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Predicting the power output of distributed renewable energy resources within a broad geographical region

Eftichios Koutroulis

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Summary

In recent years, estimating the power output of in- herently intermittent and potentially distributed renewable energy sources has become a major scientific and societal concern. In this paper, we provide an algorithmic framework, along with an inter- active web-based tool, to enable short-to-middle term forecasts of photovoltaic (PV) systems and wind generators output. Importantly, we propose a generic PV output estimation method, the backbone of which is a solar irradiance approximation model that incorpo- rates free-to-use, readily available meteorological data coming from online weather stations. The model utilizes non-linear approxima- tion components for turning cloud-coverage into radiation forecasts, such as an MLP neural network with one hidden layer. We present a thorough evaluation of the proposed techniques, and show that they can be successfully employed within a broad geographical region (the Mediterranean belt) and come with specific performance guar- antees. Crucially, our methods do not rely on complex and expensive weather models and data, and our web-based tool can be of immedi- ate use to the community as a simulation data acquisition platform.

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