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Forecasting Photovoltaic Deployment with Neural Networks

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  • Crescenzio Gallo

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  • Michelangelo De Bonis

Abstract

The photovoltaic (PV) industry in Italy has already crossed the threshold of 1 GW of installed capacity. Currently there are approximately 70,000 certified facilities in operation for a power generation of 1,300 GWh/year. With these figures, Italy has become the second country in Europe for PV installed power after Germany. The energy produced would be sufficient to meet the power needs of approximately 1,200,000 people. This leads to some questions: Will this technology continue to grow exponentially even after the recent reduction in rates by the Energy Bill? Will the number of installed PV facilities still grow even with less public support and (probably) a reduction in the technology purchase price? The purpose of this paper is therefore to develop a conceptual model to make a prediction of the PV installed power in Italy through the use of “supervised” artificial neural networks. This model is also applied to the analysis of the spread of this technology in some other European countries.

Suggested Citation

  • Crescenzio Gallo & Michelangelo De Bonis, 2011. "Forecasting Photovoltaic Deployment with Neural Networks," Quaderni DSEMS 02-2011, Dipartimento di Scienze Economiche, Matematiche e Statistiche, Universita' di Foggia.
  • Handle: RePEc:ufg:qdsems:02-2011
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    File URL: http://www.economia.unifg.it/sites/sd01/files/allegatiparagrafo/24-11-2016/q022011.pdf
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    Cited by:

    1. Palmer, J. & Sorda, G. & Madlener, R., 2015. "Modeling the diffusion of residential photovoltaic systems in Italy: An agent-based simulation," Technological Forecasting and Social Change, Elsevier, vol. 99(C), pages 106-131.

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    Keywords

    photovoltaic; forecasting; neural networks.;

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