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A Seasonal And Monthly Approach For Predicting The Delivered Energy Quantity In A Photovoltaic Power Plant In Romania

Author

Listed:
  • 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)

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

Suggested Citation

  • George C?ru?a?u & Alexandru Pîrjan, 2016. "A Seasonal And Monthly Approach For Predicting The Delivered Energy Quantity In A Photovoltaic Power Plant In Romania," Annals of University of Craiova - Economic Sciences Series, University of Craiova, Faculty of Economics and Business Administration, vol. 1(44), pages 198-207.
    • Handle: RePEc:aio:aucsse:v:1:y:2016:i:44:p:198-207
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    File URL: http://feaa.ucv.ro/AUCSSE/0044v1-019.pdf
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    References listed on IDEAS

    as
    1. Ion LUNGU & Adela BÂRA & George CĂRUTASU & Alexandru PÎRJAN, & Simona-Vasilica OPREA, 2016. "Prediction Intelligent System In The Field Of Renewable Energies Through Neural Networks," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 50(1), pages 85-102.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Artificial Neural Networks; Levenberg-Marquardt; renewable energy; overfitting; performance indicators;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • L86 - Industrial Organization - - Industry Studies: Services - - - Information and Internet Services; Computer Software
    • O13 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Agriculture; Natural Resources; Environment; Other Primary Products
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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