Modern deep neural networks for Direct Normal Irradiance forecasting: A classification approach

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dc.citation.articleNumber100853en_US
dc.citation.journalTitlee-Prime - Advances in Electrical Engineering, Electronics and Energyen_US
dc.citation.volumeNumber10en_US
dc.contributor.authorSaud Ul Hassan, Muhammaden_US
dc.contributor.authorLiaqat, Kashifen_US
dc.contributor.authorSchaefer, Lauraen_US
dc.contributor.authorZolan, Alexander J.en_US
dc.contributor.orgMechanical Engineeringen_US
dc.date.accessioned2025-01-09T20:16:56Zen_US
dc.date.available2025-01-09T20:16:56Zen_US
dc.date.issued2024en_US
dc.description.abstractThe escalating energy demand and the adverse environmental impacts of fossil-fuel use necessitate a shift towards cleaner and renewable alternatives. Concentrated Solar Power (CSP) technology emerges as a promising solution, offering a carbon-free alternative for power generation. The efficiency and profitability of CSP depend on the Direct Normal Irradiance (DNI) component of solar radiation; hence, accurate DNI forecasting can help optimize CSP plants’ operations and performance. The unpredictable nature of weather phenomena, particularly cloud cover, introduces uncertainty into DNI projections. Existing DNI forecasting models use meteorological factors, which are both challenging to estimate numerically over short prediction windows and expensive to model through data at a sufficiently high spatial and temporal resolution. This research addresses the challenge by presenting a novel approach that formulates DNI prediction as a multi-class classification problem, departing from conventional regression-based methods. The primary objective of this classification framework is to identify optimal periods aligning with specific operational thresholds for CSP plants, contributing to enhanced dispatch optimization strategies. We model the DNI classification problem using four advanced deep neural networks – rectified linear unit (ReLU) networks, 1D residual networks (ResNets), bidirectional long short-term memory (BiLSTM) networks, and transformers – achieving accuracies up to 93.5% without requiring meteorological parameters.en_US
dc.identifier.citationSaud Ul Hassan, M., Liaqat, K., Schaefer, L., & Zolan, A. J. (2024). Modern deep neural networks for Direct Normal Irradiance forecasting: A classification approach. E-Prime - Advances in Electrical Engineering, Electronics and Energy, 10, 100853. https://doi.org/10.1016/j.prime.2024.100853en_US
dc.identifier.digital1-s2-0-S2772671124004327-mainen_US
dc.identifier.doihttps://doi.org/10.1016/j.prime.2024.100853en_US
dc.identifier.urihttps://hdl.handle.net/1911/118092en_US
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.rightsExcept where otherwise noted, this work is licensed under a Creative Commons Attribution (CC BY) license. Permission to reuse, publish, or reproduce the work beyond the terms of the license or beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.subject.keywordDirect Normal Irradianceen_US
dc.subject.keywordConcentrated solar poweren_US
dc.subject.keywordDeep neural networksen_US
dc.subject.keywordRecurrent neural networksen_US
dc.subject.keywordTransformersen_US
dc.titleModern deep neural networks for Direct Normal Irradiance forecasting: A classification approachen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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