one of claims 1 to 8.
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| CN114841464A (en) * | 2022-05-25 | 2022-08-02 | 山东大卫国际建筑设计有限公司 | Building energy-saving management method, equipment and medium based on chimpanzee algorithm |
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| CN106196423A (en) * | 2016-06-30 | 2016-12-07 | 西安建筑科技大学 | A kind of Indoor Environmental Quality based on model prediction controls optimization method |
| CN109068138A (en) * | 2018-08-07 | 2018-12-21 | 北京市商汤科技开发有限公司 | Processing method and processing device, electronic equipment and the storage medium of video image |
| CN110986249A (en) * | 2019-11-07 | 2020-04-10 | 珠海格力电器股份有限公司 | Self-adjustment control method and system of air conditioner and air conditioner |
| CN111795484A (en) * | 2020-07-24 | 2020-10-20 | 北京大学深圳研究生院 | Intelligent air conditioner control method and system |
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| CN114841464A (en) * | 2022-05-25 | 2022-08-02 | 山东大卫国际建筑设计有限公司 | Building energy-saving management method, equipment and medium based on chimpanzee algorithm |
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