CN109086952A - It is a kind of based on genetic algorithm-neural network heat load prediction method - Google Patents
It is a kind of based on genetic algorithm-neural network heat load prediction method Download PDFInfo
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Abstract
The invention discloses a kind of based on genetic algorithm-neural network heat load prediction method, and emulation first obtains daily four feature vectors and a label to be predicted in a period of time；The above-mentioned data simulated are divided according to the time, are divided into training set data and forecast set data；Recycle z-scroe algorithm that the feature vector of two datasets and label to be predicted are normalized, and then the dimension of each data is unified；Parameter and BP neural network parameter to genetic algorithm are configured and initialize；BP neural network is established based on initiation parameter；Calculate the fitness value of a certain individual；Optimizing is carried out to parameter by genetic algorithm and obtains best BP neural network, the thermic load of forecast set data is predicted.The shortcomings that above method can overcome traditional artificial neural network to be easily trapped into local minimum, and thermic load is effectively predicted, it ensure that the accuracy of heat load prediction.
Description
Technical field
The present invention relates to heating system technical fields more particularly to a kind of based on genetic algorithm-neural network thermic load
Prediction technique.
Background technique
Since the control development of the current central heating system in China is incomplete, often there is user terminal and be unable to satisfy on demand
Heating, so reasonable heat production seems particularly significant, the main having time of heat load prediction method commonly used in the prior art
Serial anticipation method, scenario analysis predicted method and artificial neural network method etc., in which:
The moving law that time series forecasting is described by time series models, the final prediction number for determining thermic load
Formula is learned, the demand of following thermic load is calculated by mathematical formulae, although testing the speed for the pre- of heating system thermic load
Degree is fast, and accuracy is high, but the process for establishing model is complicated, does not account for the changing factor of special weather, therefore for reality
When prediction or data fluctuations it is big situation prediction effect it is unsatisfactory.
Multiple buildings are combined by scenario analysis predicted method, determine the scene of the thermic load of building in region.It
The heat load prediction of most probable appearance can be provided as a result, belonging to high probability prediction, precision is higher, but result depends on each heat
The changing rule of load, once there is emergency case, prediction deviation will be unable to estimate.
Neural network prediction method does not have to the specific complex mathematical model of dependence and is just capable of handling nonlinear problem, can
Self-organizing, self study and adaptive, and have powerful Nonlinear Mapping and generalization ability, but determine network parameter time-consuming consumption
Power lacks theory instruction, and neural network makes it easily fall into local minimum based on the reason of empirical risk minimization, predicts
Speed is also more slow.
Summary of the invention
The object of the present invention is to provide a kind of based on genetic algorithm-neural network heat load prediction method, and this method can
The shortcomings that overcome traditional artificial neural network to be easily trapped into local minimum, and thermic load is effectively predicted, it ensure that
The accuracy of heat load prediction.
The purpose of the present invention is what is be achieved through the following technical solutions:
It is a kind of based on genetic algorithm-neural network heat load prediction method, which comprises
Step 1, first emulation obtain daily four feature vectors and a label to be predicted in a period of time；Wherein,
Four feature vectors include outdoor dry-bulb temperature, solar illumination, wind speed and wet-bulb temperature；Label to be predicted is thermic load number
Value；
Step 2 divides the above-mentioned data simulated according to the time, is divided into training set data and forecast set data；
Step 3 recycles z-scroe algorithm that the feature vector of two datasets and label to be predicted are normalized
Processing, so the dimension of each data is unified；
Step 4 is configured and initializes to the parameter and BP neural network parameter of genetic algorithm；
Step 5 establishes BP neural network based on initiation parameter, using all weights and threshold value of BP neural network as one
The orderly chromosome of group is indicated according to the number of weight and threshold value with the real variable of corresponding dimension；
Step 6, the fitness value for calculating a certain individual, are calculated after the thermic load numerical value of prediction is carried out renormalization
The MSE numerical value of label to be predicted；
Step 7 carries out the best BP neural network of optimizing acquisition to parameter by genetic algorithm, negative to the heat of forecast set data
Lotus is predicted.
