CN116796406A - Building energy consumption optimization method based on artificial neural network and BIM - Google Patents
Building energy consumption optimization method based on artificial neural network and BIM Download PDFInfo
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- CN116796406A CN116796406A CN202310741819.2A CN202310741819A CN116796406A CN 116796406 A CN116796406 A CN 116796406A CN 202310741819 A CN202310741819 A CN 202310741819A CN 116796406 A CN116796406 A CN 116796406A
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Abstract
The invention discloses a building energy consumption optimization method based on an artificial neural network and BIM, which relates to the technical field of buildings and comprises the following steps: s1, collecting data affecting building energy consumption; s2, calculating the acquired data to obtain a clustering center of building energy consumption data and attribution conditions of all pieces of data; and S3, taking the obtained clustering center of the energy consumption data as an implicit node of the artificial neural network, and calculating a constant matrix of the implicit node to obtain all information of an implicit layer of the artificial neural network. According to the building energy consumption optimization method based on the artificial neural network and the BIM, due to the self-adaptive characteristic of the clustering method, the clustering center number does not need to be designated in advance, so that the hidden layer node information of the RBF is determined by adopting an AP clustering algorithm, the effectiveness and feasibility of a model are improved, the building energy consumption data can be rapidly analyzed and predicted, and errors caused by different data dimensionalities and numerical ranges in different building energy consumption data sets are reduced.
Description
Technical Field
The invention relates to the technical field of buildings, in particular to a building energy consumption optimization method based on an artificial neural network and BIM.
Background
Along with the increasing of building energy consumption, the influence of carbon emission on the environment is more and more serious, so that the analysis and prediction of the building energy consumption are particularly important to realize energy conservation and emission reduction by reducing the building energy consumption.
The traditional building energy consumption simulation is mainly performed on energy consumption simulation software, the optimization analysis speed is low, and an effective prediction model is difficult to obtain during modeling, so that the scheme effect of energy consumption optimization is poor.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a building energy consumption optimization method based on an artificial neural network and BIM, which solves the problems that the traditional building energy consumption simulation is difficult to obtain an effective prediction model, so that the scheme effect of energy consumption optimization is poor.
In order to achieve the above purpose, the invention is realized by the following technical scheme: the building energy consumption optimization method based on the artificial neural network and the BIM comprises the following steps:
s1, collecting data affecting building energy consumption;
s2, calculating the acquired data to obtain a clustering center of building energy consumption data and attribution conditions of all pieces of data;
s3, taking the obtained clustering center of the energy consumption data as an implicit node of the artificial neural network, and calculating a constant matrix of the implicit node to obtain all information of an implicit layer of the artificial neural network;
s4, initializing a weight matrix for connecting an input layer and an hidden layer in the artificial neural network, inputting building energy consumption training data to train the network until the training iteration number reaches an upper limit or the training error is within an allowable range, ending the training, and finally obtaining a prediction model;
s5, inputting building energy consumption test data, and evaluating generalization capability of the obtained prediction model through predicting heat load and cold load;
s6, analyzing factors of the enclosure structure affecting the energy consumption of the building, and providing a building scheme for reducing the heat load and the cold load of the building so as to achieve the aim of reducing the carbon emission.
Preferably, in the step S1, the data affecting the energy consumption of the building includes relative compactness of the building, surface area of the building, wall area, roof, total height of the building, direction of the building, area of the glass window, and area distribution of the glass window.
Preferably, in the step S2, the clustering center and the attribution condition of each piece of data are obtained by adopting the following formula:
C i ＝argmin(x i -u j )
wherein x is input data, u is a cluster center, and for each, u j For each cluster center, for each cluster center u j And performing updating iteration until a final clustering result is obtained.
Preferably, in the step S3, a formula for calculating the constant matrix of the hidden node is as follows:
wherein f (x) is an activation function, A k Output value of kth hidden layer node, w 1 To connect the weight matrix of the input layer and the hidden layer, beta k Is the threshold for the hidden layer node.
Preferably, in the step S4, when training the building energy consumption training data on the network, the weight matrix w is adjusted by means of error back propagation, so as to find an optimal solution of the prediction model, improve the generalization ability of the model, and find an optimal solution according to the following formula:
wherein t is k Is the threshold value of the output layer node, w 2 As a weight matrix, obtaining the error E of a single sample by the method i .
Preferably, after the single training error is trained, the weight matrix is updated, and the update formula is as follows:
wherein, alpha is learning rate, when alpha value is smaller than E i And (3) ending training to obtain a prediction model when the error value of the model is detected.
The invention provides a building energy consumption optimization method based on an artificial neural network and BIM, which has the following beneficial effects compared with the prior art:
according to the building energy consumption optimization method based on the artificial neural network and the BIM, due to the self-adaptive characteristic of the clustering method, the clustering center number does not need to be designated in advance, so that the hidden layer node information of the RBF is determined by adopting an AP clustering algorithm, the effectiveness and feasibility of a model are improved, the building energy consumption data can be rapidly analyzed and predicted, errors caused by different data dimensionalities and numerical ranges in different building energy consumption data sets are reduced, the influence of abnormal values on the model is effectively reduced, the training speed of the model is effectively improved, and the fitting effect of the model is optimized.
