Research on Hydroelectric Power Prediction Based on Long Short-Term Memory Network
To solve the problems of little multi-timescale correlation analysis and low accuracy of power prediction in hydroelectric plant power prediction,a hydroclectric power prediction method based on long short-term memory(LSTM)network is proposed.Firstlly,the quality of historical hydroclectric plant power generation data is evaluated,and the missing and erroneous data are repaired to eliminate the influence of erroneous data on the prediction accuracy of the model.Secondly,the factors affecting hydroelectric plant power generation are analyzed,and on this basis,the LSTM network is used to realize the correlation analysis and prediction of power generation in multi-timescale of year,season,month,day,and hour.Finally,the application in a hydroelectric plant shows that the prediction rate of hydroelectric power is 97.2%.The proposed method can effectively improve the accuracy of hydroelectric plant generation power prediction and enhance the lean management level of hydroelectric plant.
Long short-term memory(LSTM)networkHydroelectric plantPower generation predictionMulti-timescalesInfluencing factorsData quality assessmentData cleaning
NETL
NSTL
万方数据
