CN106779129A - A kind of Short-Term Load Forecasting Method for considering meteorologic factor - Google Patents

A kind of Short-Term Load Forecasting Method for considering meteorologic factor Download PDF

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CN106779129A
CN106779129A CN201510799160.1A CN201510799160A CN106779129A CN 106779129 A CN106779129 A CN 106779129A CN 201510799160 A CN201510799160 A CN 201510799160A CN 106779129 A CN106779129 A CN 106779129A
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load
firefly
meteorologic factor
factor
data
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王涛
王铁强
岳贤龙
王艳阳
顾雪平
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State Grid Hebei Electric Power Co Ltd
North China Electric Power University
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State Grid Hebei Electric Power Co Ltd
North China Electric Power University
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    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02ATECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A30/00Adapting or protecting infrastructure or their operation
    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04SSYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Abstract

The invention discloses a kind of Short-Term Load Forecasting Method of the consideration meteorologic factor for belonging to Techniques for Prediction of Electric Loads field.Historical load data and meteorological data are collected, abnormal data is detected and correct;The correlation of analysis load data and each meteorologic factor, it is determined that crucial meteorologic factor;Comprehensive meteorologic factor is set up with the correlation of crucial meteorologic factor according to load;The variation characteristic of area power grid daily load curve is summarized, the typical similar day of prediction day is found out;Elman neural network short-term load forecasting models are set up with comprehensive meteorologic factor using selected load, using glowworm swarm algorithm training network parameter;By the comprehensive meteorologic factor at moment to be predicted and corresponding load data input Elman neural network short-term load forecasting models, the predicted load at moment to be predicted is exported;Display predicted load.Can Accurate Prediction working day, the load data of weekend and legal festivals and holidays, precision of prediction is high and strong applicability, and reliable basis are provided for power grid operation personnel formulate generation schedule.

Description

A kind of Short-Term Load Forecasting Method for considering meteorologic factor
Technical field
The invention belongs to Techniques for Prediction of Electric Loads field, more particularly to a kind of Short-Term Load Forecasting Method for considering meteorologic factor.
Background technology
Short-term load forecasting is the critical function of energy management system, is the basis of power system security, economy, reliability service.The precision of load prediction directly affects security, economy and the power supply quality of power system.Therefore the emphasis that precision of prediction is current Short Term load Forecasting Technique research how is improved.
Mainly there are traditional prediction method and the modern major class of Forecasting Methodology two currently used for the method for short-term load forecasting.Traditional prediction method is including exponential smoothing, regression analysis, time series method, grey method etc..Wherein it is most widely used with time series method.Time series method is directed to One-dimension Time Series, is, according to the numerical value change for inferring future load, not account for influence of the meteorologic factor to load with historical load data, causes the missing of important information.Even if some methods consider the influence of meteorologic factor, also mostly it is the relations for analyzing the single meteorologic factors such as daily load and maximum temperature, minimum temperature, all of weather information can not be reflected, easily cause analysis result and error occur, so as to influence the precision of short-term load forecasting, it is difficult to the need for meeting regional load prediction.Modern Forecasting Methodology mainly has expert system approach, genetic algorithm, neural network, SVMs etc..Neutral net is due to self-learning capability and to Complex Nonlinear System disposal ability, a kind of important method as short-term load forecasting.Determine according to subjective experience because the structure and parameter of neutral net is more, the effect therefore, it is difficult to ensure prediction.Rationally determining the structure and parameter of neutral net can effectively improve the precision of load prediction.
For the deficiency that existing short-term load forecasting method is present, it is an object of the present invention to provide a kind of Short-Term Load Forecasting Method for considering meteorologic factor, the method has considered influence of multiple meteorologic factors to load, the crucial meteorologic factor of selection is used for load prediction, the workload of load prediction is effectively reduced, the accuracy and reliability of load prediction is improved;Using the parameter of firefly optimized algorithm optimization Elman neutral nets, the precision and real-time of load prediction are further improved, reliable basis are provided for power grid operation personnel formulate generation schedule, ensure power grid security, efficient and stable operation.
