基於改良灰預測之短期負載預測

負載預測常用於電力業者對電力供應需求之預測，對電力系統之運轉十分重要。本研究以灰預測模型為基礎，提出一個應用於短期負載預測之新方法。為驗證本文所提方法之有效性，研究數據以台電某二次變電所負載量進行測試，並比較幾種常用於負載預測的方法，包括時間序列法、類神經網路與灰色預測模型。實驗結果顯示，本研究所提出之方法，確實可以有效對原始灰預測模型的誤差進行修正，提高預測準確度。

關鍵字

灰色預測；負載預測；類神經網路；時間序列

並列摘要

Load forecasting is widely used by power utilities to predict the amount of power needed to supply the demand. It is very important for the power system operation. A novel approach based on grey prediction models for short term load forecasting was proposed in this thesis. To verify the effectiveness of the proposed approach, an experiment was conducted to make a comparison with some commonly used methods including time series, artificial neural networks, and grey prediction models. Data set used in this experiment was adopted from a practical substation of Taipower. Experimental results showed that the proposed approach is promising as it can improve the prediction accuracy of load forecasting by reducing the errors inherent from original grey prediction models.

並列關鍵字

Grey Prediction ； Load Forecasting ； Neural Network ； Time Series

參考文獻

[1] G. E. P. Box and G. M. Jenkins, Time series analysis, forecasting and control, New York: John Wiley and Sons Ltd, 1972.

[2] S. -J. Huang and K. -R. Shih, “Short-term load forecasting via ARMA model identification including non-Gaussian process considerations,” IEEE Transactions on Power Systems, vol. 18, no.2, 2003, pp. 673-679.

[3] J. -F. Chen, W. -M. Wang and C. -M. Huang, “Analysis of an adaptive time-series autoregressive moving-average (ARMA) model for short-term load forecasting,” Electric Power Systems Research, vol. 34, 1995, pp. 187-196.

[4] A. D. Papalexopoulos and T. C. Hesterberg, “A regression based approach to short-term load forecasting,” IEEE Transactions on Power Systems, vol. 5, no. 4, 1990, pp. 1535-1547.

[5] M. T. Hagan and S. M. Behr, “The time series approach to short-term load forecast,” IEEE Transactions on Power Systems, vol. 2, no. 3, 1987, pp. 785-791.

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基於改良灰預測之短期負載預測

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