{"id":"https://openalex.org/W7214735421","doi":"https://doi.org/10.56201/ijcsmt.vol.12.no1.2026.pg194.215","title":"Visual-Aware Solar Forecasting: Attention-Driven Spatiotemporal Learning from Sky Images for Grid-Responsive PV Prediction","display_name":"Visual-Aware Solar Forecasting: Attention-Driven Spatiotemporal Learning from Sky Images for Grid-Responsive PV Prediction","publication_year":2026,"publication_date":"2026-09-29","ids":{"openalex":"https://openalex.org/W7214735421","doi":"https://doi.org/10.56201/ijcsmt.vol.12.no1.2026.pg194.215"},"language":"en","primary_location":{"id":"doi:10.56201/ijcsmt.vol.12.no1.2026.pg194.215","doi":"https://doi.org/10.56201/ijcsmt.vol.12.no1.2026.pg194.215","is_oa":true,"landing_page_url":"https://doi.org/10.56201/ijcsmt.vol.12.no1.2026.pg194.215","pdf_url":"https://iiardjournals.org/get/IJCSMT/VOL. 12 NO. 1 2026/Visual-Aware Solar Forecasting 194-215.pdf","source":{"id":"https://openalex.org/S4387282488","display_name":"INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND MATHEMATICAL THEORY E-ISSN","issn_l":"2545-5699","issn":["2545-5699","2695-1924"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Computer Science and Mathematical Theory","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://iiardjournals.org/get/IJCSMT/VOL. 12 NO. 1 2026/Visual-Aware Solar Forecasting 194-215.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5038502990","display_name":"Richard Iherorochi Nneji","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Iherorochi Nneji Richard","raw_affiliation_strings":["Intelligent Computing Lab, Hace LLC, North Carolina, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Intelligent Computing Lab, Hace LLC, North Carolina, USA","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5038502990"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.50824273,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"194","last_page":"194"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11276","display_name":"Solar Radiation and Photovoltaics","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2105","display_name":"Renewable Energy, Sustainability and the Environment"},"field":{"id":"https://openalex.org/fields/21","display_name":"Energy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11276","display_name":"Solar Radiation and Photovoltaics","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2105","display_name":"Renewable Energy, Sustainability and the Environment"},"field":{"id":"https://openalex.org/fields/21","display_name":"Energy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":9.999999747378752e-05,"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/T10468","display_name":"Photovoltaic System Optimization Techniques","score":0.0,"subfield":{"id":"https://openalex.org/subfields/2105","display_name":"Renewable Energy, Sustainability and the Environment"},"field":{"id":"https://openalex.org/fields/21","display_name":"Energy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/solar-power-forecasting","display_name":"photovoltaic power forecasting","score":0.7039999961853027},{"id":"https://openalex.org/keywords/sky-images","display_name":"sky images","score":0.6729999780654907},{"id":"https://openalex.org/keywords/photovoltaic-power-generation","display_name":"photovoltaic power generation","score":0.7450000047683716},{"id":"https://openalex.org/keywords/short-term-forecasting","display_name":"short-term forecasting","score":0.628000020980835},{"id":"https://openalex.org/keywords/attention-mechanism","display_name":"attention mechanism","score":0.6800000071525574},{"id":"https://openalex.org/keywords/sequence-to-sequence-models","display_name":"sequence-to-sequence models","score":0.8960000276565552},{"id":"https://openalex.org/keywords/spatiotemporal-learning","display_name":"spatiotemporal learning","score":0.8190000057220459},{"id":"https://openalex.org/keywords/grid-integration-of-solar-power","display_name":"grid integration of solar power","score":0.718999981880188},{"id":"https://openalex.org/keywords/hybrid-cnn-lstm-model","display_name":"CNN-LSTM hybrid model","score":0.49900001287460327}],"concepts":[{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.7214999794960022},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6297000050544739},{"id":"https://openalex.org/C41291067","wikidata":"https://www.wikidata.org/wiki/Q1897785","display_name":"Photovoltaic