Abstract
Renewable energy has unique characteristics such as it is sustainable, clean and free. However, renewable generation systems have two major limitations: they are strongly dependent on the weather conditions, and they have unsynchronized generation peaks with the demand peaks, in general. In a series of two papers, an energy management strategy for a distributed photovoltaic-wind-storage system is proposed. This first paper proposes a method to predict the amount of power generated by the PV and wind power sources and the power consumed by local loads. The proposed forecasting models are developed using artificial neural networks (ANNs). Those forecasting models were verified on real data and showed good to excellent results.
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Hussein, A., Batarseh, I. (2014). Energy Management for a Grid-Tied Photovoltaic-Wind-Storage System: Part I—Forecasting Models. In: Hamdan, M., Hejase, H., Noura, H., Fardoun, A. (eds) ICREGA’14 - Renewable Energy: Generation and Applications. Springer Proceedings in Energy. Springer, Cham. https://doi.org/10.1007/978-3-319-05708-8_32
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DOI: https://doi.org/10.1007/978-3-319-05708-8_32
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