A hybrid model for short term real-time electricity price forecasting in smart grid

Abstract

Background: With the prominent growth of power market, real-time electricity price has become a trend in smart grid as it enables moderation of power consumption of customers. Accurate forecast of real-time price (RTP) has much influence on customers' behaviors, such as better scheduling operating time of domestic appliances in order to maximize benefit. In this paper, an innovative hybrid RTP forecasting model considering linear and non-linear behaviors within input data, is proposed to forecast the short-term electricity prices in smart grid. Results: The effectiveness of the proposed hybrid forecasting model is verified by numerical results in terms of forecasting performance evaluations. The results clearly demonstrate that our approach is effective in RTP forecasting with a high accuracy. The mean absolute percentage error (MAPE) is approximate to 3.5% and it also significantly outperforms the existing models. Conclusion: Based on the achieved results, we can conclude that the proposed hybrid model is an accurate and efficient tool in short-term RTP forecasting and it is potentially effective to a variety of forecasting tasks.

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