Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic A...
Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches
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Basel: MDPI AG
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English
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Basel: MDPI AG
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Background: With the development of smart grids, accurate electric load forecasting has become increasingly important as it can help power companies in better load scheduling and reduce excessive electricity production. However, developing and selecting accurate time series models is a challenging task as this requires training several different mo...
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Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches
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TN_cdi_doaj_primary_oai_doaj_org_article_ad514c7fb4344cfc9bf90f8ddf302d07
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_ad514c7fb4344cfc9bf90f8ddf302d07
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ISSN
1996-1073
E-ISSN
1996-1073
DOI
10.3390/en11071636
