VMD based Modified Multi-View Neural Network for Long-Term Load Forecasting

Abstract

Long-term load forecasting is required for power system planning, long-term investments, and expansion policies. In this paper, an ensemble of Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN) is proposed that minimizes the forecasting error for the long-term. The features are extracted from the decomposition of load series using Variational Mode Decomposition (VMD) into various timescale subseries' and are combined with the exogenous variables like temperature and rain. This data is then used to train a Fully Connected Neural Network (FCNN) that produces the final output. This ensemble is expected to provide an accurate forecast as local variations in the series are captured by CNN and long-term dependencies by LSTM, while the VMD decomposes the raw data into low and high-frequency subsequences. The efficacy of the methodology is assessed on the actual load data of Madhya Pradesh (MP) State, India. It is observed that the proposed method has higher forecasting accuracy.