{"id":"https://openalex.org/W2769809085","doi":"https://doi.org/10.1109/afrcon.2017.8095622","title":"Modelling time-series solar hot water load profile prediction using radial basis function neural network","display_name":"Modelling time-series solar hot water load profile prediction using radial basis function neural network","publication_year":2017,"publication_date":"2017-09-01","ids":{"openalex":"https://openalex.org/W2769809085","doi":"https://doi.org/10.1109/afrcon.2017.8095622","mag":"2769809085"},"language":"en","primary_location":{"id":"doi:10.1109/afrcon.2017.8095622","is_oa":false,"landing_page_url":"https://doi.org/10.1109/afrcon.2017.8095622","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE AFRICON","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/A5001000297","display_name":"Mpumelelo. Mlonzi","orcid":null},"institutions":[{"id":"https://openalex.org/I137616099","display_name":"Tshwane University of Technology","ror":"https://ror.org/037mrss42","country_code":"ZA","type":"education","lineage":["https://openalex.org/I137616099"]}],"countries":["ZA"],"is_corresponding":true,"raw_author_name":"Mpumelelo. Mlonzi","raw_affiliation_strings":["Department of Electrical Engineering, Tshwane University of Technology, Pretoria, South Africa"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Tshwane University of Technology, Pretoria, South Africa","institution_ids":["https://openalex.org/I137616099"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079639580","display_name":"Olawale Popoola","orcid":"https://orcid.org/0000-0002-9980-5241"},"institutions":[{"id":"https://openalex.org/I137616099","display_name":"Tshwane University of Technology","ror":"https://ror.org/037mrss42","country_code":"ZA","type":"education","lineage":["https://openalex.org/I137616099"]}],"countries":["ZA"],"is_corresponding":false,"raw_author_name":"Olawale M Popoola","raw_affiliation_strings":["Department of Electrical Engineering, Tshwane University of Technology, Pretoria, South Africa"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Tshwane University of Technology, Pretoria, South Africa","institution_ids":["https://openalex.org/I137616099"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014845226","display_name":"Josiah L. Munda","orcid":"https://orcid.org/0000-0002-6418-0624"},"institutions":[{"id":"https://openalex.org/I137616099","display_name":"Tshwane University of Technology","ror":"https://ror.org/037mrss42","country_code":"ZA","type":"education","lineage":["https://openalex.org/I137616099"]}],"countries":["ZA"],"is_corresponding":false,"raw_author_name":"Josiah. L Munda","raw_affiliation_strings":["Department of Electrical Engineering, Tshwane University of Technology, Pretoria, South Africa"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Tshwane University of Technology, Pretoria, South Africa","institution_ids":["https://openalex.org/I137616099"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5001000297"],"corresponding_institution_ids":["https://openalex.org/I137616099"],"apc_list":null,"apc_paid":null,"fwci":0.2867,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.60830627,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"5","issue":null,"first_page":"1020","last_page":"1025"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9994000196456909,"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":0.9994000196456909,"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/T11276","display_name":"Solar Radiation and Photovoltaics","score":0.995199978351593,"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/T12368","display_name":"Grey System Theory Applications","score":0.9847000241279602,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.622265636920929},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6212261319160461},{"id":"https://openalex.org/keywords/correlation-coefficient","display_name":"Correlation coefficient","score":0.5358062982559204},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4989454746246338},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.47441667318344116},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.45571446418762207},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.45030343532562256},{"id":"https://openalex.org/keywords/radial-basis-function","display_name":"Radial basis function","score":0.4446045756340027},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.4188503623008728},{"id":"https://openalex.org/keywords/pearson-product-moment-correlation-coefficient","display_name":"Pearson product-moment correlation coefficient","score":0.4187059700489044},{"id":"https://openalex.org/keywords/metering-mode","display_name":"Metering mode","score":0.4169427156448364},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3867957890033722},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3153536915779114},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2131420075893402},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.17919647693634033}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.622265636920929},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6212261319160461},{"id":"https://openalex.org/C2780092901","wikidata":"https://www.wikidata.org/wiki/Q3433612","display_name":"Correlation