Title :
A Hybrid Approach for Probabilistic Forecasting of Electricity Price
Author :
Can Wan ; Zhao Xu ; Yelei Wang ; Zhao Yang Dong ; Kit Po Wong
Author_Institution :
Dept. of Electr. Eng., Hong Kong Polytech. Univ., Hong Kong, China
Abstract :
The electricity market plays a key role in realizing the economic prophecy of smart grids. Accurate and reliable electricity market price forecasting is essential to facilitate various decision making activities of market participants in the future smart grid environment. However, due to the nonstationarities involved in market clearing prices (MCPs), it is rather difficult to accurately predict MCPs in advance. The challenge is getting intensified as more and more renewable energy and other new technologies emerged in smart grids. Therefore transformation from traditional point forecasts to probabilistic interval forecasts can be of great importance to quantify the uncertainties of potential forecasts, thus effectively supporting the decision making activities against uncertainties and risks ahead. This paper proposes a hybrid approach to construct prediction intervals of MCPs with a two-stage formulation. In the first stage, extreme learning machine (ELM) is applied to estimate point forecasts of MCPs and model uncertainties involved. In the second stage, the maximum likelihood method is used to estimate the noise variance. A generalized and comprehensive evaluation framework for probabilistic electricity price forecasting is proposed in this paper. The effectiveness of the proposed hybrid method has been validated through comprehensive tests using real price data from Australian electricity market.
Keywords :
decision making; maximum likelihood estimation; power markets; pricing; smart power grids; Australian electricity market; ELM; MCP; decision making; electricity price; estimate point forecasts; extreme learning machine; hybrid approach; market clearing prices; maximum likelihood method; noise variance; price forecasting; probabilistic interval forecasts; renewable energy; smart grid environment; smart grids; Artificial neural networks; Electricity; Forecasting; Noise; Predictive models; Reliability; Uncertainty; Artificial neural network; bootstrap; electricity price forecasting; maximum likelihood estimation; prediction intervals;
Journal_Title :
Smart Grid, IEEE Transactions on
DOI :
10.1109/TSG.2013.2274465