Abstract
Accurate and reliable electricity price forecasting is essential for market participants to make various decisions in the deregulated electricity market. However, due to the time-variant and nonstationary of price, which is related to change of market competitors' strategies, predicting price accurately in advance is rather difficult. Therefore, probabilistic interval forecast instead of traditional point forecast can be of great significance to make bidding strategies. In this paper, a hybrid approach for probabilistic forecast is proposed with two-stage formulation: 1) An improved RBF NNs based on Adaboost algorithm (Adaboost_RBF) is proposed for point forecast of price. 2) Prediction interval can be obtained according to the statistical distribution of price forecast error. Effectiveness and reliability of proposed model is validated through case studies from Australian electricity market by comparing with existing methods such as RBF neural network and ARMA.
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.