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Renewable energy generation resources, particularly photovoltaics (PVs), are considered as a clean source of energy but are highly complex, volatile, and intermittent in nature making their forecasting challenging. Thus, a reliable, optimized, and a robust forecasting method deployed at MG objectifies these challenges by providing accurate renewable energy production forecasting and establishing a precise power generation and consumption matching at MG. Furthermore, it ensures effective planning, operation, and acquisition from the main grid in the case of superior or inferior amounts of energy, respectively. Therefore, in this work, we develop an end\u2010to\u2010end hybrid network for automatic PV power forecasting, comprising three basic steps. Firstly, data preprocessing is performed to normalize, remove the outliers, and deal with the missing values prominently. Next, the temporal features are extracted using deep sequential modelling schemes, followed by the extraction of spatial features via convolutional neural networks. These features are then fed to fully connected layers for optimal PV power forecasting. In the third step, the proposed model is evaluated on publicly available PV power generation datasets, where its performance reveals lower error rates when compared to state\u2010of\u2010the\u2010art methods.<\/jats:p>","DOI":"10.1155\/2022\/7040601","type":"journal-article","created":{"date-parts":[[2022,10,5]],"date-time":"2022-10-05T19:35:05Z","timestamp":1664998505000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["A Hybrid Deep Learning\u2010Based Network for Photovoltaic Power Forecasting"],"prefix":"10.1155","volume":"2022","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8163-407X","authenticated-orcid":false,"given":"Altaf","family":"Hussain","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3797-9649","authenticated-orcid":false,"given":"Zulfiqar Ahmad","family":"Khan","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4861-8347","authenticated-orcid":false,"given":"Tanveer","family":"Hussain","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1243-9358","authenticated-orcid":false,"given":"Fath U Min","family":"Ullah","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1936-6785","authenticated-orcid":false,"given":"Seungmin","family":"Rho","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6678-7788","authenticated-orcid":false,"given":"Sung Wook","family":"Baik","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2022,10,5]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2021.122005"},{"key":"e_1_2_10_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2015.04.010"},{"key":"e_1_2_10_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2021.121777"},{"key":"e_1_2_10_4_2","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/8877008"},{"key":"e_1_2_10_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/jsyst.2018.2817584"},{"key":"e_1_2_10_6_2","doi-asserted-by":"publisher","DOI":"10.3390\/electronics9010031"},{"key":"e_1_2_10_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2019.2931547"},{"key":"e_1_2_10_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2021.121962"},{"key":"e_1_2_10_9_2","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/6660115"},{"key":"e_1_2_10_10_2","doi-asserted-by":"publisher","DOI":"10.3390\/en11051067"},{"key":"e_1_2_10_11_2","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/8852186"},{"key":"e_1_2_10_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2021.110992"},{"key":"e_1_2_10_13_2","doi-asserted-by":"crossref","unstructured":"HuangS.et al. 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