著者

飯坂 達也 松井 哲郎 福山 良和

出版者

一般社団法人 電気学会

雑誌

電気学会論文誌B(電力・エネルギー部門誌) (ISSN:03854213)

巻号頁・発行日

vol.124, no.3, pp.347-354, 2004 (Released:2004-06-01)

参考文献数

22

被引用文献数

8 13

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This paper presents a daily peak load forecasting method using an analyzable structured neural network (ASNN) in order to explain forecasting reasons. In this paper, we propose a new training method for ASNN in order to explain forecasting reason more properly than the conventional training method. ASNN consists of two types of hidden units. One type of hidden units has connecting weights between the hidden units and only one group of related input units. Another one has connecting weights between the hidden units and all input units. The former type of hidden units allows to explain forecasting reasons. The latter type of hidden units ensures the forecasting performance. The proposed training method make the former type of hidden units train only independent relations between the input factors and output, and make the latter type of hidden units train only complicated interactions between input factors. The effectiveness of the proposed neural network is shown using actual daily peak load. ASNN trained by the proposed method can explain forecasting reasons more properly than ASNN trained by the conventional method. Moreover, the proposed neural network can forecast daily peak load more accurately than conventional neural network trained by the back propagation algorithm.

著者

飯坂 達也 神通川 亨 近藤 英幸 中西 要祐 福山 良和 森 啓之

出版者

The Institute of Electrical Engineers of Japan

雑誌

電気学会論文誌. C, 電子・情報・システム部門誌 = The transactions of the Institute of Electrical Engineers of Japan. C, A publication of Electronics, Information and Systems Society (ISSN:03854221)

巻号頁・発行日

vol.131, no.10, pp.1672-1678, 2011-10-01

被引用文献数

2

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This paper describes a wind power forecasting method and its confidence interval estimation. Recently, flat control of wind power generators by various batteries is required. For the flat control, accurate wind power forecasts and their error confidence intervals are needed. In this paper, wind speed forecasts are calculated by regression models using GPV (Grid Point Vale) weather forecasts. The forecasts are adjusted by the fuzzy inference using the latest errors. The wind power forecasts are translated from the wind speed forecasts using two power-curves. The power-curves are selected or combined by fuzzy inference depending on wind direction. The error confidence interval models are generated for each forecasting target time. Each confidence interval is combined by the other fuzzy inference.<br>The proposed methods are applied to actual wind power generators, and found that forecasting errors are better than the conventional methods. The almost all of forecasts can be within error confidence intervals estimated by the proposed method. The results show the effectiveness of the proposed methods.