Abstract
In this paper, Multi-Layer Perceptron Neural Network (MLPNN), Quadratic model (QM), and Multiple Linear Regression (MLR) have been developed and utilized to predict power production (PP) produced from a 5.4 kW off-grid PV system located in Al Mahmra, Lebanon. For this aim, the hourly data of wind speed (WS), global solar radiation (GSR), average temperature (T), and time (Ti) were used as input parameters for the proposed models. The results showed that QM and MLPNN models were suitable for predicting the PP of the PV system. Among the developed models, the QM model presented significantly better prediction performance based on the values R2, MAE, and RMSE.
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Kassem, Y., Gökçekuş, H., Babangida, A., Larmouth, E.J., Mafela, L.G. (2023). Time Series Forecasting of Solar Power Generation for 5.4 kW Off-Grid PV System: A Case Study in Al Mahmra, Lebanon. In: Vasant, P., Weber, GW., Marmolejo-Saucedo, J.A., Munapo, E., Thomas, J.J. (eds) Intelligent Computing & Optimization. ICO 2022. Lecture Notes in Networks and Systems, vol 569. Springer, Cham. https://doi.org/10.1007/978-3-031-19958-5_58
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DOI: https://doi.org/10.1007/978-3-031-19958-5_58
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