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

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Description/Abstract

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.