Artificial Neural Networks Approach to Time Series Forecasting for Electricity Consumption in Gaza Strip

Abstract

This paper introduces two robust forecasting models for efficient forecasting, Artificial Neural Networks (ANNs) approach and Autoregressive Integrated Moving Average (ARIMA) models. ANNs approach to univariate time series forecasting and relevant theoretical results are briefly discussed. To choose the best training algorithm for the ANN model, several experimental simulations with different training algorithms are made. We compare ANNs approach with ARIMA model on real data for electricity consumption in Gaza Strip. The main finding is that, comparison of performance between the two proposed models reveals that ANNs outperform and preferable in selecting the most appropriate forecasting model over the ARIMA model. Keywords: Forecasting, Box-Jenkins methodology, Neural Networks, Multilayer Perceptrons.