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    ASIA unversity > 管理學院 > 經營管理學系  > 期刊論文 >  Item 310904400/112395


    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/112395


    Title: DEEP BELIEF NETWORK WITH SEASONAL DECOMPOSITION FOR SOLAR POWER OUTPUT FORECASTING
    Authors: Ching-Hsin Wang;Kuo-Ping Lin;Yu-Ming Lu;Chih-Feng Wu
    Contributors: 經營管理學系
    Date: 2019-04
    Issue Date: 2019-11-08 11:05:27 (UTC+8)
    Abstract: Solar power is a type of renewable energy system that uses solar energy to produce electricity, and is regarded as one of the most important power sources in Taiwan. Since sunshine duration affects the amount of energy that can be generated by a solar power, the seasons of the year are important factors that should be considered for accurate solar power prediction. In the last decade, the use of artificial intelligence for forecasting systems have been quite popular, and the deep belief network (DBN) models started getting more attention. In this study, a seasonal deep belief network (SDBN) was developed to forecast monthly solar power output data. The SDBN was constructed by combining seasonal decomposition method and DBN. Further, this study used monthly solar power output data from the Taiwan Power Company. The results indicated that the proposed forecasting system demonstrated a superior performance in terms of forecasting accuracy. Also, the performance of autoregressive integrated moving average (ARIMA), generalized regression neural network (GRNN), and DBN obtained from a separate study were compared to the performance of the proposed SDBN model and showed that the latter was better than the other three models. Thus, the SDBN model can be used as an alternative method for monthly solar power output data forecasting.
    Relation: International Journal of Reliability; Quality and Safety Engineering
    Appears in Collections:[經營管理學系 ] 期刊論文

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