{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T13:31:47Z","timestamp":1786627907404,"version":"build-2736575974"},"reference-count":0,"publisher":"European Alliance for Innovation n.o.","license":[{"start":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T00:00:00Z","timestamp":1786579200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-sa\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["EAI Endorsed Trans Energy Web"],"abstract":"<jats:p>High renewable penetration increases source-load uncertainty and challenges the stable operation of smart grids. This paper proposes a prediction-driven framework that integrates CNN-Attention-based joint renewable generation and load forecasting with rolling optimization of a battery\u2013supercapacitor hybrid energy storage system. The main innovation lies in coupling source-load forecasting with hybrid energy storage dispatch, so that predicted renewable generation and load trajectories can directly support rolling scheduling decisions. Multi-source temporal inputs, including wind power, photovoltaic power, load demand, meteorological variables, electricity price, and periodic time encodings, are used to characterize source-load coupling. One-dimensional convolution extracts local fluctuation features, while temporal attention assigns adaptive weights to informative historical periods. In the dispatch stage, the battery mainly undertakes low-frequency energy balancing, whereas the supercapacitor suppresses high-frequency power fluctuations. The optimization objective jointly considers grid purchase cost, storage degradation cost, renewable curtailment penalty, load shedding penalty, and grid power fluctuation penalty. Experiments were conducted on one-year operational data from a regional wind\u2013photovoltaic\u2013storage microgrid, with a 15-min sampling interval and 35,040 time steps. Compared with LSTM, GRU, CNN, CNN-LSTM, and Transformer baselines, the proposed model achieved the lowest forecasting errors, with MAE\/RMSE\/MAPE of 6.692 kW\/8.322 kW\/3.288% for renewable generation and 3.747 kW\/4.490 kW\/0.406% for load forecasting. At the dispatch level, the proposed strategy reduced the total operating cost index to 360, decreased grid power fluctuation to 3.4%, improved renewable accommodation to 90.6%, and lowered curtailment to 2.8%.<\/jats:p>","DOI":"10.4108\/ew.13260","type":"journal-article","created":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T12:44:44Z","timestamp":1786625084000},"source":"Crossref","is-referenced-by-count":0,"title":["CNN-Attention-Based Renewable Energy Generation and Load Forecasting Method for Stable Operation of Smart Grids"],"prefix":"10.4108","volume":"13","author":[{"given":"Wei","family":"Luo","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/0387aje64","id-type":"ROR","asserted-by":"publisher"}],"name":"Nanjing University of Science and Technology ZiJin College"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2587","published-online":{"date-parts":[[2026,8,13]]},"container-title":["EAI Endorsed Transactions on Energy Web"],"original-title":[],"link":[{"URL":"https:\/\/publications.eai.eu\/index.php\/ew\/article\/download\/13260\/4234","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/publications.eai.eu\/index.php\/ew\/article\/download\/13260\/4234","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T12:44:45Z","timestamp":1786625085000},"score":1,"resource":{"primary":{"URL":"https:\/\/publications.eai.eu\/index.php\/ew\/article\/view\/13260"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,13]]},"references-count":0,"URL":"https:\/\/doi.org\/10.4108\/ew.13260","relation":{},"ISSN":["2032-944X"],"issn-type":[{"value":"2032-944X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,13]]}}}