@prefix ocrer: . @prefix owl: . @prefix scires: . @prefix xsd: . @prefix skos: . @prefix rdfs: . @prefix ocresd: . @prefix swo: . @prefix cito: . @prefix geo: . @prefix ocresst: . @prefix dcterms: . @prefix vivo: . @prefix event: . @prefix vann: . @prefix foaf: . @prefix c4o: . @prefix fabio: . @prefix vcard: . @prefix thkoeln: . @prefix vitro: . @prefix vitro-public: . @prefix rdf: . @prefix ocresp: . @prefix bibo: . @prefix obo: . @prefix ro: . obo:BFO_0000031 a owl:Class ; rdfs:label "Bedingter Kontinuant"@de-DE , "Generically Dependent Continuant"@en-US , "Generically Dependent Continuant"@en . . a , owl:NamedIndividual , owl:Thing ; rdfs:label "Open Access"@en-US , "Open Access"@de-DE . a obo:BFO_0000001 , obo:BFO_0000020 , vivo:Authorship , vivo:Relationship , obo:BFO_0000002 , owl:Thing ; vivo:relates . obo:BFO_0000002 a owl:Class ; rdfs:label "Kontinuant"@de-DE , "Continuant"@en-US , "Continuant"@en . thkoeln:Artikel a owl:Class ; rdfs:label "Artikel"@de-DE , "Article"@en-US . a obo:BFO_0000001 , obo:BFO_0000020 , vivo:Authorship , vivo:Relationship , obo:BFO_0000002 , owl:Thing ; vivo:relates . obo:BFO_0000001 a owl:Class ; rdfs:label "Entität"@de-DE , "Entity"@en-US , "Entity"@en . a owl:NamedIndividual , , owl:Thing ; rdfs:label "Text"@de-DE , "Text"@en-US . bibo:Document a owl:Class ; rdfs:label "Dokument"@de-DE , "Document"@en-US , "Document"@en . a obo:BFO_0000001 , obo:BFO_0000020 , vivo:Authorship , vivo:Relationship , obo:BFO_0000002 , owl:Thing ; vivo:relates . owl:Thing a owl:Class . a thkoeln:Zitat , owl:Thing . thkoeln:Journalartikel a owl:Class ; rdfs:label "Journal Article"@en-US , "Journalartikel"@de-DE . a thkoeln:Organisationseinheit , foaf:Agent , obo:BFO_0000001 , obo:BFO_0000004 , obo:BFO_0000002 , foaf:Organization , owl:Thing ; rdfs:label "Cologne Institute for Renewable Energy" ; . a bibo:Journal , bibo:Collection , thkoeln:Zeitschrift , owl:Thing , obo:IAO_0000030 , obo:BFO_0000001 , obo:BFO_0000031 , bibo:Periodical , obo:BFO_0000002 ; rdfs:label "IEEE access" ; vivo:publicationVenueFor . obo:IAO_0000030 a owl:Class ; rdfs:label "Entität für Informationsinhalt"@de-DE , "Information Content Entity"@en-US , "Information Content Entity"@en . a thkoeln:Zitat , owl:Thing . a obo:BFO_0000001 , obo:BFO_0000020 , vivo:Authorship , vivo:Relationship , obo:BFO_0000002 , owl:Thing ; vivo:relates . a obo:BFO_0000003 , obo:BFO_0000008 , obo:BFO_0000001 , vivo:DateTimeValue , obo:BFO_0000148 , owl:Thing . a thkoeln:Zitat , owl:Thing . a obo:BFO_0000002 , obo:BFO_0000031 , owl:Thing , foaf:Document , obo:IAO_0000030 , thkoeln:Artikel , thkoeln:Journalartikel , obo:BFO_0000001 , bibo:Document , thkoeln:Publikation ; rdfs:label "RDF description of A Probabilistic Model Predictive Control Approach for PV-Diesel Hybrid Systems in Ghana’s Health Sector Using Seamless State Prediction Methods - https://fis.th-koeln.de/vivo/individual/publ_11670" , "A Probabilistic Model Predictive Control Approach for PV-Diesel Hybrid Systems in Ghana’s Health Sector Using Seamless State Prediction Methods" ; thkoeln:dokumententyp "Wissenschaftlicher Artikel" ; thkoeln:peerReviewed "true" ; thkoeln:sprache "englisch" ; thkoeln:status "validiert" ; ; ; ; , , ; ; "2026-04-04T01:48:50"^^xsd:dateTime ; ; ; bibo:abstract "In Ghana, unreliable public grid infrastructure greatly impacts rural healthcare, where diesel generators are commonly used despite their high financial and environmental costs. Photovoltaic (PV)-hybrid systems offer a sustainable alternative, but require robust, predictive control strategies to ensure reliability. This study proposes a sector-specific Model Predictive Control (MPC) approach, integrating advanced load and meteorological forecasting for optimal energy dispatch. The methodology includes a long-short-term memory (LSTM)-based load forecasting model with probabilistic Monte Carlo dropout, a customized Numerical Weather Prediction (NWP) model based on the Weather Research and Forecasting (WRF) framework, and deep learning-based All-Sky Imager (ASI) nowcasting to improve short-term solar predictions. By combining these forecasting methods into a seamless prediction framework, the proposed MPC optimizes system performance while reducing reliance on fossil fuels. This study benchmarks the MPC against a traditional rule-based dispatch system, using data collected from a rural health facility in Kologo, Ghana. Results demonstrate that predictive control greatly reduces both economic and ecological costs. Compared to rule-based dispatch, diesel generator operation and fuel consumption are reduced by up to 61.62% and 47.17%, leading to economical and ecological cost savings of up to 20.7% and 31.78%. Additionally, system reliability improves, with battery depletion events during blackouts decreasing by up to 99.42%, while wear and tear on the diesel generator and battery are reduced by up to 54.93% and 37.34%, respectively. Furthermore, hyperparameter tuning enhances MPC performance, introducing further optimization potential. These findings highlight the effectiveness of predictive control in improving energy resilience for critical healthcare applications in rural settings." ; bibo:doi "10.1109/ACCESS.2025.3556980" ; bibo:pageEnd "61927" ; bibo:pageStart "61890" ; bibo:volume "13" ; vitro:mostSpecificType thkoeln:Journalartikel ; vivo:dateTimeValue ; vivo:freetextKeyword "MPC" , "Model Predictive Control" , "Machine Learning" , "Health Sector" , "Artificial Intelligence" , "West Africa" , "Deep Learning" , "Forecasting" , "Ghana" , "Energy Meteorology" ; vivo:hasPublicationVenue ; vivo:relatedBy , , , , . thkoeln:Publikation a owl:Class ; rdfs:label "Publikationen"@de-DE , "Publications"@en-US . a obo:BFO_0000001 , obo:BFO_0000020 , vivo:Authorship , vivo:Relationship , obo:BFO_0000002 , owl:Thing ; vivo:relates .