Sequential wavelet-ANN with embedded ANN-PSO hybrid electricity price forecasting model for Indian energy exchange

Abstract

This research paper presents a novel sequential wavelet-artificial neural network (ANN) with embedded ANN-particle swarm optimization (PSO) for short-term day-ahead forecasting of market clearing price (MCP) in the Indian energy exchange. A precise price forecasting helps suppliers to set up bidding strategies, make investment decisions and be cautious against risks. Conversely, consumers can use price forecasting to exploit appropriate power purchasing strategies for maximum utility utilization. Here the most influential historical data, namely purchase bid and MCP, are considered for training the feed-forward back-propagation neural network. The proposed model involves three sequential phases. Initially, the raw historical data are smoothened by removing the high-frequency components using a wavelet transform method which may enable better training of neural network. Then, ANN is used to train historical patterns. More number of trials is carried out, and the final weights that give the least training error are stored. In the final phase, the stored weights that are obtained from various trials are used as the initial population for the embedded ANN-PSO model. Here the performance of the proposed forecasting model is carried out using three error indices, namely mean absolute percentage error, normalized mean square error and error variance.