The content of the invention
It is an object of the invention to propose a kind of Short-Term Load Forecasting Method for considering meteorologic factor, it is characterised in that comprise the following steps:
1) historical load data and corresponding history meteorological data of area power grid are collected, and type carries out category filter to load data and meteorological data by date, detects and correct abnormal data;
2) correlation analysis, analytical procedure 1 are used) degree of correlation of the load data that obtains and each meteorologic factor, it is determined that the crucial meteorologic factor of influence this area's load;
3) comprehensive meteorologic factor is set up with the correlation of crucial meteorologic factor according to regional electric load;
4) Fast Fourier Transform (FFT) being used, to step 1) load data that obtains carries out spectrum analysis, summarizes the variation characteristic of area power grid daily load curve, finds out the typical similar day of prediction day, and sort by date;
5) Elman neutral nets are set up with comprehensive meteorologic factor using selected load, determine the structure of Elman neutral nets, Elman neutral nets are trained using glowworm swarm algorithm and optimizes network parameter, the Elman neutral nets to training are tested to set up Elman neural network short-term load forecasting models;
6) the weather forecast data at moment to be predicted are obtained, calculate comprehensive meteorologic factor, by comprehensive meteorologic factor and corresponding load data genaration test input vector, Elman neural network short-term load forecasting models are input into, its output is the predicted load at moment to be predicted;
7) predicted load is shown.
The history meteorological data includes:Temperature, relative humidity, wind speed, rainfall, air pressure, radiation, comfort index, Body Comfort Index.
The formula of the correlation analysis is:
In formula (1), xiTo need the meteorologic factor of its correlation of identification, such as temperature, humidity, wind speed, yiIt is load data,It is the average value of the meteorologic factor,It is the average value of load
It is described use glowworm swarm algorithm train Elman neutral nets and the step of optimize network parameter for:
Step 401:Initialization firefly population scale n, maximum iteration Tmax, each firefly encodes the connection weight of Elman neutral nets, structure sheaf initial input and self feed back gain factor successively;
Step 402:Calculate the fitness function f of each fireflyi, and as the brightness of firefly, according to fiThe smaller firefly brightness of value principle higher, firefly is ranked up, find most bright firefly.The computing formula of fitness function is as follows:
In formula (2),It is predicted value, yjIt is actual value, N is total sample number.
Step 403:The Attraction Degree β of each firefly is calculated, computing formula is as follows:
In formula (3), β0It is the maximum Attraction Degree of firefly;βminIt is minimum Attraction Degree;γ is the Absorption of Medium factor.
Step 404:The position of all non-most bright fireflies is updated, formula is as follows:
xi(t+1)=xi(t)+β(xj(t)-xi(t))+rand1(xg(t)-xi(t)) (4)
In formula (4), xi(t+1) it is firefly xiIn the position in t+1 generations;xiF () is firefly xiIn the position in f generations;xjF () is firefly xjIn the position in t generations;xgT () is most bright firefly in the position in f generations;rand1Uniform random factor is obeyed for [0,1] is interval.
Step 405:Position to most bright firefly carries out random perturbation, it is to avoid be absorbed in local optimum too early.Formula is as follows:
In formula (5), rand2It is [0,1] interval random number;α is step factor, is [0,1] interval constant;
Step 406:According to the position of firefly after renewal, the fitness function of firefly is recalculated according to formula (2).
Step 407:Judge whether to meet required precision or reached pre-defined iterations, if meeting, algorithm terminates;Otherwise, 203 are gone to step, into searching for next time.
Step 408:The most bright firefly information of output, i.e., the parameter of optimal Elman neutral nets.