coefficient","level":2,"score":0.5358062982559204},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4989454746246338},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.47441667318344116},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.45571446418762207},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.45030343532562256},{"id":"https://openalex.org/C98856871","wikidata":"https://www.wikidata.org/wiki/Q1588488","display_name":"Radial basis function","level":3,"score":0.4446045756340027},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.4188503623008728},{"id":"https://openalex.org/C55078378","wikidata":"https://www.wikidata.org/wiki/Q1136628","display_name":"Pearson product-moment correlation coefficient","level":2,"score":0.4187059700489044},{"id":"https://openalex.org/C30905978","wikidata":"https://www.wikidata.org/wiki/Q815598","display_name":"Metering mode","level":2,"score":0.4169427156448364},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3867957890033722},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3153536915779114},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2131420075893402},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.17919647693634033},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/afrcon.2017.8095622","is_oa":false,"landing_page_url":"https://doi.org/10.1109/afrcon.2017.8095622","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE AFRICON","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":12,"referenced_works":["https://openalex.org/W1999828721","https://openalex.org/W2025797714","https://openalex.org/W2036653639","https://openalex.org/W2061152874","https://openalex.org/W2061811978","https://openalex.org/W2087675932","https://openalex.org/W2101716611","https://openalex.org/W2109538555","https://openalex.org/W2127192673","https://openalex.org/W2164320319","https://openalex.org/W6675611861","https://openalex.org/W6678915558"],"related_works":["https://openalex.org/W357196361","https://openalex.org/W2027314909","https://openalex.org/W3096637473","https://openalex.org/W1036938216","https://openalex.org/W3109425891","https://openalex.org/W2113714434","https://openalex.org/W2377792686","https://openalex.org/W4200439127","https://openalex.org/W829658220","https://openalex.org/W2946560178"],"abstract_inverted_index":{"Power":[0],"demands":[1],"problem,":[2],"zero":[3],"reserved":[4],"margin":[5],"and":[6,28,63,90,127,133,164,176,196],"depletion":[7],"in":[8,146,185,192],"coal":[9],"production":[10],"has":[11],"seen":[12],"the":[13,49,77,97,109,143,152,173],"adoption":[14],"of":[15,76,92,99,135,151,162,187],"renewable":[16],"energy":[17,23],"technologies":[18],"(RET)":[19],"increase.":[20],"Unlike":[21],"conventional":[22],"sources,":[24],"RET":[25,62,117],"are":[26,142],"unpredictable":[27],"affected":[29],"by":[30],"non-linear,":[31],"complex":[32],"factors":[33,82,144],"making":[34],"it":[35],"challenging":[36],"to":[37,47,51,107,131,139,156,194],"predict":[38],"their":[39],"daily/monthly":[40],"supply":[41],"contribution.":[42],"Most":[43],"available":[44],"techniques":[45],"seems":[46],"lack":[48],"aptitude":[50],"handle":[52],"contribution":[53,129],"complexities":[54],"(e.g.":[55],"ill-defined":[56],"or":[57,74],"uncertainty":[58],"factors)":[59],"associated":[60,114],"with":[61,115],"its":[64,188],"usage.":[65],"This":[66,94],"thereby":[67],"results":[68,170],"into":[69],"under":[70],"/":[71],"over":[72],"estimation":[73],"prediction":[75,118],"technology":[78],"demand":[79],"outcomes.":[80],"These":[81],"include":[83],"environmental":[84,195],"implication,":[85],"geographical":[86],"location,":[87],"occupant":[88],"behaviour":[89],"time":[91,132,134],"use.":[93],"study":[95],"proposes":[96],"use":[98],"Radial":[100],"Basic":[101],"Function":[102],"neural":[103],"network":[104],"(RBFNN-based":[105],"model)":[106],"improve":[108],"shortcoming":[110],"that":[111],"may":[112],"be":[113],"other":[116],"technique.":[119],"Ambient":[120],"temperature,":[121,123,125],"inlet":[122],"outlet":[124],"irradiance,":[126],"auxiliary":[128],"relatively":[130],"usage":[136],"(TOU)":[137],"relative":[138],"behavioural":[140],"pattern":[141],"considered":[145],"this":[147],"investigation.":[148],"The":[149,169],"accuracy":[150],"model":[153],"was":[154],"subjected":[155],"statistical":[157],"measures":[158],"(correlation":[159],"coefficient,":[160],"coefficient":[161],"determination":[163],"root":[165],"mean":[166],"square":[167],"error).":[168],"obtained":[171],"using":[172],"investigative":[174],"data":[175,178],"metering":[177],"showed":[179],"an":[180],"improved":[181],"error":[182],"prone":[183],"capability":[184],"terms":[186],"learning":[189],"predictive":[190],"skill":[191],"relation":[193],"behavioral":[197],"variableness.":[198]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2018,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
