{"id":"https://openalex.org/W2991717426","doi":"https://doi.org/10.1145/3368691.3368701","title":"Data-driven modelling for building energy prediction using regression-based analysis","display_name":"Data-driven modelling for building energy prediction using regression-based analysis","publication_year":2019,"publication_date":"2019-12-02","ids":{"openalex":"https://openalex.org/W2991717426","doi":"https://doi.org/10.1145/3368691.3368701","mag":"2991717426"},"language":"en","primary_location":{"id":"doi:10.1145/3368691.3368701","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3368691.3368701","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Second International Conference on Data Science, E-Learning and Information Systems","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5038344738","display_name":"Ahmed Ghareeb","orcid":"https://orcid.org/0000-0003-4054-809X"},"institutions":[{"id":"https://openalex.org/I102149020","display_name":"University of North Carolina at Charlotte","ror":"https://ror.org/04dawnj30","country_code":"US","type":"education","lineage":["https://openalex.org/I102149020"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ahmed Ghareeb","raw_affiliation_strings":["University of North Carolina at Charlotte"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of North Carolina at Charlotte","institution_ids":["https://openalex.org/I102149020"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100331965","display_name":"Weimin Wang","orcid":"https://orcid.org/0000-0003-2521-9095"},"institutions":[{"id":"https://openalex.org/I102149020","display_name":"University of North Carolina at Charlotte","ror":"https://ror.org/04dawnj30","country_code":"US","type":"education","lineage":["https://openalex.org/I102149020"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Weimin Wang","raw_affiliation_strings":["University of North Carolina at Charlotte"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of North Carolina at Charlotte","institution_ids":["https://openalex.org/I102149020"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014198134","display_name":"Kevin P. Hallinan","orcid":"https://orcid.org/0000-0002-0305-4886"},"institutions":[{"id":"https://openalex.org/I127591826","display_name":"University of Dayton","ror":"https://ror.org/021v3qy27","country_code":"US","type":"education","lineage":["https://openalex.org/I127591826"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kevin Hallinan","raw_affiliation_strings":["University of Dayton"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Dayton","institution_ids":["https://openalex.org/I127591826"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.4844,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.66915996,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10121","display_name":"Building Energy and Comfort Optimization","score":0.9987999796867371,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12120","display_name":"Air Quality Monitoring and Forecasting","score":0.9866999983787537,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6015475988388062},{"id":"https://openalex.org/keywords/covariate","display_name":"Covariate","score":0.5977143049240112},{"id":"https://openalex.org/keywords/lasso","display_name":"Lasso (programming language)","score":0.5882381200790405},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.5473425984382629},{"id":"https://openalex.org/keywords/imputation","display_name":"Imputation (statistics)","score":0.5115429162979126},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.5042680501937866},{"id":"https://openalex.org/keywords/ordinary-least-squares","display_name":"Ordinary least squares","score":0.5008430480957031},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.48333117365837097},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.4765249490737915},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.46764087677001953},{"id":"https://openalex.org/keywords/linear-regression","display_name":"Linear regression","score":0.45966824889183044},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.42318448424339294},{"id":"https://openalex.org/keywords/partial-least-squares-regression","display_name":"Partial least squares regression","score":0.4113861322402954},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3489232659339905},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3442367613315582},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.32028335332870483},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.19037160277366638}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6015475988388062},{"id":"https://openalex.org/C119043178","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Covariate","level":2,"score":0.5977143049240112},{"id":"https://openalex.org/C37616216","wikidata":"https://www.wikidata.org/wiki/Q3218363","display_name":"Lasso (programming language)","level":2,"score":0.5882381200790405},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.5473425984382629},{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.5115429162979126},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.5042680501937866},{"id":"https://openalex.org/C99656134","wikidata":"https://www.wikidata.org/wiki/Q2912993","display_name":"Ordinary least squares","level":2,"score":0.5008430480957031},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.48333117365837097},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.4765249490737915},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.46764087677001953},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.45966824889183044},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.42318448424339294},{"id":"https://openalex.org/C22354355","wikidata":"https://www.wikidata.org/wiki/Q422009","display_name":"Partial least squares regression","level":2,"score":0.4113861322402954},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3489232659339905},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3442367613315582},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.32028335332870483},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.19037160277366638},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3368691.3368701","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3368691.3368701","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Second International Conference on Data Science, E-Learning and Information Systems","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7900000214576721,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W271394661","https://openalex.org/W616486760","https://openalex.org/W1815414460","https://openalex.org/W1981457167","https://openalex.org/W1995919245","https://openalex.org/W2033815293","https://openalex.org/W2038879577","https://openalex.org/W2044186388","https://openalex.org/W2071151783","https://openalex.org/W2081770709","https://openalex.org/W2140490477","https://openalex.org/W2163167422","https://openalex.org/W2284910918","https://openalex.org/W2313169588","https://openalex.org/W2341164870","https://openalex.org/W2574209248","https://openalex.org/W2602283702","https://openalex.org/W2607521821","https://openalex.org/W2735345949","https://openalex.org/W2754029504","https://openalex.org/W2786918196","https://openalex.org/W2786998222","https://openalex.org/W2787790374","https://openalex.org/W2797373921","https://openalex.org/W2800369870","https://openalex.org/W2885301212","https://openalex.org/W2885683779","https://openalex.org/W2894665398","https://openalex.org/W2898623405","https://openalex.org/W2898776936","https://openalex.org/W2899074204","https://openalex.org/W2909436466","https://openalex.org/W3126081667"],"related_works":["https://openalex.org/W1989457222","https://openalex.org/W2158863190","https://openalex.org/W4256576576","https://openalex.org/W4211215373","https://openalex.org/W3217094455","https://openalex.org/W3090384609","https://openalex.org/W3194013178","https://openalex.org/W2989589450","https://openalex.org/W2770989956","https://openalex.org/W4399653172"],"abstract_inverted_index":{"The":[0,104],"prediction":[1],"of":[2,22,28,32,85],"short-term":[3],"building":[4,13],"electric":[5],"loads":[6],"is":[7,108],"the":[8,33,83,86,90,101,123],"essential":[9],"basis":[10],"for":[11,66,110],"many":[12],"energy":[14,64],"management":[15],"tasks,":[16],"such":[17],"as":[18],"detection":[19],"and":[20,26,40,76,88,93,116,119],"diagnosis":[21],"faults,":[23],"demand-side":[24],"management,":[25],"optimization":[27],"control":[29],"systems.":[30],"Because":[31],"fair":[34],"precision,":[35],"comparatively":[36],"easy":[37],"to":[38,45,61,80,98],"implement":[39],"interpret":[41],"regression-based":[42],"models":[43,126],"related":[44],"other":[46],"techniques,":[47],"a":[48],"straightforward":[49],"generalized":[50],"linear":[51],"regression":[52,91],"model":[53],"with":[54,70,82,100],"an":[55],"ordinary":[56],"least":[57],"squares":[58],"estimator":[59],"used":[60,79,97],"predict":[62],"day-ahead":[63],"demand":[65],"U.S.":[67],"institutional":[68],"buildings":[69],"15-minute":[71],"intervals.":[72],"Double-Lasso":[73],"variable":[74],"selection":[75],"regularization":[77],"approach":[78,107],"deal":[81,99],"association":[84],"covariates":[87],"penalize":[89],"coefficients,":[92],"artificial":[94],"imputation":[95],"method":[96],"missing":[102],"data.":[103],"proposed":[105],"data-driven":[106],"validated":[109],"3":[111],"months":[112],"out-of-sample":[113],"validation":[114],"dataset":[115],"showed":[117],"promising":[118],"acceptable":[120],"outcomes":[121],"among":[122],"advanced":[124],"statistical":[125],"by":[127],"achieving":[128],"approximately":[129],"5%":[130],"MAPE.":[131]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}