2024-03-29T17:37:48Zhttps://ipsj.ixsq.nii.ac.jp/ej/?action=repository_oaipmhoai:ipsj.ixsq.nii.ac.jp:001576372024-03-29T05:26:34Z01164:04402:08598:08599
寒冷地住宅のコージェネレーションシステム利用に向けた電力・給湯需要予測と評価Electricity and Hot Water Load Prediction Method and Evaluation for Cogeneration System Applied to Cold Region Householdsjpnhttp://id.nii.ac.jp/1001/00157603/Technical Reporthttps://ipsj.ixsq.nii.ac.jp/ej/?action=repository_action_common_download&item_id=157637&item_no=1&attribute_id=1&file_no=1Copyright (c) 2016 by the Information Processing Society of Japan北海道大学(株)北海道ガス技術開発研究所(株)北海道ガス技術開発研究所はこだて未来大学北海道大学今野, 陽子武田, 清賢横川, 誠鈴木, 恵二川村, 秀憲住宅向けガスコージェネレーションシステムは,家庭で発電して電気をつくり,同時発生する熱を回収して給湯,暖房などに利用する.消費量に合わせた学習運転機能を有し,家庭の日々の電力と給湯を予測して発電する.しかし現在の学習運転機能は,寒冷地の需要に対し最適化されていない.本研究では,コージェネレーションシステムの寒冷地での利用に向けて,電力と給湯の需要予測手法について ANN を用いて検討し,システムの運転効率を評価する.A residential cogeneration system (CGS) has attracted attention for its high energy saving and environmental performance, and systems using fuel cells have recently been introduced into the market in Japan. It is useful for reductions of CO2 emission because the total efficiency of CGSs can reach about 80%. This CGS system generates electricity at household. It collects heat to coincide with generation of electricity and make hot-water and the hot-water is stocked in the tank. The system determines an optimal operation time everyday for saving energy. For this control, the system has a function to predict the daily load of electricity and hot-water, and generates electricity according to the prediction. However, the prediction method is not optimized for cold region. This study examines the prediction method of load of electricity and hot-water supply by applying an artificial neural network (ANN) for 20 households in Hokkaido. Then it evaluates the operating performance by using CGS operating simulator.AA11135936研究報告知能システム(ICS)2016-ICS-1824182016-02-242188-885x2016-02-23