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With the recent development of AI and IoT technologies, it is possible for deep learning techniques to achieve more accurate energy generation forecasting results for the PV systems. Difficulties exist for the traditional PV energy generation forecasting method considering external feature variables, such as the seasonality. In this study, we propose a hybrid deep learning method that combines the clustering techniques, convolutional neural network (CNN), long short\u2010term memory (LSTM), and attention mechanism with the wireless sensor network to overcome the existing difficulties of the PV energy generation forecasting problem. The overall proposed method is divided into three stages, namely, clustering, training, and forecasting. In the clustering stage, correlation analysis and self\u2010organizing mapping are employed to select the highest relevant factors in historical data. In the training stage, a convolutional neural network, long short\u2010term memory neural network, and attention mechanism are combined to construct a hybrid deep learning model to perform the forecasting task. In the testing stage, the most appropriate training model is selected based on the month of the testing data. The experimental results showed significantly higher prediction accuracy rates for all time intervals compared to existing methods, including traditional artificial neural networks, long short\u2010term memory neural networks, and an algorithm combining long short\u2010term memory neural network and attention mechanism.<\/jats:p>","DOI":"10.1155\/2021\/9249387","type":"journal-article","created":{"date-parts":[[2021,6,19]],"date-time":"2021-06-19T16:20:08Z","timestamp":1624119608000},"update-policy":"http:\/\/dx.doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":55,"title":["Deep Learning Enhanced Solar Energy Forecasting with AI\u2010Driven IoT"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-9518-5559","authenticated-orcid":false,"given":"Hangxia","family":"Zhou","sequence":"first","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0003-2707-5056","authenticated-orcid":false,"given":"Qian","family":"Liu","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-1611-6636","authenticated-orcid":false,"given":"Ke","family":"Yan","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0003-2254-778X","authenticated-orcid":false,"given":"Yang","family":"Du","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,6,19]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2008.10.006"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1002\/pip.1033"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2020.2980802"},{"key":"e_1_2_9_4_2","doi-asserted-by":"crossref","unstructured":"CaiZ.andHeZ. 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