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A SEASONAL AND MONTHLY APPROACH FOR PREDICTING THE DELIVERED ENERGY QUANTITY IN A PHOTOVOLTAIC POWER PLANT IN ROMANIA

George C?ru?a?u and Alexandru Pîrjan
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George C?ru?a?u: The Romanian-American University Faculty of Computer Science for Business Management
Alexandru Pîrjan: The Romanian-American University Faculty of Computer Science for Business Management

Annals of University of Craiova - Economic Sciences Series, 2016, vol. 1, issue 44, 198-207

Abstract: In this paper, we present solutions that facilitate the forecasting of the delivered energy quantity in a photovoltaic power plant using the data measured from the solar panels' sensors: solar irradiation level, present module temperature, environmental temperature, atmospheric pressure and humidity. We have developed and analyzed a series of Artificial Neural Networks (ANNs) based on the Levenberg-Marquardt algorithm, using seasonal and monthly approaches. We have also integrated our developed Artificial Neural Networks into callable functions that we have compiled using the Matlab Compiler SDK. Thus, our solution can be accessed by developers through multiple Application Programming Interfaces when programming software that predicts the photovoltaic renewable energy production considering the seasonal particularities of the Romanian weather patterns

Keywords: Artificial Neural Networks; Levenberg-Marquardt; renewable energy; overfitting; performance indicators (search for similar items in EconPapers)
JEL-codes: C53 L86 O13 Q42 Q47 (search for similar items in EconPapers)
Date: 2016
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