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

Xing Luo,Xu Zhu,Eng Gee Lim

doi:10.1186/s41044-018-0036-x

Abstract

BackgroundWith 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.ResultsThe 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.ConclusionBased 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.

Highlights

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
The achieved results are compared with the previous methods (e.g., ARIMA model, independent back propagation (BP)-artificial neural network (ANN) model, etc.) in this work
This is because the combined model (LS model + grey prediction (GP) model) perform not well in a specific time period, i.e., 6:30 to 8:30 in this case, so that the errors improved significantly in overall, it has higher forecasting accuracies in other time periods compared with the GP model

Summary

Introduction

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. 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. The real-time price tariff is an inexorable trend in generation of power system reforming [2, 3]. In consideration of the manufacturing cost in different load levels, the dynamic tariff is a potential load management method for properly allocating incremental prices of electricity consumption to the time delivery, ensuring the overall economic rationality [6].

Methods

Results

Discussion

Conclusion