Drawings
FIG. 1 is a block flow diagram of the method of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the present invention provides a technical solution: the building energy consumption optimization method based on the artificial neural network and the BIM comprises the following steps:
s1, collecting data affecting building energy consumption;
s2, calculating the acquired data to obtain a clustering center of building energy consumption data and attribution conditions of all pieces of data;
s3, taking the obtained clustering center of the energy consumption data as an implicit node of the artificial neural network, and calculating a constant matrix of the implicit node to obtain all information of an implicit layer of the artificial neural network;
s4, initializing a weight matrix for connecting an input layer and an hidden layer in the artificial neural network, inputting building energy consumption training data to train the network until the training iteration number reaches an upper limit or the training error is within an allowable range, ending the training, and finally obtaining a prediction model;
s5, inputting building energy consumption test data, and evaluating generalization capability of the obtained prediction model through predicting heat load and cold load;
s6, analyzing factors of the enclosure structure affecting the energy consumption of the building, and providing a building scheme for reducing the heat load and the cold load of the building so as to achieve the aim of reducing the carbon emission.
In the embodiment of the present invention, in step S1, the data affecting the energy consumption of the building includes the relative compactness of the building, the surface area of the building, the wall surface area, the roof, the total height of the building, the direction of the building, the area of the glass window, and the area distribution of the glass window.
In the embodiment of the present invention, in step S2, the following formula is adopted to obtain the clustering center and the attribution condition of each piece of data, where the formula is as follows:
C i ＝argmin(x i -u j )
wherein x is input data, u is a cluster center, and for each, u j For each cluster center, for each cluster center u j And performing updating iteration until a final clustering result is obtained.
In the embodiment of the present invention, in step S3, the formula for calculating the constant matrix of the hidden node is as follows:
wherein f (x) is an activation function, A k Output value of kth hidden layer node, w 1 To connect the weight matrix of the input layer and the hidden layer, beta k Is the threshold for the hidden layer node.
In the embodiment of the present invention, in step S4, when training the building energy consumption training data on the network, the weight matrix w is adjusted by means of error back propagation, so as to find the optimal solution of the prediction model, and improve the generalization capability of the model, and the formula for finding the optimal solution is as follows:
wherein t is k Is the threshold value of the output layer node, w 2 As a weight matrix, obtaining the error E of a single sample by the method i .
In the embodiment of the invention, after training out the single training error, the weight matrix is updated, and the updating formula is as follows:
wherein, alpha is learning rate, when alpha value is smaller than E i And (3) ending training to obtain a prediction model when the error value of the model is detected.
And all that is not described in detail in this specification is well known to those skilled in the art.
Claims (6)
1. The building energy consumption optimization method based on the artificial neural network and the BIM is characterized by comprising the following steps of: the method comprises the following steps:
s1, collecting data affecting building energy consumption;
s2, calculating the acquired data to obtain a clustering center of building energy consumption data and attribution conditions of all pieces of data;
s3, taking the obtained clustering center of the energy consumption data as an implicit node of the artificial neural network, and calculating a constant matrix of the implicit node to obtain all information of an implicit layer of the artificial neural network;
s4, initializing a weight matrix for connecting an input layer and an hidden layer in the artificial neural network, inputting building energy consumption training data to train the network until the training iteration number reaches an upper limit or the training error is within an allowable range, ending the training, and finally obtaining a prediction model;
s5, inputting building energy consumption test data, and evaluating generalization capability of the obtained prediction model through predicting heat load and cold load;
s6, analyzing factors of the enclosure structure affecting the energy consumption of the building, and providing a building scheme for reducing the heat load and the cold load of the building so as to achieve the aim of reducing the carbon emission.
2. The building energy consumption optimization method based on the artificial neural network and the BIM according to claim 1, wherein: in the step S1, the data affecting the energy consumption of the building include the relative compactness of the building, the surface area of the building, the wall surface area, the roof, the total height of the building, the building direction, the area of the glass window and the area distribution of the glass window.
3. The building energy consumption optimization method based on the artificial neural network and the BIM according to claim 1, wherein: in the step S2, the following formula is adopted to obtain the clustering center and the attribution condition of each piece of data, and the formula is as follows:
C i ＝argmin(x i -u j )
wherein x is input data, u is a cluster center, and for each, u j For each cluster center, for each cluster center u j And performing updating iteration until a final clustering result is obtained.
4. The building energy consumption optimization method based on the artificial neural network and the BIM according to claim 1, wherein: in the step S3, the formula for calculating the constant matrix of the hidden node is as follows:
wherein f (x) is an activation function, A k For the kth hidden layer sectionOutput value of point, w 1 To connect the weight matrix of the input layer and the hidden layer, beta k Is the threshold for the hidden layer node.
5. The building energy consumption optimization method based on the artificial neural network and the BIM according to claim 1, wherein: in the step S4, when training the building energy consumption training data on the network, the weight matrix w is adjusted by means of error back propagation, so as to find the optimal solution of the prediction model, improve the generalization capability of the model, and find the optimal solution according to the following formula:
wherein t is k Is the threshold value of the output layer node, w 2 As a weight matrix, obtaining the error E of a single sample by the method i。
6. The building energy consumption optimization method based on the artificial neural network and the BIM according to claim 5, wherein the building energy consumption optimization method is characterized by comprising the following steps of: after the single training error is trained, the weight matrix is updated according to the following update formula:
wherein, alpha is learning rate, when alpha value is smaller than E i And (3) ending training to obtain a prediction model when the error value of the model is detected.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117634006A (en) * | 2024-01-26 | 2024-03-01 | 新疆三联工程建设有限责任公司 | BIM technology-based sleeve embedded engineering management system and method |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117634006A (en) * | 2024-01-26 | 2024-03-01 | 新疆三联工程建设有限责任公司 | BIM technology-based sleeve embedded engineering management system and method |
| CN117634006B (en) * | 2024-01-26 | 2024-04-26 | 新疆三联工程建设有限责任公司 | BIM technology-based sleeve embedded engineering management system and method |
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