對於企業的營運來說，電力成本是一大支出，因應全球電力需求日益增加與地球暖化等因素，電能管理尤以電力需量預測被企業最為重視，在影響整體生產運作程序最小的情況下，對用電負載做有效的電力監控，準確地即時預測用電，不僅能大幅降低電費的支出，進一步提升競爭力，並可同時做到兼顧節能減碳的社會責任。
本文以鋼鐵廠之特性與負載資料提出一套預測系統，包括廠內負載與發電機歷史資料的特性分類、關聯性分析與即時需量預測，此預測模型以自我迴歸與時間序列模型針對台灣電力公司以每15分鐘為一週期計算需量，每天皆不間斷地即時預測下一週期需量值，藉以解決供電中心管理需量不超出契約容量之標準。
為驗證所提預測模型的適用性，利用廠內實際之用電負載資料分析，並進一步比較所提方法與文獻現有方法預測準確度。數值結果顯示本文所提的預測模型比起文獻使用人工神經網絡及廠內現有預測方法皆有更高的預測準確率。

In recent years, the power management has attracted more and more attention in every industry. Due to the prosperity and development of the economy and the general improvement of national standard of living. Power demand has been rising continuously all over the world. Electricity consumption caused the power system peak load obviously in the summer, and the peak load is increasing year by year. Percent reserve margin almost equal to alert, power crisis surfaced immediately.
This thesis proposes a novel model that combines autoregressive and time series method for real-time forecasting of electricity demand. Proposed method no need to retrain the model or re-estimate parameters. For the user side, this is very economical and time-saving as cost down.

[1] J.W. Taylor, “An evaluation of methods for very short-term load forecasting using minute-by-minute British data,” Int. J. Forecast., vol. 24, no. 4, pp. 645–658, 2008.
[2] M. Alamaniotis, A. Ikonomopoulos, and L. H. Tsoukalas, “Evolutionary multiobjective optimization of Kernel-based very-short-term load forecasting,” IEEE Trans. Power Syst., vol. 27, no. 3, pp. 1477–1484, Feb. 2012.
[3] C. Guan, P. B. Luh, L. D. Michel, Y. Wang, and P. B. Friedland, “Very short-term load forecasting: Wavelet neural networks with data pre-filtering,” IEEE Trans. Power Syst., vol. 28, no. 1, pp. 30–41, Jan. 2013.
[4] Y. C. Guo and D. X. Niu, “Intelligent short-term load forecasting based on pattern-base,” IEEE Int. Conf. Mach. Learn. Cybern., vol. 3, pp. 1282–1287, 2008.
[5] V. H. Hinojosa and A. Hoese, “Short-term load forecasting using fuzzy inductive reasoning and evolutionary algorithms,” IEEE Trans. Power Syst., vol. 25, no. 1, pp. 565–574, Jan. 2010.
[6] Y. Chen et al., “Short-term load forecasting: Similar day-based wavelet neural networks,” IEEE Trans. Power Syst., vol. 25, no. 1, pp. 322–330, Jan. 2010.
[7] Y. Wang, Q. Xia, and C. Kang, “Secondary forecasting based on deviation analysis for short-term load forecasting,” IEEE Trans. Power Syst., vol. 26, no. 2, pp. 500–507, Apr. 2011.
[8] Y. Chakhchoukh, P. Panciatici, and L. Mili, “Electric load forecasting based on statistical robust methods,” IEEE Trans. Power Syst., vol. 26, no. 3, pp. 982–991, Jul. 2011.
[9] H. Kebriaei, B. N. Araabi, and A. Rahimi-Kian, “Short-term load forecasting with a new nonsymmetric penalty function,” IEEE Trans. Power Syst., vol. 26, no. 4, pp. 1817–1825, Oct. 2011.
[10] M. Hanmandlu and B. K. Chauhan, “Load forecasting using hybrid models,” IEEE Trans. Power Syst., vol. 26, no. 1, pp. 20–29, Jan. 2011.
[11] J. W. Taylor, “Short-term load forecasting with exponentially weighted methods,” IEEE Trans. Power Syst., vol. 27, no. 1, pp. 458–464, Jan. 2012.