As seen from the above technical solution provided by the invention, the above method can overcome traditional artificial neural network to hold
The shortcomings that easily falling into local minimum, and thermic load is effectively predicted, it ensure that the accuracy of heat load prediction.
Detailed description of the invention
In order to illustrate the technical solution of the embodiments of the present invention more clearly, required use in being described below to embodiment
Attached drawing be briefly described, it should be apparent that, drawings in the following description are only some embodiments of the invention, for this
For the those of ordinary skill in field, without creative efforts, it can also be obtained according to these attached drawings other
Attached drawing.
Fig. 1 is provided in an embodiment of the present invention based on the signal of genetic algorithm-neural network heat load prediction method flow
Figure；
Fig. 2 establishes BP nerve net network structure body schematic diagram by the embodiment of the present invention.
Specific embodiment
With reference to the attached drawing in the embodiment of the present invention, technical solution in the embodiment of the present invention carries out clear, complete
Ground description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.Based on this
The embodiment of invention, every other implementation obtained by those of ordinary skill in the art without making creative efforts
Example, belongs to protection scope of the present invention.
The embodiment of the present invention is described in further detail below in conjunction with attached drawing, is implemented as shown in Figure 1 for the present invention
Example provide based on genetic algorithm-neural network heat load prediction method flow schematic diagram, which comprises
Step 1, first emulation obtain daily four feature vectors and a label to be predicted in a period of time；
Wherein, four feature vectors include outdoor dry-bulb temperature, solar illumination, wind speed and wet-bulb temperature；It is to be predicted
Label is thermic load numerical value；
Step 2 divides the above-mentioned data simulated according to the time, is divided into training set data and forecast set data；
Step 3 recycles z-scroe algorithm that the feature vector of two datasets and label to be predicted are normalized
Processing, so the dimension of each data is unified；
In the step, the calculation formula of use is normalized are as follows:
Wherein, xi,jRepresent the jth dimension data to normalized i-th group of data；μjRepresent the mean value of jth dimensional feature；σjGeneration
The standard deviation of table jth dimensional feature；x′i,jThe jth dimension data of i-th group of data after representing normalization.
Step 4 is configured and initializes to the parameter and BP neural network parameter of genetic algorithm；
In the step, the parameter of genetic algorithm includes population quantity, population size, mrna length, crossover probability and variation
Probability；Such as it is 200 that population quantity, which can be set, population size 50, each mrna length is 10, and each initial parameter is seen below
Shown in table 1:
Table 1
| Parameter | Numerical value |
| Population quantity | 200 |
| Population size | 50 |
| Mrna length | 10 |
| Crossover probability | 0.05 |
| Mutation probability | 0.5 |
BP neural network parameter includes input neuron number, hidden neuron number, output neuron number and coding
Length, in which:
Input the dimension that neuron number is feature vector；Hidden neuron number is configured as needed, such as this
It can be set to 25 in example；Output neuron number is the dimension of label to be predicted；The calculation formula of code length are as follows:
S=R × S1+S1×S2+S1+S2
In formula, S is code length；R is input neuron number；S1For hidden neuron number；S2For output neuron
Number.
Step 5 establishes BP neural network based on initiation parameter, using all weights and threshold value of BP neural network as one
The orderly chromosome of group is indicated according to the number of weight and threshold value with the real variable of corresponding dimension；
Here, all weights and threshold value include power battle array, the power battle array of hidden layer to output layer, hidden layer threshold value of the input layer to hidden layer
With output layer threshold value.
For example, it is illustrated in figure 2 the established BP nerve net network structure body schematic diagram of the embodiment of the present invention, by compiling
Gene representation after code are as follows:
X=[ω11,ω12,…ωmn,v11,v12,…vpm,θ1,θ2,…θm,t1,t2,…tp]
Wherein, ωi,jJ-th of neuron of input layer is represented to the threshold value of i-th of neuron of hidden layer；vi,jIt represents hidden
Threshold value of j-th of the neuron containing layer to i-th of neuron of output layer；θiIndicate the threshold value of i-th of neuron of hidden layer；ti
Indicate the threshold value of i-th of neuron of output layer.
