An improved Grey-based approach for electricity demand forecasting

An improved Grey-based prediction algorithm to forecast a very-short-term electric power demand for the demand-control of electricity is presented and the adaptive value of α in the Grey differential equation is obtained quickly with the average system slope technique.

Abstract

The aim of this project is to develop an online electricity demand predictor. In this paper, we present an improved Grey-based prediction algorithm to forecast a very-short-term electric power demand for the demand-control of electricity. We adopted Grey prediction as a forecasting means because of its fast calculation with as few as four data inputs needed. However, our preliminary study shows that the general Grey model, GM(1,1) is inadequate to handle a volatile electrical system. The general GM(1,1) prediction generates the dilemmas of dissipation and overshoots. In this study, the prediction is improved significantly by applying the transformed Grey model and the concept of average system slope. The adaptive value of α in the Grey differential equation is obtained quickly with the average system slope technique. The present intelligent Grey-based electric demand-control system is able to provide an instrument to save operation costs for high energy consuming enterprises. In such a way, the wastage of electric consumption can be avoided. That is, it is another achievement of virtual electric power plant.