The beneficial effects of the invention are as follows the deficiency existed for existing short-term load forecasting method, propose a kind of Short-Term Load Forecasting Method for considering meteorologic factor, consider influence of multiple meteorologic factors to load, the crucial meteorologic factor of selection influence load constitutes comprehensive meteorologic factor is used for Elman neutral net load predictions, it is effectively reduced the workload of load prediction, ensure precision of prediction, improve forecasting efficiency, strengthen the applicability of load forecasting model;The parameter of Elman neutral nets quickly, is reasonably determined using firefly optimized algorithm, the precision of prediction and real-time of Elman neural network prediction models is improve;The present invention can be calculated to a nicety the load data of working day, weekend and legal festivals and holidays, and reliable basis are provided for power grid operation personnel formulate generation schedule, ensure power grid security, efficient and stable operation;The present invention is easily operated, be suitable for actual engineer applied.
Brief description of the drawings
Fig. 1 is a kind of Short-Term Load Forecasting Method flow chart for considering meteorologic factor.
Specific embodiment
The present invention proposes a kind of Short-Term Load Forecasting Method for considering meteorologic factor, and the present invention is elaborated with specific embodiment below in conjunction with the accompanying drawings.
Fig. 1 show a kind of Short-Term Load Forecasting Method flow chart for considering meteorologic factor, comprises the following steps:
1) historical load data and corresponding history meteorological data of area power grid are collected, and type carries out category filter to load data and meteorological data by date, detects and correct abnormal data;
2) correlation analysis, analytical procedure 1 are used) degree of correlation of the load data that obtains and each meteorologic factor, it is determined that the crucial meteorologic factor of influence this area's load;
3) comprehensive meteorologic factor is set up with the correlation of crucial meteorologic factor according to regional electric load;
4) Fast Fourier Transform (FFT) being used, to step 1) load data that obtains carries out spectrum analysis, summarizes the variation characteristic of area power grid daily load curve, finds out the typical similar day of prediction day, and sort by date;
5) Elman neutral nets are set up with comprehensive meteorologic factor using selected load, determine the structure of Elman neutral nets, Elman neutral nets are trained using glowworm swarm algorithm and optimizes network parameter, the Elman neutral nets to training are tested to set up Elman neural network short-term load forecasting models;
6) the weather forecast data at moment to be predicted are obtained, calculate comprehensive meteorologic factor, by comprehensive meteorologic factor and corresponding load data genaration test input vector, Elman neural network short-term load forecasting models are input into, its output is the predicted load at moment to be predicted.
7) predicted load is shown.
The history meteorological data includes:Temperature, relative humidity, wind speed, rainfall, air pressure, radiation, comfort index, Body Comfort Index.
The formula of the correlation analysis is:
In formula (1), xiTo need the meteorologic factor of its correlation of identification, such as temperature, humidity, wind speed, yiIt is load data,It is the average value of the meteorologic factor,It is the average value of load.
It is described use glowworm swarm algorithm train Elman neutral nets and the step of optimize network parameter for:
Step 401:Initialization firefly population scale n, maximum iteration Tmax, each firefly encodes the connection weight of Elman neutral nets, structure sheaf initial input and self feed back gain factor successively;
Step 402:Calculate the fitness function f of each fireflyi, and as the brightness of firefly, according to fiThe smaller firefly brightness of value principle higher, firefly is ranked up, find most bright firefly.The computing formula of fitness function is as follows:
In formula (2),It is predicted value, yjIt is actual value, N is total sample number.
Step 403:The Attraction Degree β of each firefly is calculated, computing formula is as follows:
In formula (3), β0It is the maximum Attraction Degree of firefly;βminIt is minimum Attraction Degree;γ is the Absorption of Medium factor.
Step 404:The position of all non-most bright fireflies is updated, formula is as follows:
xi(t+1)=xi(t)+β(xj(t)-xi(t))+rand1(xg(t)-xi(t)) (4)
In formula (4), xi(t+1) it is firefly xiIn the position in t+1 generations;xiT () is firefly xiIn the position in f generations;xjT () is firefly xjIn the position in f generations;xgT () is most bright firefly in the position in t generations;rand1Uniform random factor is obeyed for [0,1] is interval.
Step 405:Position to most bright firefly carries out random perturbation, it is to avoid be absorbed in local optimum too early.Formula is as follows:
In formula (5), rand2It is [0,1] interval random number;α is step factor, is [0,1] interval constant;
Step 406:According to the position of firefly after renewal, the fitness function of firefly is recalculated according to formula (2).
