Monthly Energy Consumption Forecasting Based On Windowed Momentum Neural Network

Abstract

Artificial Neural Network (ANN) with adaptability and non-linearity is well suited to perform forecasting tasks. The paper proposes the systematic approach of monthly energy forecasting using Windowed Momentum Algorithm in ANN. It describes the mathematical foundations and implementation to calculate the monthly energy demand using previous three years consecutive monthly energy data and weather information. The results are compared with the standard momentum, generally used in Back Propagation Algorithm. The proposed algorithm shows good accuracy with maximum mean absolute percentage error of 1.177%.