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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한전기학회 International Journal of Control Automation and Systems International Journal of Control Automation and System Vol.6 No.5
- 발행연도
- 2008.10
- 수록면
- 639 - 650 (12page)
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초록· 키워드
오류제보하기
Electricity price forecasting has become an integral part of power system operation and control. In this paper, a wavelet transform (WT) based neural network (NN) model to forecast price profile in a deregulated electricity market has been presented. The historical price data has been decomposed into wavelet domain constitutive sub series using WT and then combined with the other time domain variables to form the set of input variables for the proposed forecasting model. The behavior of the wavelet domain constitutive series has been studied based on statistical analysis. It has been observed that forecasting accuracy can be improved by the use of WT in a forecasting model. Multi-scale analysis from one to seven levels of decomposition has been performed and the empirical evidence suggests that accuracy improvement is highest at third level of decomposition. Forecasting performance of the proposed model has been compared with (ⅰ) a heuristic technique, (ⅱ) a simulation model used by Ontario's Independent Electricity System Operator (IESO), (ⅲ) a Multiple Linear Regression (MLR) model, (ⅳ) NN model, (ⅴ) Auto Regressive Integrated Moving Average (ARlMA) model, (ⅵ) Dynamic Regression (DR) model, and (ⅶ) Transfer Function (TF) model. Forecasting results show that the performance of the proposed WT based NN model is satisfactory and it can be used by the participants to respond properly as it predicts price before closing of window for submission of initial bids.
목차
Abstract
1. INTRODUCTION
2. PROBLEM FORMULATION
3. WAVELET TRANSFORM (WT)
4. ONTARIO ELECTRICITY MARKET AND PRICE INFLUENCING VARIABLES
5. EFFECT OF WT ON HOEP
6. MODELS AND METHODOLOGY FOR HOEP FORECASTING
7. FORECASTING RESULT ANALYSIS AND DISCUSSION
8. CONCLUSION
REFERENCES
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