Step 407:Judge whether to meet required precision or reached pre-defined iterations, if meeting, algorithm terminates;Otherwise, 203 are gone to step, into searching for next time.
Step 408:The most bright firefly information of output, i.e., the parameter of optimal Elman neutral nets.
The present invention collects 2010 to 2014 historical load datas in this area and corresponding history meteorological data with somewhere actual electric network as embodiment.Wherein, history meteorological data includes temperature, relative humidity, wind speed, rainfall, air pressure, radiation, comfort index, Body Comfort Index.Date type is divided into working day and nonworkdays, and wherein nonworkdays is divided into legal festivals and holidays and weekend again.Load data and meteorological data are classified according to different date types, summarizes the rule of its change.Abnormal data is recognized by the vertical treatment and horizontal processing of data, bad data therein, such as load burr is rejected;For missing data, carry out curve fitting to correct missing data using the normal data of same type adjacent day.
For numerous meteorologic factors, to recognize the correlation of each meteorologic factor and load, the degree of correlation of each meteorologic factor and load data is calculated using Pearson correlation coefficient formula, computing formula is as follows:
In formula, xiTo need the meteorologic factor of its correlation of identification, such as temperature, humidity, wind speed, yiIt is load data,It is the average value of the meteorologic factor,It is the average value of load., it is necessary to be normalized to each data before degree of correlation analysis is carried out.Correlation analysis are carried out to 2010 to the 2014 per day loads in this area and each meteorologic factor, load is as shown in table 1 with the coefficient correlation of each meteorologic factor.Wherein, Body Comfort Index is the comprehensive functions of the meteorological element to human body such as measurement temperature, humidity, wind speed, characterizes human body whether comfortable in atmospheric environment.The computing formula of Body Comfort Index is as follows:
In formula, D is human comfort, and T is temperature (DEG C), and U is relative humidity (%), and V is wind speed (m/s).The computing formula of comfort index is as follows:
THI=TH-(0.55-0.55U)×(TH-58)
In formula, THIt is Fahrenheit temperature, U is relative humidity (%).
The coefficient correlation of the per day load of table 1 and each meteorologic factor
Meteorologic factor 2010 2011 2012 2013 2014
Mean temperature 0.8227 0.6913 0.7891 0.7056 0.8185
Maximum temperature 0.6444 0.5533 0.6917 0.6983 0.6980
Minimum temperature 0.5496 0.5286 0.6612 0.6497 0.6373
Medial humidity -0.2349 -0.3527 -0.3953 -0.4722 -0.2624
Mean wind speed 0.0224 0.0185 0.0290 0.1041 -0.0483
Average gas pressure -0.1650 -0.2052 -0.3140 -0.2079 -0.1962
Average precipitation 0.1734 0.1010 0.1812 0.0756 -0.0598
Average radiation 0.3876 0.2657 0.3102 0.2865 0.2706
Average comfort index 0.7332 0.6528 0.6924 0.6489 0.7264
Average ride number 0.8334 0.7215 0.7412 0.7298 0.8165
Found by calculating coefficient correlation, the load of this area is maximum with mean temperature and the average ride number degree of correlation, illustrate that influence of the two meteorologic factors to this area's load is larger with respect to other factors.Therefore, mean temperature and average ride number are exactly the key factor for influenceing this area's load.
Because weather forecast is typically for districts and cities' scope, not directly against the weather forecast for saving net, therefore when the relation of provincial total load and meteorologic factor is analyzed, it is necessary to carry out certain treatment to meteorological condition.If in view of the weather information of city-level cities of the whole province is carried out, comprehensive workload is very huge and be difficult to realize, the present invention is directed to two critical load factors of mean temperature and ride number, have chosen 4 representative prefecture-level cities.Using 4 Practical Meteorological Requirements factors of prefecture-level city, the average weighted comprehensive meteorologic factor of crucial meteorologic factor is formed with reference to the actual electricity consumption situation in each city, as the meteorologic factor of Power Short-Term Load Forecasting Model.
