Abstract
The real-time price (RTP) of electricity becomes a trend in next decades since it is able to moderate power consumption of customers in rush hours. In order to save bills, the residential customers get involved in load shifting and the service time of domestic electric appliances are scheduled more intelligently. In this paper, first of all, according to the historical prices information, an advanced RTP forecasting model is proposed on the basis of the least-square (LS) fitting function and the grey prediction technique (GPT). Secondly, considering the factors such as predicted real-time price, type of appliances, user's preferences behaviors etc., a load scheduling approach is introduced to schedule the operating time of home appliances intelligently and estimate the resulting electricity bill. Simulation results verify the effectiveness of the proposed RTP forecasting model. The results also show that the load scheduling approach is able to accomplish peak load shifting and reduce the bill by around 12% in a typical day.