{"id":"https://openalex.org/W7125652235","doi":"https://doi.org/10.1186/s42162-025-00613-6","title":"nZEB beyond prediction in smart built environments: formalising engineering knowledge through modular explainable machine learning","display_name":"nZEB beyond prediction in smart built environments: formalising engineering knowledge through modular explainable machine learning","publication_year":2026,"publication_date":"2026-01-25","ids":{"openalex":"https://openalex.org/W7125652235","doi":"https://doi.org/10.1186/s42162-025-00613-6"},"language":"en","primary_location":{"id":"doi:10.1186/s42162-025-00613-6","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s42162-025-00613-6","pdf_url":null,"source":{"id":"https://openalex.org/S3035173479","display_name":"Energy Informatics","issn_l":"2520-8942","issn":["2520-8942"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Energy Informatics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1186/s42162-025-00613-6","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5121785017","display_name":"Nuno Soares Domingues","orcid":null},"institutions":[{"id":"https://openalex.org/I203847022","display_name":"Instituto Polit\u00e9cnico de Lisboa","ror":"https://ror.org/04ea70f07","country_code":"PT","type":"education","lineage":["https://openalex.org/I203847022"]}],"countries":["PT"],"is_corresponding":true,"raw_author_name":"Nuno Soares Domingues","raw_affiliation_strings":["Instituto Polit\u00e9cnico de Lisboa, Instituto Superior de Engenharia de Lisboa, Rua Conselheiro Em\u00eddio Navarro, 1, Lisbon, 1959-007, Portugal"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Instituto Polit\u00e9cnico de Lisboa, Instituto Superior de Engenharia de Lisboa, Rua Conselheiro Em\u00eddio Navarro, 1, Lisbon, 1959-007, Portugal","institution_ids":["https://openalex.org/I203847022"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5121785017"],"corresponding_institution_ids":["https://openalex.org/I203847022"],"apc_list":{"value":790,"currency":"GBP","value_usd":969},"apc_paid":{"value":790,"currency":"GBP","value_usd":969},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.12799859,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"9","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11918","display_name":"Forecasting Techniques and Applications","score":0.11710000038146973,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11918","display_name":"Forecasting Techniques and Applications","score":0.11710000038146973,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.10300000011920929,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.07209999859333038,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/modular-design","display_name":"Modular design","score":0.6395999789237976},{"id":"https://openalex.org/keywords/workflow","display_name":"Workflow","score":0.5443000197410583},{"id":"https://openalex.org/keywords/automation","display_name":"Automation","score":0.5297999978065491},{"id":"https://openalex.org/keywords/schema","display_name":"Schema (genetic algorithms)","score":0.5271000266075134},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5012999773025513},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.4779999852180481},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.4494999945163727},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.4383000135421753},{"id":"https://openalex.org/keywords/uncertainty-quantification","display_name":"Uncertainty quantification","score":0.4219000041484833}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7057999968528748},{"id":"https://openalex.org/C101468663","wikidata":"https://www.wikidata.org/wiki/Q1620158","display_name":"Modular design","level":2,"score":0.6395999789237976},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5633000135421753},{"id":"https://openalex.org/C177212765","wikidata":"https://www.wikidata.org/wiki/Q627335","display_name":"Workflow","level":2,"score":0.5443000197410583},{"id":"https://openalex.org/C115901376","wikidata":"https://www.wikidata.org/wiki/Q184199","display_name":"Automation","level":2,"score":0.5297999978065491},{"id":"https://openalex.org/C52146309","wikidata":"https://www.wikidata.org/wiki/Q7431116","display_name":"Schema (genetic algorithms)","level":2,"score":0.5271000266075134},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5012999773025513},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47999998927116394},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.4779999852180481},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.4494999945163727},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.4383000135421753},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.4219000041484833},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.40540000796318054},{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.34860000014305115},{"id":"https://openalex.org/C115903868","wikidata":"https://www.wikidata.org/wiki/Q80993","display_name":"Software engineering","level":1,"score":0.3411000072956085},{"id":"https://openalex.org/C72414096","wikidata":"https://www.wikidata.org/wiki/Q1367461","display_name":"Mass customization","level":3,"score":0.31690001487731934},{"id":"https://openalex.org/C161301231","wikidata":"https://www.wikidata.org/wiki/Q3478658","display_name":"Knowledge representation and