Step 6, the fitness value for calculating a certain individual, are calculated after the thermic load numerical value of prediction is carried out renormalization
The MSE numerical value of label to be predicted；
It is to calculate 50 individual precision of prediction MSE numerical value in this example, above-mentioned renormalization calculation formula is expressed as；
In formula, yiThe thermic load of normalized i-th of sample point is represented, μ represents the mean value of thermic load, and σ represents thermic load
Standard deviation, y 'iThe thermic load of i-th of sample point after representing renormalization.
Step 7 carries out the best BP neural network of optimizing acquisition to parameter by genetic algorithm, negative to the heat of forecast set data
Lotus is predicted.
In the step, the process that optimizing obtains best BP neural network is carried out to parameter by genetic algorithm are as follows:
Excellent individual is chosen using roulette form；
Previous generation excellent individual is subjected to single point crossing, forms new individual；
The operation for carrying out step 6 based on new individual obtains the MSE numerical value of label to be predicted, if meeting optimal termination item
Part then terminates, and obtains all weights of optimal network and the numerical value of threshold value；If not satisfied, then continuing iteration optimum individual.
In addition, in an iterative process, can also be changed to a certain encoded radio individual in population, improve algorithm with
Machine search capability and prevent algorithm occur " precocity " and terminate.
The realization process of heat load prediction method described in the embodiment of the present invention specifically:
Go out the daily outdoor dry-bulb temperature, solar illumination, wind speed in the 1-3 month in 1 year first with dest software emulation
With wet-bulb temperature and thermic load numerical value, and using the data in 1-2 month as training set, the data in March are as forecast set；
Then data are normalized with z-score algorithm；
Independent variable and objective function setting, objective function of the invention are set as to all weights and threshold value of network
The MSE numerical value of label after being set as renormalization；
Then best BP neural network is obtained to parameter progress optimizing by genetic algorithm to carry out in advance the thermic load in March
It surveys.
It is worth noting that, the content being not described in detail in the embodiment of the present invention belongs to professional and technical personnel in the field's public affairs
The prior art known.
The foregoing is only a preferred embodiment of the present invention, but scope of protection of the present invention is not limited thereto,
Within the technical scope of the present disclosure, any changes or substitutions that can be easily thought of by anyone skilled in the art,
It should be covered by the protection scope of the present invention.Therefore, protection scope of the present invention should be with the protection model of claims
Subject to enclosing.
Claims (6)
1. a kind of based on genetic algorithm-neural network heat load prediction method, which is characterized in that the described method includes:
Step 1, first emulation obtain daily four feature vectors and a label to be predicted in a period of time；Wherein, described
Four feature vectors include outdoor dry-bulb temperature, solar illumination, wind speed and wet-bulb temperature；Label to be predicted is thermic load numerical value；
Step 2 divides the above-mentioned data simulated according to the time, is divided into training set data and forecast set data；
Step 3 recycles z-scroe algorithm that place is normalized in the feature vector of two datasets and label to be predicted
Reason, so the dimension of each data is unified；
Step 4 is configured and initializes to the parameter and BP neural network parameter of genetic algorithm；
Step 5 establishes BP neural network based on initiation parameter, has using all weights of BP neural network and threshold value as one group
Sequence chromosome is indicated according to the number of weight and threshold value with the real variable of corresponding dimension；
Step 6, the fitness value for calculating a certain individual, are calculated after the thermic load numerical value of prediction is carried out renormalization to pre-
The MSE numerical value of mark label；
Step 7 carries out optimizing to parameter by genetic algorithm and obtains best BP neural network, to the thermic loads of forecast set data into
Row prediction.
