Forecasting energy consumption using enhanced LSTM

Abstract

Accurate electricity consumption forecast has primary importance in the energy planning of the developing countries. During the last decade several new techniques are being used for electricity consumption planning to accurately predict the future electricity consumption needs. But still they lag in accurate electricity prediction. To address this problem and to accurately predict the electricity consumption in future, Enhanced LSTM architecture has been proposed named Enhanced LSTM (E-LSTM). In this proposed E-LSTM, feature extraction and prediction has been carried out. A new layer named veracious layer has been added to the LSTM to improve the prediction accuracy of the model. Result shows that the proposed technique is outperformed the existing techniques in terms of prediction accuracy and training loss.