Using Fast Fourier Transform (FFT), spectrum analysis is carried out to this area's historical load data, compare the spectrum position and respective magnitudes of load curve, summarize the variation characteristic of this area's power network daily load curve, find out the typical similar day of prediction day, and sorted by date.
Set up Elman neural network short-term load forecasting models.Choose 3 the actual negative charge values and comprehensive meteorologic factor of typical similar day synchronization, 3 the actual negative charge values and comprehensive meteorologic factor of typical similar day previous hour, predict the actual negative charge values and comprehensive meteorologic factor of day previous hour, the comprehensive meteorologic factor of prediction time, the date type of prediction time as Elman neutral nets input quantity, the predicted load of prediction time as Elman neutral nets output quantity.The structure of Elman neutral nets has 4 layers, comprising input layer, hidden layer, structure sheaf and output layer, the number of wherein input layer and output node layer is determined that the parameter such as input layer to hidden layer weights, hidden layer to output layer weights, structure sheaf initial input, self feed back gain factor is obtained using the optimization of firefly optimized algorithm by input quantity and output quantity.
The parameter setting of glowworm swarm algorithm is as follows:The population scale n=100 of firefly, Absorption of Medium factor gamma=1.0, maximum Attraction Degree β0=1.0, step factor α=0.02, maximum iteration T=200.Firefly optimized algorithm trains Elman neutral nets, and algorithm exports the positional information of optimal firefly, the as optimal parameter information of Elman neutral nets after terminating.Then, the Elman neutral nets for training are tested to set up Elman neural network short-term load forecasting models.
The weather forecast data at moment to be predicted are obtained, comprehensive meteorologic factor is calculated, by comprehensive meteorologic factor and corresponding load data genaration test input vector, Elman neural network short-term load forecasting models is input into, its output is the predicted load at moment to be predicted.To predict the outcome and compare with actual load data, calculate the error of prediction.
This area's on July 1st, 2014 is chosen to the August load data of 17 days and meteorological data as sample data set, Elman neutral nets are trained and tested.18 working day of August (Monday) in 2014,23 weekend of Augusts (Saturday) in 2014 and the 24 hours loads of National Day of on October 3rd, 2014 are predicted using the Elman neural network prediction models set up.Table 2 is three kinds of mean errors of the hourly load forecasting of method 24, wherein Elman refers to the Elman neutral nets for not accounting for meteorologic factor and being obtained using gradient descent method training, but FA-Elman refers to the Elman neutral nets for not accounting for meteorologic factor using glowworm swarm algorithm Optimal Parameters, and FA-Elman (consideration meteorologic factor) refers to the method for the present invention.
2 three kinds of methods of table are on weekdays, weekend and legal festivals and holidays (National Day) predicts the outcome
From table 2 it can be seen that relative to Elman neutral nets and FA-Elman neutral nets, the method for the present invention can obtain best predicting the outcome.
The present invention proposes a kind of Short-Term Load Forecasting Method for considering meteorologic factor, has considered multiple meteorologic factors, it is ensured that the precision and reasonability of load prediction;Using comprehensive meteorologic factor as the input of Elman neutral nets, the workload of load prediction is effectively reduced, improve the degree of accuracy and the efficiency of prediction;The parameter of Elman neutral nets is determined using firefly optimized algorithm, the accuracy and real-time of load prediction is further increased;The method of the present invention is capable of the load data of prediction work day, weekend and legal festivals and holidays exactly.
The above; the only present invention preferably specific embodiment, but protection scope of the present invention is not limited thereto, any one skilled in the art the invention discloses technical scope in; the change or replacement that can be readily occurred in, should all be included within the scope of the present invention.Therefore, protection scope of the present invention should be defined by scope of the claims.

