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
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].
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.