摘要： We combine MA line of TAIEX and SVR to overcome the load demands over-prediction problems caused by the economic downturn. The Taiwan island-wide electricity power system was used as the case study. Short- to middle-term MA lines of TAIEX are found to be good economic input variables for load forecasting models. The global economic downturn in 2008 and 2009, which was spurred by the bankruptcy of Lehman Brothers, sharply reduced the demand for electricity load. Conventional load-forecasting approaches were unable to respond to sudden changes in the economy, because these approaches do not consider the effect of economic factors. Therefore, the over-prediction problem occurred. To overcome this problem, this paper proposes a novel, economy-reflecting, short-term load forecasting (STLF) approach based on theories of moving average (MA) line of stock index and machine learning. In this approach, the stock indices decision model is designed to reflect fluctuations in the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) series, which is selected as an optimal input variable in support vector regression load forecasting model at an appropriate timing. The Taiwan island-wide hourly electricity load demands from 2008 to 2010 are used as the case study for performance benchmarking. Results show that the proposed approach with a 60-day MA of the TAIEX as economic learning pattern achieves good forecasting performance. It outperforms the conventional approach by 29.16% on average during economic downturn-affected days. Overall, the proposed approach successfully overcomes the over-prediction problems caused by the economic downturn. To the best of our knowledge, this paper is the first attempt to apply MA line theory of stock index on STLF. 出版者： Kidlington: Elsevier Ltd 出版日期： 2013-01 出處： Energy conversion and management, 2013-01, Vol.65, p.331-342 版權： 2012 Elsevier Ltd 版權： 2014 INIST-CNRS 識別號： ISSN: 0196-8904 識別號： EISSN: 1879-2227 識別號： DOI: 10.1016/j.enconman.2012.08.001 識別號： CODEN: ECMADL
