{"id":"https://openalex.org/W2988861166","doi":"https://doi.org/10.1145/3360322.3360846","title":"SWaP","display_name":"SWaP","publication_year":2019,"publication_date":"2019-11-05","ids":{"openalex":"https://openalex.org/W2988861166","doi":"https://doi.org/10.1145/3360322.3360846","mag":"2988861166"},"language":"en","primary_location":{"id":"doi:10.1145/3360322.3360846","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3360322.3360846","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation","raw_type":"proceedings-article"},"type":"conference-paper","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/A5061616570","display_name":"Gissella Bejarano","orcid":"https://orcid.org/0000-0002-5624-8553"},"institutions":[{"id":"https://openalex.org/I123946342","display_name":"Binghamton University","ror":"https://ror.org/008rmbt77","country_code":"US","type":"education","lineage":["https://openalex.org/I123946342"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Gissella Bejarano","raw_affiliation_strings":["Computer Science Department, SUNY Binghamton"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science Department, SUNY Binghamton","institution_ids":["https://openalex.org/I123946342"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038818138","display_name":"Adita Kulkarni","orcid":"https://orcid.org/0000-0001-6216-3401"},"institutions":[{"id":"https://openalex.org/I123946342","display_name":"Binghamton University","ror":"https://ror.org/008rmbt77","country_code":"US","type":"education","lineage":["https://openalex.org/I123946342"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Adita Kulkarni","raw_affiliation_strings":["Computer Science Department, SUNY Binghamton"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science Department, SUNY Binghamton","institution_ids":["https://openalex.org/I123946342"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040141466","display_name":"Raushan Raushan","orcid":null},"institutions":[{"id":"https://openalex.org/I123946342","display_name":"Binghamton University","ror":"https://ror.org/008rmbt77","country_code":"US","type":"education","lineage":["https://openalex.org/I123946342"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Raushan Raushan","raw_affiliation_strings":["Computer Science Department, SUNY Binghamton"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science Department, SUNY Binghamton","institution_ids":["https://openalex.org/I123946342"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030680142","display_name":"Anand Seetharam","orcid":"https://orcid.org/0000-0003-4559-7886"},"institutions":[{"id":"https://openalex.org/I123946342","display_name":"Binghamton University","ror":"https://ror.org/008rmbt77","country_code":"US","type":"education","lineage":["https://openalex.org/I123946342"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Anand Seetharam","raw_affiliation_strings":["Computer Science Department, SUNY Binghamton"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science Department, SUNY Binghamton","institution_ids":["https://openalex.org/I123946342"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101696376","display_name":"Arti Ramesh","orcid":"https://orcid.org/0000-0001-8840-8163"},"institutions":[{"id":"https://openalex.org/I123946342","display_name":"Binghamton University","ror":"https://ror.org/008rmbt77","country_code":"US","type":"education","lineage":["https://openalex.org/I123946342"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Arti Ramesh","raw_affiliation_strings":["Computer Science Department, SUNY Binghamton"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science Department, SUNY Binghamton","institution_ids":["https://openalex.org/I123946342"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I123946342"],"apc_list":null,"apc_paid":null,"fwci":3.7008,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.93648345,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"233","last_page":"242"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11220","display_name":"Water Systems and Optimization","score":0.9975000023841858,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural 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/T11220","display_name":"Water Systems and Optimization","score":0.9975000023841858,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural 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/T10969","display_name":"Water resources management and optimization","score":0.9962999820709229,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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/T12697","display_name":"Water Quality Monitoring Technologies","score":0.9941999912261963,"subfield":{"id":"https://openalex.org/subfields/2312","display_name":"Water Science and Technology"},"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":0.7784028053283691},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.627920925617218},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.6016461253166199},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.5945273637771606},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5631982684135437},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5566700100898743},{"id":"https://openalex.org/keywords/autoregressive-integrated-moving-average","display_name":"Autoregressive integrated moving average","score":0.544219970703125},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.5373396277427673},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.4718480110168457},{"id":"https://openalex.org/keywords/conditional-random-field","display_name":"Conditional random