system","level":2,"score":0.5044000148773193},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.49709999561309814},{"id":"https://openalex.org/C73329638","wikidata":"https://www.wikidata.org/wiki/Q527","display_name":"Sky","level":2,"score":0.4803999960422516},{"id":"https://openalex.org/C541104983","wikidata":"https://www.wikidata.org/wiki/Q40015","display_name":"Solar energy","level":2,"score":0.47600001096725464},{"id":"https://openalex.org/C150217764","wikidata":"https://www.wikidata.org/wiki/Q6803607","display_name":"Mean absolute percentage error","level":3,"score":0.4580000042915344},{"id":"https://openalex.org/C187691185","wikidata":"https://www.wikidata.org/wiki/Q2020720","display_name":"Grid","level":2,"score":0.4575999975204468},{"id":"https://openalex.org/C188154048","wikidata":"https://www.wikidata.org/wiki/Q6803609","display_name":"Mean absolute error","level":3,"score":0.44200000166893005},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.428600013256073},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.41990000009536743},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.39169999957084656},{"id":"https://openalex.org/C10551718","wikidata":"https://www.wikidata.org/wiki/Q5227332","display_name":"Data pre-processing","level":2,"score":0.38920000195503235},{"id":"https://openalex.org/C188573790","wikidata":"https://www.wikidata.org/wiki/Q12705","display_name":"Renewable energy","level":2,"score":0.3783999979496002},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.37369999289512634},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.367000013589859},{"id":"https://openalex.org/C16405173","wikidata":"https://www.wikidata.org/wiki/Q192316","display_name":"Geostationary orbit","level":3,"score":0.36320000886917114},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3481000065803528},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.334199994802475},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.32739999890327454},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.3156000077724457},{"id":"https://openalex.org/C122383733","wikidata":"https://www.wikidata.org/wiki/Q865920","display_name":"Approximation error","level":2,"score":0.3050000071525574},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.28610000014305115},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.2840000092983246},{"id":"https://openalex.org/C2780092901","wikidata":"https://www.wikidata.org/wiki/Q3433612","display_name":"Correlation coefficient","level":2,"score":0.26019999384880066},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.25999999046325684},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.25850000977516174},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.2522999942302704}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.56201/ijcsmt.vol.12.no1.2026.pg194.215","doi":"https://doi.org/10.56201/ijcsmt.vol.12.no1.2026.pg194.215","is_oa":true,"landing_page_url":"https://doi.org/10.56201/ijcsmt.vol.12.no1.2026.pg194.215","pdf_url":"https://iiardjournals.org/get/IJCSMT/VOL. 12 NO. 1 2026/Visual-Aware Solar Forecasting 194-215.pdf","source":{"id":"https://openalex.org/S4387282488","display_name":"INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND MATHEMATICAL THEORY E-ISSN","issn_l":"2545-5699","issn":["2545-5699","2695-1924"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Computer Science and Mathematical Theory","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.56201/ijcsmt.vol.12.no1.2026.pg194.215","doi":"https://doi.org/10.56201/ijcsmt.vol.12.no1.2026.pg194.215","is_oa":true,"landing_page_url":"https://doi.org/10.56201/ijcsmt.vol.12.no1.2026.pg194.215","pdf_url":"https://iiardjournals.org/get/IJCSMT/VOL. 12 NO. 1 2026/Visual-Aware Solar Forecasting 194-215.pdf","source":{"id":"https://openalex.org/S4387282488","display_name":"INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND MATHEMATICAL THEORY E-ISSN","issn_l":"2545-5699","issn":["2545-5699","2695-1924"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Computer Science and Mathematical Theory","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","score":1.0,"display_name":"Affordable and clean energy"},{"id":"https://metadata.un.org/sdg/13","score":1.0,"display_name":"Climate action"},{"id":"https://metadata.un.org/sdg/9","score":0.6773999929428101,"display_name":"Industry, innovation and