Abstract
The lack of meteorological measurements at a location of interest (target location) constitutes a problem that is crucial for the purposes of both weather forecasting and energy system design/validation. This paper constitutes a pilot study for the accurate estimation of meteorological values at a target location employing the meteorological measurements collected at a nearby (reference) location. Artificial neural networks are investigated and compared with traditional estimation methods such as linear models of first and higher orders and the non-linear model. The significance of the improvement obtained via the estimation--and especially the artificial neural network approach--over simply considering the measurements at the reference location is demonstrated in a number of energy applications.
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