field","score":0.47149723768234253},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4451019763946533},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4346426725387573},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.22877934575080872},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.20777347683906555}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7784028053283691},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.627920925617218},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.6016461253166199},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.5945273637771606},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5631982684135437},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5566700100898743},{"id":"https://openalex.org/C24338571","wikidata":"https://www.wikidata.org/wiki/Q2566298","display_name":"Autoregressive integrated moving average","level":3,"score":0.544219970703125},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.5373396277427673},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.4718480110168457},{"id":"https://openalex.org/C152565575","wikidata":"https://www.wikidata.org/wiki/Q1124538","display_name":"Conditional random field","level":2,"score":0.47149723768234253},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4451019763946533},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4346426725387573},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.22877934575080872},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.20777347683906555},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3360322.3360846","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3360322.3360846","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1262302848","https://openalex.org/W1953942032","https://openalex.org/W2069143585","https://openalex.org/W2116150816","https://openalex.org/W2147880316","https://openalex.org/W2194828942","https://openalex.org/W2346686637","https://openalex.org/W2403231150","https://openalex.org/W2515357728","https://openalex.org/W2557283755","https://openalex.org/W2585773698","https://openalex.org/W2607541615","https://openalex.org/W2620804815","https://openalex.org/W2755872419","https://openalex.org/W2761113961","https://openalex.org/W2780684356","https://openalex.org/W2802770308","https://openalex.org/W2883388482","https://openalex.org/W2885442183","https://openalex.org/W2890820635","https://openalex.org/W2901513671","https://openalex.org/W2902257695","https://openalex.org/W2903254051","https://openalex.org/W2903507950","https://openalex.org/W2903902573","https://openalex.org/W2904391776","https://openalex.org/W2904850375","https://openalex.org/W2914265404","https://openalex.org/W2950635152"],"related_works":["https://openalex.org/W3135881084","https://openalex.org/W2380590035","https://openalex.org/W2351712633","https://openalex.org/W4389116644","https://openalex.org/W2153315159","https://openalex.org/W4388984322","https://openalex.org/W2356597680","https://openalex.org/W2163278254","https://openalex.org/W155708904","https://openalex.org/W1574213390"],"abstract_inverted_index":{"Accurately":[0],"predicting":[1],"water":[2,16,42,91,99,128],"consumption":[3,43,92,100,129],"in":[4,53,64,89,106,130,151,160,253],"residential":[5],"and":[6,18,60,72,110,116,157,168,176,181,203,239,247],"commercial":[7],"buildings":[8,105,150],"is":[9],"essential":[10],"for":[11,19,23,148,260],"identifying":[12],"possible":[13],"leaks,":[14],"minimizing":[15],"wastage,":[17],"paving":[20],"the":[21,90,114,134,154,174,201,206,210,223,227],"way":[22],"a":[24,33,107],"sustainable":[25],"future.":[26],"In":[27],"this":[28,50],"paper,":[29],"we":[30,55,220],"present":[31],"SWaP,":[32,54],"Smart":[34],"Water":[35],"Prediction":[36],"system":[37,97],"that":[38,112,190,222],"predicts":[39],"future":[40,126],"hourly":[41,127],"based":[44,78,118,216,229],"on":[45,98,184,217],"historical":[46],"data.":[47,93],"To":[48],"perform":[49],"prediction":[51,146,213],"task,":[52],"design":[56],"discriminative":[57],"probabilistic":[58],"graphical":[59],"deep":[61,79,119,230],"learning":[62,231],"models,":[63,84],"particular,":[65],"sparse":[66],"Gaussian":[67],"Conditional":[68],"Random":[69],"Fields":[70],"(GCRFs)":[71],"Long":[73],"Short":[74],"Term":[75],"Memory":[76],"(LSTM)":[77],"Recurrent":[80],"Neural":[81],"Network":[82],"(RNN)":[83],"to":[85,123,153,237],"successfully":[86],"encode":[87],"dependencies":[88],"We":[94,187],"evaluate":[95],"our":[96,192,218],"data":[101,139],"collected":[102],"from":[103],"multiple":[104],"university":[108],"campus":[109],"demonstrate":[111,189],"both":[113],"GCRF":[115,175,224,251],"LSTM":[117,177,228],"models":[120,178,193,252],"are":[121],"able":[122],"accurately":[124],"predict":[125],"advance":[131],"using":[132],"just":[133],"last":[135],"24":[136],"hours":[137],"of":[138,162,200,205,250],"at":[140,241],"test":[141,242],"time.":[142,243],"SWaP":[143,254],"achieves":[144],"superior":[145],"performance":[147],"all":[149],"comparison":[152],"linear":[155],"regression":[156],"ARIMA":[158],"baselines":[159],"terms":[161],"Root":[163],"Mean":[164,169],"Squared":[165],"Error":[166,171],"(RMSE)":[167],"Absolute":[170],"(MAE),":[172],"with":[173,194],"providing":[179],"50%":[180],"44%":[182],"improvements":[183],"average,":[185],"respectively.":[186],"also":[188],"augmenting":[191],"temporal":[195],"features":[196],"such":[197],"as":[198],"time":[199],"day":[202,204],"week":[207],"can":[208],"improve":[209],"overall":[211],"average":[212],"performance.":[214],"Additionally,":[215],"evaluation,":[219],"observe":[221],"model":[225],"outperforms":[226],"model,":[232],"while":[233],"simultaneously":[234],"being":[235],"faster":[236],"train":[238],"execute":[240],"The":[244],"computationally":[245],"efficient":[246],"interpretable":[248],"nature":[249],"make":[255],"them":[256],"an":[257],"ideal":[258],"choice":[259],"practical":[261],"deployment.":[262]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2019-11-22T00:00:00"}
