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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한전기학회 Journal of Electrical Engineering & Technology Journal of Electrical Engineering & Technology Vol.12 No.6
- 발행연도
- 2017.11
- 수록면
- 2,166 - 2,176 (11page)
이용수
초록· 키워드
오류제보하기
Electricity price prediction plays a crucial part in making the schedule and managing the risk to the competitive electricity market participants. However, it is a difficult and challenging task owing to the characteristics of the nonlinearity, non-stationarity and uncertainty of the price series. This study proposes a hybrid improved strategy which incorporates data preprocessor components and a forecasting engine component to enhance the forecasting accuracy of the electricity price. In the developed forecasting procedure, the Seasonal Adjustment (SA) method and the Ensemble Empirical Mode Decomposition (EEMD) technique are synthesized as the data preprocessing component; the Coupled Simulated Annealing (CSA) optimization method and the Least Square Support Vector Regression (LSSVR) algorithm construct the prediction engine. The proposed hybrid approach is verified with electricity price data sampled from the power market of New South Wales in Australia. The simulation outcome manifests that the proposed hybrid approach obtains the observable improvement in the forecasting accuracy compared with other approaches, which suggests that the proposed combinational approach occupies preferable predication ability and enough precision.
목차
Abstract
1. Introduction
2. Methodology
3. Case Study
4. Conclusion
References
참고문헌 (25)
참고문헌 신청
Catalao JPS, Pousinho HMI, Mendes VMF. Hybrid wavelet-PSO-ANFIS approach for short-term electricity prices forecasting. IEEE Trans Power System; vol. 22, no. 1, pp. 137-44, Feb. 2011.
Lin W-M, Gow H-J, Tsai M-T. Electricity price forecasting using enhanced probability neural network. Energy Converison and Management,vol 51, no. 12, pp. 2707-14 , Dec. 2010.
Catalao JPS, Pousinho HMI, Mendes VMF. Shortterm electricity prices forecasting in a competitive market by a hybrid intelligent approach. Energy Converison and Management, vol. 52, no. 2, pp. 1061-5, Feb. 2011.
Shayeghi H, Ghasemi A. Day-ahead electricity prices forecasting by a modified CGSA technique and hybrid WT in LSSVR based scheme. Energy Converison and Management, vol. 74, pp. 482-91, Oct. 2013.
Li XR, Yu CW, Ren SY, Chiu CH, Meng K. Dayahead electricity price forecastingbased on panel cointegration and particle filter. Electron Power System Research, vol. 95, pp. 66-76, Feb. 2013.
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