本研究探討以氣溫輪廓的概念融入以類神經網路為核心的負載預測程式，配合近幾年的每小時歷史負載資料、每小時歷史氣溫資料、每日最高、最低溫及假日等資料，來預測台電短期負載，並分析此負載預測結果的準確性及特性。分析結果發現，採用氣溫輪廓後的負載預測結果，準確度明顯較沒有使用氣溫輪廓的負載預測結果高很多。除使得在氣象局雖未提供未來一周24小時氣溫預測資料時，能採用歷史24小時氣溫值協助負載預測外，更提高負載預測的準確度。期能應用本負載預測方法於電力公司相關運轉工作。

This thesis is used to investigate the short term load forecasting of Taipower system using temperature profiles and neural networks. The historical hourly load data, historical hourly temperature values, highest and lowest temperatures of each day, and holiday indices are used. It wants to obtain one-week short-term load forecasting values and the results are compared with the actual values. From the research results, the forecasting values using temperature profiles are more accurate than that without temperature profiles. The historical 24-hour temperature values also could be used even if the forecast temperature values are not given. The load forecasting results are better than ever. It is hoped that the results in this thesis could be helpful to Taipower.

[1]張斐章、張麗秋、黃浩倫，類神經網路理論與實務，東華書局，2003.
[2]葉怡成，應用類神經網路，儒林圖書公司，1997.
[3]蘇木春、張孝德，機械學習：類神經網路、模糊系統以及基因演算法則，全華科技圖書股份有限公司，1999.
[4]周鵬程，類神經網路入門活用Matlab，全華科技圖書股份有限公司，2002.
[5]羅華強，類神經網路-MATLAB的應用，清蔚科技，2001.
[6]古瓊景，EMC類神經網路IC原理及應用，第1-3~1-5頁，全華科技圖書股份有限公司，1999.
[7]王進德、蕭大全，類神經網路與模糊控制理論入門，第2~14頁，全華科技圖書股份有限公司，1999
[8] X. P. Lai and H. X. Zhou, ‟ Short-Term Load Forecasting Using Neural Network Combined with Linear Models,” Proceedings of the 3rd World Congress on Intelligent Control and Automation , pp.1633-P1636,June 2000.
[9] I. Drezga and S. Rahman, ‟ Short-Term Load Forecasting With Local ANN Predictors, ” IEEE Transactions on Power Systems, Vol. 14, No. 3, pp.844-P849, August 1999.
[10] N. Kandil, V. Sood, and M. Saad, ‟ Use Of ANNs For Short-Term Load Forecasting, ” Proceedings of the 1999 IEEE Canadian Conference on Electrical and Computer Engineering Shaw Conference Center, Edmonton , Alberta, Canada, pp.1057–1061, May 1999.
[11] W. Charytoniuk, W. J. Lee, C. S. Chen, P. Kotas, and P. V. Olinda, ‟ Neural-Network-Based Demand Forecasting in a Deregulated Environment, ” IEEE Transactions on Industry Applications, Vol. 36, No. 3, pp.1 –5 ,June 2000.
[12] James W. Taylor and R. Buizza, ‟ Neural Network Load Forecasting With Weather Ensemble Predictions, ” IEEE Transactions on Power Systems, Vol. 17, No. 3, pp.626–P632, August 2002.
[13] H.S. Hippert and C.E. Pedreira, ‟ Estimation Temperature Profiles For Short-Term Load Forecasting: Neural Networks Compared To Linear Models, ” IEE Proc-Gener. Transm. Distrib. , Vol. 151, No.4, pp.543–546,July 2004.