The development of power system informatization,the massive access of distributed power supply and electric vehicles have increased the complexity of power consumption in the distribution network,which puts forward hi...The development of power system informatization,the massive access of distributed power supply and electric vehicles have increased the complexity of power consumption in the distribution network,which puts forward higher requirements for the accuracy and stability of load forecasting.In this paper,an integrated network architecture which consists of the self-organized mapping,chaotic time series,intelligent optimization algorithm and long short-term memory(LSTM)is proposed to extend the load forecasting length,decrease artificial debugging,and improve the prediction precision for the short-term power load forecasting.Compared with LSTM prediction,the algorithm in this paper improves the prediction accuracy by 61.87%in terms of root mean square error(RMSE),and reduces the prediction error by 50%in the 40-fold forecast window under some circumstances.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.61673107)the National Ten Thousand Talent Program for Young Top-notch Talents(Grant No.W2070082)+1 种基金the General Joint Fund of the Equipment Advance Research Program of Ministry of Education(Grant No.6141A020223)the Jiangsu Provincial Key Laboratory of Networked Collective Intelligence(Grant No.BM2017002)。
文摘The development of power system informatization,the massive access of distributed power supply and electric vehicles have increased the complexity of power consumption in the distribution network,which puts forward higher requirements for the accuracy and stability of load forecasting.In this paper,an integrated network architecture which consists of the self-organized mapping,chaotic time series,intelligent optimization algorithm and long short-term memory(LSTM)is proposed to extend the load forecasting length,decrease artificial debugging,and improve the prediction precision for the short-term power load forecasting.Compared with LSTM prediction,the algorithm in this paper improves the prediction accuracy by 61.87%in terms of root mean square error(RMSE),and reduces the prediction error by 50%in the 40-fold forecast window under some circumstances.