reasoning","level":2,"score":0.30239999294281006},{"id":"https://openalex.org/C83931994","wikidata":"https://www.wikidata.org/wiki/Q1149653","display_name":"Building automation","level":2,"score":0.3021000027656555},{"id":"https://openalex.org/C2780416260","wikidata":"https://www.wikidata.org/wiki/Q2063","display_name":"JSON","level":2,"score":0.2915000021457672},{"id":"https://openalex.org/C58328972","wikidata":"https://www.wikidata.org/wiki/Q184609","display_name":"Expert system","level":2,"score":0.29109999537467957},{"id":"https://openalex.org/C143299363","wikidata":"https://www.wikidata.org/wiki/Q900584","display_name":"Attribution","level":2,"score":0.288100004196167},{"id":"https://openalex.org/C195344581","wikidata":"https://www.wikidata.org/wiki/Q2555318","display_name":"Automated reasoning","level":2,"score":0.27239999175071716},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.271699994802475},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.27149999141693115},{"id":"https://openalex.org/C46743427","wikidata":"https://www.wikidata.org/wiki/Q1341685","display_name":"Inference engine","level":3,"score":0.26660001277923584},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2621000111103058},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.2538999915122986}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1186/s42162-025-00613-6","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s42162-025-00613-6","pdf_url":null,"source":{"id":"https://openalex.org/S3035173479","display_name":"Energy Informatics","issn_l":"2520-8942","issn":["2520-8942"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Energy Informatics","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:7469fb66cefc4b3a84196f900b7d1c44","is_oa":true,"landing_page_url":"https://doaj.org/article/7469fb66cefc4b3a84196f900b7d1c44","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Energy Informatics, Vol 9, Iss 1 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1186/s42162-025-00613-6","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s42162-025-00613-6","pdf_url":null,"source":{"id":"https://openalex.org/S3035173479","display_name":"Energy Informatics","issn_l":"2520-8942","issn":["2520-8942"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Energy Informatics","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1980893177","https://openalex.org/W1993025628","https://openalex.org/W2218653053","https://openalex.org/W2605614336","https://openalex.org/W2754029504","https://openalex.org/W2783743057","https://openalex.org/W2795587401","https://openalex.org/W2948860915","https://openalex.org/W2972137370","https://openalex.org/W3013120860","https://openalex.org/W3038790237","https://openalex.org/W3082664913","https://openalex.org/W3150015576","https://openalex.org/W3177153475","https://openalex.org/W3191624987","https://openalex.org/W4210390777","https://openalex.org/W4220686148","https://openalex.org/W4221053610","https://openalex.org/W4295941072","https://openalex.org/W4306252084","https://openalex.org/W4313578093","https://openalex.org/W4412631460"],"related_works":[],"abstract_inverted_index":{"This":[0],"paper":[1],"demonstrates":[2],"how":[3,154],"explainable":[4],"machine":[5],"learning":[6],"(XAI)":[7],"can":[8,138],"be":[9,139,158],"operationalised":[10],"as":[11,58,141],"a":[12,26,65,148],"methodological":[13],"pathway":[14],"for":[15,54],"formalising":[16],"engineering":[17],"knowledge":[18,49],"from":[19],"high-frequency":[20],"building":[21],"operational":[22],"data.":[23],"We":[24,144],"propose":[25],"modular":[27],"pipeline":[28],"that":[29,106,137],"combines":[30],"feature":[31],"engineering,":[32],"ensemble":[33,125],"and":[34,39,61,88,94,102,111,117,120,152,163],"sequence":[35],"learners,":[36],"SHAP":[37],"attribution":[38,104],"uncertainty":[40,121],"quantification":[41],"to":[42],"convert":[43],"raw":[44],"sensor":[45],"streams":[46],"into":[47],"machine-readable":[48],"artefacts":[50,156],"(JSON":[51],"schema)":[52],"suitable":[53],"automation":[55],"workflows":[56],"such":[57],"fault":[59],"detection":[60],"demand":[62],"response.":[63],"Using":[64],"monitored":[66],"nearly":[67],"Zero-Energy":[68],"Building":[69],"(nZEB)":[70],"in":[71],"Lisbon":[72],"(12":[73],"months,":[74],"5-minute":[75],"resolution),":[76],"we":[77],"(i)":[78],"report":[79],"model":[80],"performance":[81],"(LightGBM,":[82],"Random":[83],"Forest,":[84],"SVR,":[85],"Linear":[86],"Regression,":[87],"LSTM)":[89],"under":[90],"time-aware":[91],"70/15/15":[92],"split":[93],"5-fold":[95],"temporal":[96],"cross-validation;":[97],"(ii)":[98],"present":[99],"SHAP-based":[100],"global":[101],"local":[103],"analyses":[105],"identify":[107],"stable":[108],"seasonal":[109],"drivers;":[110],"(iii)":[112],"provide":[113],"computational":[114],"cost":[115],"(training":[116],"inference":[118],"times)":[119],"quantification.":[122],"Results":[123],"show":[124],"models":[126],"achieve":[127],"superior":[128],"short-term":[129],"forecasting":[130],"accuracy":[131],"while":[132],"producing":[133],"consistent,":[134],"actionable":[135],"attributions":[136],"encoded":[140],"reusable":[142],"artefacts.":[143],"close":[145],"by":[146],"describing":[147],"JSON":[149],"artefact":[150],"schema":[151],"outlining":[153],"these":[155],"could":[157],"integrated":[159],"within":[160],"digital":[161],"twins":[162],"supervisory":[164],"control":[165],"systems.":[166]},"counts_by_year":[],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2026-01-26T00:00:00"}