Techno-economic analysis and energy forecasting study of domestic and commercial photovoltaic system installations in Estonia

Noman Shabbir, Lauri Kütt, Hadi A. Raja, Muhammad Jawad, Alo Allik and Oleksandr Husev

Energy, 2022, vol. 253, issue C

Abstract: The Baltic countries have good potential for solar photovoltaic (PV) energy generation, as on average 15 hours of sunlight is available in summer. Another potential option is to encourage the construction of nearly zero-energy buildings (NZEBs) according to the EU framework. This study focuses on solar irradiance and energy generation potential in different regions of Estonia as a case study. Techno-economic analysis of possible solutions to use differently rated domestic and commercial PV systems’ feasibility and payback periods are presented. The results illustrate that all PV systems studied in the research are self-sufficient while selling excess energy to the grid with a nominal payback period. Furthermore, for short-term energy management, we developed an efficient deep learning-based forecasting algorithm. Apart from the inherent non-linear nature of solar energy data, what makes forecasting particularly challenging is to efficiently cope with the issue of data regression and random noise. The RNN-LSTM algorithm is chosen for the prediction of solar energy. This is the first comprehensive report that can encourage potential Estonian users to invest in solar PV systems and gain economic benefits. The results presented in this study cover a broader perspective and are more useful keeping in mind the real market situation of the Baltic countries.

Keywords: PV Systems; Economic analysis; Energy forecasting; Machine learning; Payback time (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:253:y:2022:i:c:s0360544222010593

DOI: 10.1016/j.energy.2022.124156

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