Research On Short-term Power Load Forecasting Based On Wavelet Decomposition And LSTM Network

Electricity is the necessity of enterprise production and human life.It is an important basis for ensuring the continuous improvement of the quality of our people’s lives and rapid economic development.The timely supply of power load can guarantee the rapid development of the society,but when the load supply exceeds the demand,it will cause the waste of energy and increase environmental pollution.Therefore,short-term power load forecasting has become the core work of each power company and even the country.Through accurate prediction results,the balance between supply and demand of the power system can be realized,and the purpose of energy conservation,consumption reduction and environmental protection can be achieved while ensuring sufficient power.In this paper,the prediction model of wavelet-LSTM neural network is constructed by considering the effects of climate factors,time and date properties on the prediction and the negative impact of noise on the load prediction.Combining wavelet decomposition with LSTM can not only overcome the influence of noise on prediction,but also solve the problem of long-term information loss of power load data.In order to verify the applicability and advantages of the model,this paper uses the real load data of a power company for empirical analysis,and uses five methods,namely wavelet-LSTM,ARIMA,LSTM,SVR and multiple linear regression,to predict the load data of the last week in the sample.The results showed that the combined prediction model of wavelet and LSTM had the best prediction effect,and the MAPE value was 2.77%.The MAPE values of the prediction results of ARIMA,LSTM,SVR and multiple linear regression are 3.51%,3.21%,3.02% and 13.95%,respectively.It can be seen that the prediction accuracy of the model is improved with the addition of wavelet decomposition.To sum up,the new model proposed in this paper can be an effective tool for intelligent analysis and prediction of power system.It is hoped that each power company can scientifically and reasonably use the model to forecast the load data and realize the optimal load allocation.While helping enterprises generate economic benefits,it can also ensure economic development and social progress,and promote energy conservation and green development of the whole society.