Panagopoulos, Athanasios Aris, Chalkiadakis, Georgios and Koutroulis, Eftichios
Predicting the power output of distributed renewable
energy resources within a broad geographical region.
In, ECAI-2012/PAIS-2012: 20th European Conference on Artificial Intelligence, Prestigious Applications of Intelligent Systems Track , Montpellier, FR,
27 - 31 Aug 2012.
PDF (Paper to appear in Proc. of ECAI-2012)
- Version of Record
In recent years, estimating the power output of inherently 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 interactive 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 incorporates free-to-use, readily available meteorological data coming from online weather stations. The model utilizes non-linear approximation 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 guarantees. Crucially, our methods do not rely on complex and expensive weather models and data, and our web-based tool can be of immediate use to the community as a simulation data acquisition platform.
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