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In this paper, we present a new method to forecast the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) using fuzzy time series and automatically generated weights of multiple factors. The proposed method uses the variation magnitudes of adjacent historical data to generate fuzzy variation groups of the main factor (i.e., the TAIEX) and the elementary secondary factors (i.e., the Dow Jones, the NASDAQ and the M1B), respectively. Based on the variation magnitudes of the main factor TAIEX and the elementary secondary factors of a particular trading day, it constructs the occurrence vector of the main factor and the occurrence vectors of the elementary secondary factors on the trading day, respectively. By calculating the correlation coefficients between the numerical data series of the main factor and the numerical data series of each elementary secondary factor, respectively, it calculates the relevance degree between the forecasted variation of the main factor and the forecasted variation of each elementary secondary factor. Based on the correlation coefficients between the numerical data series of the main factor and the numerical data series of each elementary secondary factor on a trading day, it automatically generates the weights of the occurrence vector of the main factor and the occurrence vector of each elementary secondary factor on the trading day, respectively. Then, it calculates the forecasted variation of the main factor and the forecasted variation of each elementary secondary factor on the trading day, respectively, to obtain the final forecasted variation on the trading day. Finally, based on the closing index of the TAIEX on the trading day and the final forecasted variation on the trading day, it generates the forecasted value of the next trading day. The experimental results show that the proposed method outperforms the existing methods. [ABSTRACT FROM PUBLISHER]

The ultimate goal of an electric utility is to create maximum profit while maintaining reliability and security of the power supply. The operation and control of power system is sensitive to system demand. Therefore, improvements in load-forecasting accuracy will lead to cost savings and enhance system security. Due to Taiwan's distinct climate characteristics, it is difficult to obtain satisfactory load-forecasting results by treating the whole island as a single region. In addition, weather factors, such as temperature, relative humidity, and the Heat Index (HI) (a human-perceived equivalent temperature) may also affect load-consumption patterns. This paper proposes a multiregion short-term load-forecasting methodology, taking into account the HI to improve load-forecasting accuracy in Taiwan Power Company's (Taipower's) system. The results show that adopting the HI as a parameter can effectively improve the accuracy if the temperature of the region under investigation is above 27 ^\circ\C (80 ^ \circ\F). By considering both the load/weather diversity and the HI, further improvements to the load forecasting for the Taipower system during summer can be achieved. [ABSTRACT FROM AUTHOR]