Electricity load forecasting for Urban area using weather forecast information

Artificial Neural Network (ANN) and Bagged Regression Trees are developed to generate and predicted load forecast in Urban area using Meteorological data and good agreement was observed by comparing these results with those available in the literature.

Abstract

The global demand for energy is increasing daily with the expansion of energy infrastructure and the addition of new appliances. Efficient Energy Management System (EMS) is the need of the day. All residential and commercial buildings can achieve better energy efficiency and consumption with the use of EMS. Load forecasting is one of the methods to enable EMS to work efficiently. The accuracy of load forecast depends on many factors. The load forecast model must consider the weather forecast for the region in developing an accurate forecast. This paper develops Artificial Neural Network (ANN) and Bagged Regression Trees to generate and predicted load forecast in Urban area using Meteorological data. ANN model is compared with Bagged Regression Trees for prediction accuracy. Good agreement was observed by comparing these results with those available in the literature. It has been observed through analysis that Bagged Regression Trees produce better load prediction for the day ahead load in the urban area.