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• W3048448847 abstract "Power production prediction from Renewable Energy (RE) sources has been widely studied in the last decade. This is extremely important for utilities to counterpart electricity supply with consumer demands across centralized grid networks. In this context, we propose a local training strategy-based Artificial Neural Network (ANN) for predicting the power productions of solar Photovoltaic (PV) systems. Specifically, the timestamp, weather variables, and corresponding power productions collected locally at each hour interval h, h = [1,24] (i.e., an interval of Δh = 1 hour), are exploited to build, optimize, and evaluate H = 24 different ANNs for the 24 hourly solar PV production predictions. The proposed local training strategy-based ANN is expected to provide more accurate predictions with short computational times than those obtained by a single (i.e., H = 1) ANN model (hereafter called benchmark) built, optimized, and evaluated globally on the entire available dataset. The proposed strategy is applied to a case study regarding a 264kWp solar PV system located in Amman, Jordan, and its effectiveness compared to the benchmark is verified by resorting to different performance metrics from the literature. Further, its effectiveness is verified and compared when Extreme Learning Machines (ELMs) are adopted instead of the ANNs, and when the Persistence model is used. The prediction performance of the two training strategies-based ANN is also investigated and compared in terms of i) different weather conditions (i.e., seasons) experienced by the solar PV system under study and ii) different hour intervals (i.e., Δh = 2, 3, and 4 hours) used for partitioning the overall dataset and, thus, establishing the different ANNs (i.e., H = 12, 8, and 6 models, respectively)." @default.
• W3048448847 created "2020-08-18" @default.
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• W3048448847 date "2020-01-01" @default.
• W3048448847 modified "2023-10-18" @default.
• W3048448847 title "A Local Training Strategy-Based Artificial Neural Network for Predicting the Power Production of Solar Photovoltaic Systems" @default.
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• W3048448847 doi "https://doi.org/10.1109/access.2020.3016165" @default.
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