Forecasting of peak electricity demand using ANNGA and ANN-PSO approaches

Abstract

Electrical load forecasting is essential in the field of power systems to enhance the operation and economical utilization In this paper, a combined approaches of artificial neural networks (ANN) with particle-swarm-optimization (PSO) and genetic algorithm optimization (GA) for short and mid-term load forecasting is developed. The model identifies the relationship among load, temperature and humidity using a case study of Sharjah City in United Arab Emirates. The ANN model trains the hourly peak load data for a set of days and then forecasts the load for next day. Actual data obtained from Sharjah Electricity and Water Authority (SEWA) is used to validate the results.