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This study first presents a comprehensive and comparative review of existing deep learning methods used for smart grid applications such as solar photovoltaic (PV) generation forecasting and power consumption forecasting. In this work, electrical consumption forecasting is long term and will consider smart meter data and socioeconomic and demographic data. Photovoltaic power generation forecasting is short term by considering climatic data such as solar irradiance, temperature, and humidity. Moreover, we have proposed a novel hybrid deep learning method based on multilayer perceptron (MLP), long short\u2010term memory (LSTM), and genetic algorithm (GA). We then simulated all the deep learning methods on a climate and electricity consumption dataset for the city of Douala. Electrical consumption data are collected from smart meters installed at consumers in Douala. Climate data are collected at the climate management center in the city of Douala. The results obtained show the outperformance of the proposed optimized method based on deep learning in the both electrical consumption and PV power generation forecasting and its superiority compared to basic methods of deep learning such as support vector machine (SVM), MLP, recurrent neural network (RNN), and random forest algorithm (RFA).<\/jats:p>","DOI":"10.1155\/2024\/9257508","type":"journal-article","created":{"date-parts":[[2024,4,2]],"date-time":"2024-04-02T16:50:06Z","timestamp":1712076606000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["A Novel Deep Learning\u2010Based Data Analysis Model for Solar Photovoltaic Power Generation and Electrical Consumption Forecasting in the Smart Power Grid"],"prefix":"10.1155","volume":"2024","author":[{"given":"Camille Franklin","family":"Mbey","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1257-8470","authenticated-orcid":false,"given":"Felix Ghislain","family":"Yem Souhe","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7848-3004","authenticated-orcid":false,"given":"Vinny Junior","family":"Foba Kakeu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3783-3343","authenticated-orcid":false,"given":"Alexandre Teplaira","family":"Boum","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,4,2]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.1049\/tje2.12324"},{"key":"e_1_2_10_2_2","doi-asserted-by":"publisher","DOI":"10.3934\/mbe.2022155"},{"key":"e_1_2_10_3_2","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/7978263"},{"key":"e_1_2_10_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3041178"},{"key":"e_1_2_10_5_2","first-page":"74","article-title":"Optimal reliability of a smart grid","volume":"5","author":"Foba V. 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