Application of Soft Computing Techniques with Fourier Series to Forecast Monthly Electricity Demand
碩士 === 國立清華大學 === 工業工程與工程管理學系 === 104 === The information from electricity demand forecasting helps energy generation enterprises develop an electricity supply system. This study aims to develop a monthly electricity forecasting model to predict the electricity demand for energy management. Given th...
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ndltd-TW-104NTHU50310702017-07-09T04:30:31Z http://ndltd.ncl.edu.tw/handle/23171218166774438081 Application of Soft Computing Techniques with Fourier Series to Forecast Monthly Electricity Demand 應用柔性運算技術與傅立葉級數於月電力需求預測 Lai, Chia Liang 賴佳良 碩士 國立清華大學 工業工程與工程管理學系 104 The information from electricity demand forecasting helps energy generation enterprises develop an electricity supply system. This study aims to develop a monthly electricity forecasting model to predict the electricity demand for energy management. Given that the influence of weather factors, such as temperature and humidity, is diluted in the overall monthly electricity demand, the forecasting model uses historical electricity consumption data as an integrated factor to obtain future prediction. The proposed approach is applied to a monthly electricity demand time series forecasting model that includes trend and fluctuation series, of which the former describes the trend of the electricity demand series and the latter describes the periodic fluctuation imbedded in the trend. An integrated genetic algorithm and neural network model (GANN) is then trained to forecast the trend series. Given that the fluctuation series demonstrates an oscillatory behavior, we apply Fourier series to fit the fluctuation series. The complete demand model is named GANN–Fourier series. U.S. electricity demand data are used to evaluate the proposed model and to compare the results of applying this model with those of using conventional neural networks. Wang, Hsiao Fan 王小璠 2016 學位論文 ; thesis 40 en_US |
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碩士 === 國立清華大學 === 工業工程與工程管理學系 === 104 === The information from electricity demand forecasting helps energy generation enterprises develop an electricity supply system. This study aims to develop a monthly electricity forecasting model to predict the electricity demand for energy management. Given that the influence of weather factors, such as temperature and humidity, is diluted in the overall monthly electricity demand, the forecasting model uses historical electricity consumption data as an integrated factor to obtain future prediction. The proposed approach is applied to a monthly electricity demand time series forecasting model that includes trend and fluctuation series, of which the former describes the trend of the electricity demand series and the latter describes the periodic fluctuation imbedded in the trend. An integrated genetic algorithm and neural network model (GANN) is then trained to forecast the trend series. Given that the fluctuation series demonstrates an oscillatory behavior, we apply Fourier series to fit the fluctuation series. The complete demand model is named GANN–Fourier series. U.S. electricity demand data are used to evaluate the proposed model and to compare the results of applying this model with those of using conventional neural networks.
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author2 |
Wang, Hsiao Fan |
author_facet |
Wang, Hsiao Fan Lai, Chia Liang 賴佳良 |
author |
Lai, Chia Liang 賴佳良 |
spellingShingle |
Lai, Chia Liang 賴佳良 Application of Soft Computing Techniques with Fourier Series to Forecast Monthly Electricity Demand |
author_sort |
Lai, Chia Liang |
title |
Application of Soft Computing Techniques with Fourier Series to Forecast Monthly Electricity Demand |
title_short |
Application of Soft Computing Techniques with Fourier Series to Forecast Monthly Electricity Demand |
title_full |
Application of Soft Computing Techniques with Fourier Series to Forecast Monthly Electricity Demand |
title_fullStr |
Application of Soft Computing Techniques with Fourier Series to Forecast Monthly Electricity Demand |
title_full_unstemmed |
Application of Soft Computing Techniques with Fourier Series to Forecast Monthly Electricity Demand |
title_sort |
application of soft computing techniques with fourier series to forecast monthly electricity demand |
publishDate |
2016 |
url |
http://ndltd.ncl.edu.tw/handle/23171218166774438081 |
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