infrastructure"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7214735421.pdf","grobid_xml":"https://content.openalex.org/works/W7214735421.grobid-xml"},"referenced_works_count":23,"referenced_works":["https://openalex.org/W2998130345","https://openalex.org/W3047034125","https://openalex.org/W3146366485","https://openalex.org/W4320011847","https://openalex.org/W4353046973","https://openalex.org/W4376114895","https://openalex.org/W4381095462","https://openalex.org/W4381949573","https://openalex.org/W4386074448","https://openalex.org/W4392619847","https://openalex.org/W4394870039","https://openalex.org/W4396220518","https://openalex.org/W4401324061","https://openalex.org/W4401900283","https://openalex.org/W4402307552","https://openalex.org/W4402507534","https://openalex.org/W4402636217","https://openalex.org/W4403240695","https://openalex.org/W4405676036","https://openalex.org/W4408159887","https://openalex.org/W4409128900","https://openalex.org/W4409349756","https://openalex.org/W4409563103"],"related_works":["https://openalex.org/W7127610020","https://openalex.org/W4414572380","https://openalex.org/W7168296012","https://openalex.org/W7161266258","https://openalex.org/W4411315449","https://openalex.org/W4403728375","https://openalex.org/W4408789980","https://openalex.org/W4414314566","https://openalex.org/W4406729433","https://openalex.org/W7214752953"],"abstract_inverted_index":{"Solar":[0],"energy":[1,8,44,229,235],"plays":[2],"a":[3,67],"pivotal":[4],"role":[5],"in":[6,111,193,204,216],"sustainable":[7],"systems,":[9],"yet":[10],"its":[11],"inherent":[12],"variability":[13],"due":[14],"to":[15,91,224],"weather":[16],"dynamics":[17],"poses":[18],"significant":[19],"challenges":[20,31],"for":[21,41,73,143],"grid":[22,226],"integration":[23],"and":[24,57,80,102,107,135,145,161,179,232],"operational":[25],"efficiency.":[26],"This":[27],"research":[28],"addresses":[29],"these":[30],"by":[32,191],"proposing":[33],"an":[34,174],"attention-based":[35],"sequence-to-sequence":[36],"(Seq2Seq)":[37],"deep":[38],"learning":[39],"model":[40,184],"short-term":[42,82],"solar":[43],"forecasting,":[45],"leveraging":[46],"the":[47,199,211],"SKIPP\u2019D":[48],"dataset":[49],"comprising":[50],"sky":[51,78],"images":[52,79],"(64\u00d764":[53],"resolution,":[54],"1-minute":[55],"intervals)":[56],"synchronized":[58],"photovoltaic":[59],"(PV)":[60],"generation":[61],"data.":[62],"The":[63,183],"hybrid":[64],"architecture":[65],"integrates":[66],"convolutional":[68],"neural":[69],"network":[70],"(CNN)":[71],"encoder":[72],"spatial":[74],"feature":[75],"extraction":[76],"from":[77],"long":[81],"memory":[83],"(LSTM)":[84],"decoder":[85],"enhanced":[86,225],"with":[87,140],"multi-head":[88],"attention":[89,202],"mechanisms":[90,203],"capture":[92],"complex":[93],"spatiotemporal":[94],"dependencies.":[95],"Rigorous":[96],"preprocessing":[97],"ensures":[98],"data":[99],"alignment,":[100],"normalization,":[101],"partitioning":[103],"into":[104],"training,":[105],"validation,":[106],"test":[108],"sets":[109],"stored":[110],"HDF5":[112],"format.":[113],"Model":[114],"performance":[115,151],"is":[116],"evaluated":[117],"using":[118],"metrics":[119],"including":[120],"root":[121],"mean":[122,126,130],"squared":[123],"error":[124,128,133],"(RMSE),":[125],"absolute":[127,131],"(MAE),":[129],"percentage":[132],"(MAPE),":[134],"coefficient":[136],"of":[137,177,181,201,213],"determination":[138],"(R\u00b2),":[139],"separate":[141],"analyses":[142],"sunny":[144,153],"cloudy":[146,165],"conditions.":[147],"Results":[148],"demonstrate":[149],"robust":[150],"on":[152],"days":[154],"(RMSE:":[155,167],"2.156,":[156],"MAE:":[157,169],"1.720,":[158],"R\u00b2:":[159,171],"0.914)":[160],"moderate":[162],"accuracy":[163],"under":[164],"skies":[166],"4.885,":[168],"3.818,":[170],"0.579),":[172],"achieving":[173],"overall":[175],"RMSE":[176,194],"3.779":[178],"R\u00b2":[180],"0.755.":[182],"outperforms":[185],"traditional":[186],"baselines":[187],"(e.g.,":[188],"ARIMA,":[189],"LSTM)":[190],"30%":[192],"during":[195],"clear-sky":[196],"periods,":[197],"validating":[198],"efficacy":[200],"prioritizing":[205],"critical":[206],"temporal":[207],"features.":[208],"By":[209],"addressing":[210],"limitations":[212],"conventional":[214],"models":[215],"handling":[217],"rapid":[218],"cloud-induced":[219],"fluctuations,":[220],"this":[221],"work":[222],"contributes":[223],"stability,":[227],"optimized":[228],"storage":[230],"dispatch,":[231],"scalable":[233],"renewable":[234],"integration.":[236]},"counts_by_year":[],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2026-09-30T00:00:00"}