2. according to claim 1 based on genetic algorithm-neural network heat load prediction method, which is characterized in that described
The calculation formula of use is normalized in step 3 are as follows:
Wherein, xi,jRepresent the jth dimension data to normalized i-th group of data；μjRepresent the mean value of jth dimensional feature；σjRepresent jth
The standard deviation of dimensional feature；x′i,jThe jth dimension data of i-th group of data after representing normalization.
3. according to claim 1 based on genetic algorithm-neural network heat load prediction method, which is characterized in that in step
In rapid 4, the parameter of genetic algorithm includes population quantity, population size, mrna length, crossover probability and mutation probability；
BP neural network parameter includes inputting neuron number, hidden neuron number, output neuron number and code length,
Wherein:
Input the dimension that neuron number is feature vector；Hidden neuron number is configured as needed；Output neuron
Number is the dimension of label to be predicted；The calculation formula of code length are as follows:
S=R × S1+S1×S2+S1+S2
In formula, S is code length；R is input neuron number；S1For hidden neuron number；S2For output neuron number.
4. according to claim 1 based on genetic algorithm-neural network heat load prediction method, which is characterized in that in step
Gene representation in rapid 5, after coding are as follows:
X=[ω11,ω12,…ωmn,v11,v12,…vpm,θ1,θ2,…θm,t1,t2,…tp]
Wherein, ωi,jJ-th of neuron of input layer is represented to the threshold value of i-th of neuron of hidden layer；vi,jRepresent hidden layer
J-th of neuron to the threshold value of i-th of neuron of output layer；θiIndicate the threshold value of i-th of neuron of hidden layer；tiIt indicates
The threshold value of i-th of neuron of output layer.
5. according to claim 1 based on genetic algorithm-neural network heat load prediction method, which is characterized in that in step
In rapid 6, renormalization calculation formula is expressed as；
In formula, yiThe thermic load of normalized i-th of sample point is represented, μ represents the mean value of thermic load, and σ represents the standard of thermic load
Difference, y 'iThe thermic load of i-th of sample point after representing renormalization.
6. according to claim 1 based on genetic algorithm-neural network heat load prediction method, which is characterized in that in step
In rapid 7, the process that optimizing obtains best BP neural network is carried out to parameter by genetic algorithm are as follows:
Excellent individual is chosen using roulette form；
Previous generation excellent individual is subjected to single point crossing, forms new individual；
The operation for carrying out step 6 based on new individual obtains the MSE numerical value of label to be predicted, if meeting optimal termination condition,
Terminate, obtains all weights of optimal network and the numerical value of threshold value；If not satisfied, then continuing iteration optimum individual.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111401604A (en) * | 2020-02-17 | 2020-07-10 | 国网新疆电力有限公司经济技术研究院 | Power system load power prediction method and energy storage power station power distribution method |
| CN112926795A (en) * | 2021-03-22 | 2021-06-08 | 西安建筑科技大学 | SBO (statistical analysis) -based CNN (continuous casting) optimization-based high-rise residential building group heat load prediction method and system |
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| US20050192915A1 (en) * | 2004-02-27 | 2005-09-01 | Osman Ahmed | System and method for predicting building thermal loads |
| CN103105246A (en) * | 2012-12-31 | 2013-05-15 | 北京京鹏环球科技股份有限公司 | Greenhouse environment forecasting feedback method of back propagation (BP) neural network based on improvement of genetic algorithm |
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| CN111401604A (en) * | 2020-02-17 | 2020-07-10 | 国网新疆电力有限公司经济技术研究院 | Power system load power prediction method and energy storage power station power distribution method |
| CN111401604B (en) * | 2020-02-17 | 2023-07-07 | 国网新疆电力有限公司经济技术研究院 | Power system load power prediction method and energy storage power station power distribution method |
| CN112926795A (en) * | 2021-03-22 | 2021-06-08 | 西安建筑科技大学 | SBO (statistical analysis) -based CNN (continuous casting) optimization-based high-rise residential building group heat load prediction method and system |
| CN112926795B (en) * | 2021-03-22 | 2023-11-14 | 新疆苏通工程建设有限公司 | High-rise residential building group heat load prediction method and system based on SBO optimization CNN |
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