A Novel Approach for Electrical Load Forecasting Using Distributed Sensor Networks

Abstract

Electrical market often demands accurate forecasting of electrical load for planning and operation of the power infrastructure. Current models can forecast load from half hour up to 24 hours and are based on aggregate temperature for the entire day. Although these models work very well, they do not consider the intermediate real time information between time intervals to forecast load which introduce many uncertainties pertaining to factors such as climatic conditions, geographic locations etc. Furthermore, such intermediate real time information is costly and difficult to obtain. With the aid of distributed sensor networks, real time information can easily be obtained which can lead to precise planning and operation of power systems. Such information can easily improve electrical load forecasting and reduce uncertainty which can have a direct impact on the customer. We propose new and improved models for electricity load forecasting by incorporating real-time weather (temperature) information arising from the low-cost distributed sensor networks.