Load forecasting can enhance the reliability and efficiency of operations in a home energy management system(HEMS).The rise of big data with machine learning in recent years makes it a potential solution.This paper pr...Load forecasting can enhance the reliability and efficiency of operations in a home energy management system(HEMS).The rise of big data with machine learning in recent years makes it a potential solution.This paper proposes two new energy load forecasting methods,enhancing the traditional sequence to sequence long short-term memory(S2S-LSTM)model.Method 1 integrates S2S-LSTM with human behavior patterns recognition,implemented and compared by 3 types of algorithms:density based spatial clustering of applications with noise(DBSCAN),K-means and Pearson correlation coefficient(PCC).Among them,PCC is proven to be better than the others and suitable for a large number of residential customers.Method 2 further improves Method 1’s performance with a modified multi-layer Neural Network architecture,which is constituted by fully-connected,dropout and stable improved softmax layers.It optimizes the process of supervised learning in LSTM and improves the stability and accuracy of the prediction model.The performances of both proposed methods are evaluated on a dataset of 8-week electricity consumptions from 2337 residential customers.展开更多
基金This work was supported in part by EPSRC Grant EP/N032888/1 and EP/L017725/1.
文摘Load forecasting can enhance the reliability and efficiency of operations in a home energy management system(HEMS).The rise of big data with machine learning in recent years makes it a potential solution.This paper proposes two new energy load forecasting methods,enhancing the traditional sequence to sequence long short-term memory(S2S-LSTM)model.Method 1 integrates S2S-LSTM with human behavior patterns recognition,implemented and compared by 3 types of algorithms:density based spatial clustering of applications with noise(DBSCAN),K-means and Pearson correlation coefficient(PCC).Among them,PCC is proven to be better than the others and suitable for a large number of residential customers.Method 2 further improves Method 1’s performance with a modified multi-layer Neural Network architecture,which is constituted by fully-connected,dropout and stable improved softmax layers.It optimizes the process of supervised learning in LSTM and improves the stability and accuracy of the prediction model.The performances of both proposed methods are evaluated on a dataset of 8-week electricity consumptions from 2337 residential customers.
