{"id":"doi:10.1016/j.apenergy.2020.114683","kind":"article","title":"Building thermal load prediction through shallow machine learning and deep learning","authors":["Zhe Wang","Tianzhen Hong","Mary Ann Piette"],"author_count":3,"year":2020,"published":"2020-02-19","venue":"Applied Energy","cited_by":507,"doi":"10.1016/j.apenergy.2020.114683","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1016/j.apenergy.2020.114683","archive_url":"https://space-kitty.com/papers/doi:10.1016/j.apenergy.2020.114683","updated":null,"pmid":null,"pmcid":null,"openalex_id":"W3008571545","pdf_url":null,"abstract":null,"abstract_license":null,"license":null,"is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Building Energy and Comfort Optimization"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0).","topics":[{"slug":"ai-weather-climate","name":"AI Weather & Climate Modeling","domain_slug":"energy"}],"cited_in_dossiers":[]}