[12] S. Fan and R. J. Hyndman, “Short-term load forecasting based on a semiparametric additive model,” IEEE Trans. Power Syst., vol. 27, no. 1, pp. 134–141, Jan. 2012.
[13] A. Khosravi, S. Nahavandi, D. Creighton, and D. Srinivasan, “Interval Type-2 fuzzy logic systems for load forecasting: A comparative study,” IEEE Trans. Power Syst., vol. 27, no. 3, pp. 1274–1282, Jul. 2012.
[14] S. Arora and J. W. Taylor, “Short-term forecasting of anomalous load using rule based triple seasonal methods,” IEEE Trans. Power Syst., vol. 28, no. 3, pp. 3235–3242, Jul. 2013.
[15] E. Paparoditis and T. Sapatinas, “Short-term load forecasting: The similar shape functional time-series predictor,” IEEE Trans. Power Syst., vol. 28, no. 4, pp. 3818–3825, Oct. 2013.
[16] A. Khosravi and S. Nahavandi, “Load forecasting using interval Type-2 fuzzy logic systems: Optimal type reduction,” IEEE Trans. Ind. Informat., vol. 10, no. 2, pp. 1055–1063, May 2014.
[17] C. E. Borges, K. P. Yoseba, and I. Fernández, “Evaluating combined load forecasting in large power systems and smart grids,” IEEE Trans. Ind. Informat., vol. 9, no. 3, pp. 1570–1577, Aug. 2013.
[18] N. Amjady and A. Daraeepour, “Midterm demand prediction of electrical power systems using a new hybrid forecast technique,” IEEE Trans. Power Syst., vol. 26, no. 2, pp. 755–765, Apr. 2011.
[19] Yannig Goude, Raphael Nedellec, and Nicolas Kong, “Local Short and Middle Term Electricity Load Forecasting with Semi-Parametric Additive Models,” IEEE Trans. Smart Grid., vol. 5, no. 1, Jan. 2014.
[20] R. J. Hyndman and S. Fan, “Density forecasting for long-term peak electricity demand,” IEEE Trans. Power Syst., vol. 25, no. 2, pp. 1142–1153, Apr. 2010.
[21] Y. M. Wi, S. K. Joo, and K. B. Song, “Holiday load forecasting using fuzzy polynomial regression with weather feature selection and adjustment,” IEEE Trans. Power Syst., vol. 27, no. 2, pp. 596–603, Apr. 2012.
[22] Luis Hernandez, Carlos Baladr´on, Javier M. Aguiar, Bel´en Carro, Antonio J. Sanchez-Esguevillas, Jaime Lloret, and Joaquim Massana, “A Survey on Electric Power Demand Forecasting: Future Trends in Smart Grids, Microgrids and Smart Buildings,” IEEE Communication Surveys & Tutorials., vol. 16, no. 3, Third Quarter. 2014.
[23] D. De Silva, X. Yu, D. Alahakoon, and G. Holmes, “A data mining framework for electricity consumption analysis from meter data,” IEEE Trans. Ind. Informat., vol. 7, no. 3, pp. 399–407, Aug. 2011.
[24] A. Motto, F. Galiana, A. Conejo, and J. Arroyo, “Network-constrained multiperiod auction for a pool-based electricity market,” IEEE Trans. Power Syst., vol. 17, no. 3, pp. 646–653, Aug. 2002.
[25] L.H. Tsoukalas, R. Gao, "From smart grids to an energy internet: Assumptions, architectures and requirements,"Third International Conference on Electric Utility Deregulation and Restructuring and Power Technolohies (2008), pp. 94-98.
[26] S. Kiliccote, M. A. Piette, J. Mathieu, and K. Parrish, “Finding from seven years of field performance data for automated demand response in commercial buildings,” in Proc. ACEEE Summer Study Energy Effic. Build., 2010.
[27] S. J. Huang and K. R. Shih, “Short-term load forecasting via ARMA model identification including non-Gaussian process considerations,” IEEE Trans. Power Syst., vol. 18, no. 2, pp. 673–679, May 2003.
