| Summary: | 碩士 === 國立雲林科技大學 === 電機工程系碩士班 === 92 === In the deregulated electricity environment, power producers and consumers need accurate price forecasting to plan bidding strategies in order to maximum their benefits.
Moreover, load effect electricity price greatly, so how to achieve load forecasting is also essential. This thesis presents multiple regression analysis and fuzzy system method to obtain the estimated load first, because the modeling of regression analysis is simple and useful, and the fuzzy system method use some experience rules can solve many problems. Sequentially, inputting the estimated load to the price-regression models or price-fuzzy inference mechanism can derive more exact expected price.
The introduced data of this structure contain demand data of Taiwan Power Company (Taipower) and temperature data of Central Weather Bureau. However, electrical power industry in Taiwan is not yet deregulated, therefore, to simulate price data, the actual generation data of Taipower electricity is used as historical and observed values. As long as market is deregulated, using these approaches may forecast market price in the future effectively. This thesis presents regression analysis and fuzzy system for the forecasting load and price, and the result is quite accurate.
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