期刊
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
卷 15, 期 9, 页码 5378-5388出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2019.2911700
关键词
Autoregressive time varying model; electricity price; feature selection; kernel regression; price spikes; wavelet technique
类别
资金
- Hong Duc, Thanh Hoa-UOWResearch Scholarship Program
Forecasting spot prices of electricity is challenging because it not only contains seasonal variations, but also random, abrupt spikes, which depend on market conditions and network contingencies. In this paper, a hybrid model has been developed to forecast the spot prices of electricity in two main stages. In the first stage, the prices are forecasted using autoregressive time varying (ARXTV) model with exogenous variables. To improve the forecasting ability of the ARXTV model, the price variations in the training process have been smoothened using the wavelet technique. In the second stage, a kernel regression is used to estimate the price spikes, which are detected using support vector machine based model. In addition, mutual information technique is employed to select appropriate input variables for the model. A case study is carried out with the aid of price data obtained from the Australian energy market operator. It is demonstrated that the proposed hybrid method can accurately forecast electricity prices containing spikes.
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