[28] P. E. McSharry, S. Bouwman, and G. Bloemhof, “Probabilistic forecast of the magnitude and timing of peak electricity demand,” IEEE Trans. Power Syst., vol. 20, no. 2, pp. 1166–1172, May 2005.
[29] J. Taylor and P. McSharry, “Short-term load forecasting methods: An evaluation based on European data,” IEEE Trans. Power Syst., vol. 22, no. 4, pp. 2213–2219, Nov. 2007.
[30] H. S. Migon and L. C. Alves, “Multivariate dynamic regression: modeling and forecasting for intraday electricity load,” Appl. Stochastic Models Bus. Ind., vol. 29, no. 6, pp. 579–598, 2013.
[31] H. Hippert, C. Pedreira, and R. Souza, “Neural networks for short-term load forecasting: A review and evaluation,” IEEE Trans. Power Syst., vol. 16, no. 1, pp. 44–55, Feb. 2001.
[32] N. Amjady, “Day-ahead price forecasting of electricity markets by a new fuzzy neural network,” IEEE Trans. Power Syst., vol. 21, no. 2, pp. 887–896, May 2006.
[33] N. Amjady, “Short-term bus load forecasting of power systems by a new hybrid method,” IEEE Trans. Power Syst., vol. 22, no. 1, pp. 333–341, Feb. 2007.
[34] M. S. Kandil, S. M. El-Debeiky, and N. E. Hasanien, “Long-term load forecasting for fast developing utility using a knowledge-based expert system,” IEEE Trans. Power Syst., vol. 17, no. 2, pp. 491–496, May 2002.
[35] K. B. Song, Y. S. Baek, D. H. Hong, and G. Jang, “Short-term load forecasting for the holidays using fuzzy linear regression method,” IEEE Trans. Power Syst., vol. 20, no. 1, pp. 96–101, Feb. 2005.
[36] V. Ferreira and A. Alves da Silva, “Toward estimating autonomous neural network based electric load forecasters,” IEEE Trans. Power Syst., vol. 22, no. 4, pp. 1554–1562, Nov. 2007.
[37] S. Fan and L. Chen, “Short-term load forecasting based on an adaptive hybrid method,” IEEE Trans. Power Syst., vol. 21, no. 1, pp. 392–401, Feb. 2006.
[38] A. Hinojosa and V. H. Hoese, “Short-term load forecasting using fuzzy inductive reasoning and evolutionary algorithms,” IEEE Trans. Power Syst., vol. 25, no. 1, pp. 565–574, Feb. 2010.
[39] Hartigan, J. A.; Wong, M. A. “Algorithm AS 136: A k-Means Clustering Algorithm., ”Journal of the Royal Statistical Society, Series C. 1979, 28 (1): 100–108.
[40] Archana Singh, Avantika Yadav and Ajay Rana, “K-means with Three different Distance Metrics,” International Journal of Computer Applications (0975 – 8887)., vol. 67, no. 10, Apr. 2013.
[41] Mauro Tucci, Emanuele Crisostomi, Giuseppe Giunta, and Marco Raugi, “A Multi-Objective Method for Short-Term Load Forecasting in European Countries,” IEEE Trans. on Power Syst., vol. 31, No. 3, Sep. 2016.
[42] Box, G. E. P., and Jenkins, G. (1976), Time Series Analysis: Forecasting and Control, Holden-Day.
[43] Box, G. E. P., Jenkins, G. M., and Reinsel, G. C. (1994). Time Series Analysis, Forecasting and Control, 3rd ed. Prentice Hall, Englewood Clifs, NJ.
[44] Brockwell, Peter J. and Davis, Richard A. (2002). Introduction to Time Series and Forecasting, 2nd. ed., Springer-Verlang.
[45] C. D. Lewis, Industrial and Business Forecasting Methods: A Practical Guide to Exponential Smoothing and Curve Fitting, London, U.K.: Butterworth Scientific, 1982.
[46] Yu-Hsiang Hsiao, “Household Electricity Demand Forecast Based on Context Information and User Daily Schedule Analysis From Meter Data,” IEEE Trans. Industrial Informatics., vol. 11, no. 1, Feb. 2015.