Optimizing the deep learning parameters using metaheuristic algorithms for PV-battery-based DC nanogrids

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dc.authorscopusidAmir Seyyedabbasi / 57202833910
dc.authorwosidAmir Seyyedabbasi / HJH-7387-2023
dc.contributor.authorSulaiman, Mohd Herwan
dc.contributor.authorMustaffa, Zuriani
dc.contributor.authorSeyyedabbasi, Amir
dc.contributor.authorIrawan, Addie
dc.date.accessioned2025-04-18T07:30:52Z
dc.date.available2025-04-18T07:30:52Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Yazılım Mühendisliği Bölümü
dc.description.abstractThis paper presents an implementation of metaheuristic algorithms to optimise deep learning neural networks (DNNs) for sensor-less control of photovoltaic (PV) Converters in DC nanogrids. Using a Fixed Forward Neural Network (FFNN), it estimates PV output current (IPV) based on three days of real data. Given the vulnerability of current sensors in DC system measurements, accurately replicating current sensor data is vital. The data exhibits dynamic nonlinear relationships with inputs like solar irradiance, temperature, and voltage. The study assesses the effectiveness of Evolutionary Mating Algorithm (EMA) and Sand Cat Swarm Optimisation (SCSO), comparing them with Adaptive Moment Estimation (ADAM), Genetic Algorithm (GA), and Particle Swarm Optimisation (PSO). The research leverages these metaheuristic algorithms to optimise machine learning integration, addressing regression estimation problems, and enhancing system reliability by eliminating sensors. Key findings indicate that the EMA-DNN combination achieved remarkable performance, with the lowest Mean Squared Error (MSE) of 0.5906, the lowest Mean Absolute Error (MAE) of 0.4680, and the lowest Mean Absolute Percentage Error (MAPE) of 12.1780%. These results offer valuable insights into how the metaheuristic-DNN approach can solve regression estimation problems and reduce the number of sensors in PV battery-based nanogrids’ control layers.
dc.identifier.citationSulaiman, M. H., Mustaffa, Z., Seyyedabbasi, A., & Irawan, A. (2024). Optimizing the deep learning parameters using metaheuristic algorithms for PV-battery-based DC nanogrids. International Journal of Ambient Energy, 45(1), 2358068.
dc.identifier.doi10.1080/01430750.2024.2358068
dc.identifier.endpage14
dc.identifier.issn01430750
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85195215764
dc.identifier.scopusqualityQ1
dc.identifier.startpage1
dc.identifier.urihttp://dx.doi.org/10.1080/01430750.2024.2358068
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6447
dc.identifier.volume45
dc.indekslendigikaynakScopus
dc.institutionauthorSeyyedabbasi, Amir
dc.institutionauthoridAmir Seyyedabbasi / 0000-0001-5186-4499
dc.language.isoen
dc.publisherTaylor and francis ltd.
dc.relation.ispartofInternational journal of ambient energy
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectDeep Learning Neural Networks
dc.subjectFixed Forward Neural Networks
dc.subjectMetaheuristic Optimisers
dc.subjectPhotovoltaic
dc.subjectSensor-Less Control
dc.titleOptimizing the deep learning parameters using metaheuristic algorithms for PV-battery-based DC nanogrids
dc.typeArticle

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