Claims (4)

1. it is a kind of consider meteorologic factor Short-Term Load Forecasting Method, it is characterised in that the basic step of the method is as follows:
1) historical load data and corresponding history meteorological data of area power grid are collected, and type carries out category filter to load data and meteorological data by date, detects and correct abnormal data;
2) correlation analysis, analytical procedure 1 are used) degree of correlation of the load data that obtains and each meteorologic factor, it is determined that the crucial meteorologic factor of influence this area's load;
3) comprehensive meteorologic factor is set up with the correlation of crucial meteorologic factor according to regional electric load;
4) Fast Fourier Transform (FFT) being used, to step 1) load data that obtains carries out spectrum analysis, summarizes the variation characteristic of area power grid daily load curve, finds out the typical similar day of prediction day, and sort by date;
5) Elman neutral nets are set up with comprehensive meteorologic factor using selected load, determine the structure of Elman neutral nets, Elman neutral nets are trained using glowworm swarm algorithm and optimizes network parameter, the Elman neutral nets to training are tested to set up Elman neural network short-term load forecasting models;
6) the weather forecast data at moment to be predicted are obtained, comprehensive meteorologic factor is calculated, by comprehensive meteorologic factor and corresponding load data genaration input vector, Elman neural network short-term load forecasting models is input into, its output is the predicted load at moment to be predicted.
7) predicted load is shown.
2. according to claim 1 it is a kind of consider meteorologic factor Short-Term Load Forecasting Method, it is characterised in that the history meteorological data includes:Temperature, relative humidity, wind speed, rainfall, air pressure, radiation, comfort index, Body Comfort Index.
3. a kind of Short-Term Load Forecasting Method for considering meteorologic factor according to claim 1, it is characterised in that the formula of the correlation analysis is:
In formula (1), xiTo need the meteorologic factor of its correlation of identification, such as temperature, humidity, wind speed, yiIt is load data,It is the average value of the meteorologic factor,It is the average value of load.
4. a kind of Short-Term Load Forecasting Method for considering meteorologic factor according to claim 1, it is characterised in that it is described use glowworm swarm algorithm train Elman neutral nets and the step of optimize network parameter for:
Step 401:Initialization firefly population scale n, maximum iteration Tmax, each firefly encodes the connection weight of Elman neutral nets, structure sheaf initial input and self feed back gain factor successively;
Step 402:Calculate the fitness function f of each fireflyi, and as the brightness of firefly, according to fiThe smaller firefly brightness of value principle higher, firefly is ranked up, find most bright firefly.The computing formula of fitness function is as follows:
In formula (2),It is predicted value, yjIt is actual value, N is total sample number.
Step 403:The Attraction Degree β of each firefly is calculated, computing formula is as follows:
In formula (3), β0It is the maximum Attraction Degree of firefly;βminIt is minimum Attraction Degree;γ is the Absorption of Medium factor.
Step 404:The position of all non-most bright fireflies is updated, formula is as follows:
xi(t+1)=xi(t)+β(xj(t)-xi(t))+rand1(xg(t)-xi(t)) (4)
In formula (4), xi(t+1) it is firefly xiIn the position in t+1 generations;xiT () is firefly xiIn the position in t generations;xjT () is firefly xjIn the position in t generations;xgT () is most bright firefly in the position in t generations;rand1Uniform random factor is obeyed for [0,1] is interval.
Step 405:Position to most bright firefly carries out random perturbation, it is to avoid be absorbed in local optimum too early.Formula is as follows:
In formula (5), rand2It is [0,1] interval random number;α is step factor, is [0,1] interval constant;
Step 406:According to the position of firefly after renewal, the fitness function of firefly is recalculated according to formula (2).
Step 407:Judge whether to meet required precision or reached pre-defined iterations, if meeting, algorithm terminates;Otherwise, 203 are gone to step, into searching for next time.
Step 408:The most bright firefly information of output, i.e., the parameter of optimal Elman neutral nets.
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CN109858700A (en) * 2019-02-01 2019-06-07 华北水利水电大学 BP neural network heating system energy consumption prediction technique based on similar screening sample
CN110033134A (en) * 2019-04-09 2019-07-19 国网安徽省电力有限公司 A kind of short-term load forecasting algorithm of segmentation day by day considering meteorologic factor
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