Solar Energy Prediction Model Based on Artificial Neural Networks and Open Data

Jose Manuel Barrera, Alejandro Reina, Alejandro Maté and Juan Carlos Trujillo
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Jose Manuel Barrera: Department of Software and Computing Systems, University of Alicante, 03690 Alicante, Spain
Alejandro Reina: Department of Software and Computing Systems, University of Alicante, 03690 Alicante, Spain
Alejandro Maté: Department of Software and Computing Systems, University of Alicante, 03690 Alicante, Spain
Juan Carlos Trujillo: Department of Software and Computing Systems, University of Alicante, 03690 Alicante, Spain

Sustainability, 2020, vol. 12, issue 17, 1-20

Abstract: With climate change driving an increasingly stronger influence over governments and municipalities, sustainable development, and renewable energy are gaining traction across the globe. This is reflected within the EU 2030 agenda, that envisions a future where there is universal access to affordable, reliable and sustainable energy. One of the challenges to achieve this vision lies on the low reliability of certain renewable sources. While both particulars and public entities try to reach self-sufficiency through sustainable energy generation, it is unclear how much investment is needed to mitigate the unreliability introduced by natural factors such as varying wind speed and daylight across the year. In this sense, a tool that aids predicting the energy output of sustainable sources across the year for a particular location can aid greatly in making sustainable energy investments more efficient. In this paper, we make use of Open Data sources, Internet of Things (IoT) sensors and installations distributed across Europe to create such tool through the application of Artificial Neural Networks. We analyze how the different factors affect the prediction of energy production and how Open Data can be used to predict the expected output of sustainable sources. As a result, we facilitate users the necessary information to decide how much they wish to invest according to the desired energy output for their particular location. Compared to state-of-the-art proposals, our solution provides an abstraction layer focused on energy production, rather that radiation data, and can be trained and tailored for different locations using Open Data. Finally, our tests show that our proposal improves the accuracy of the forecasting, obtaining a lower mean squared error (MSE) of 0.040 compared to an MSE 0.055 from other proposals in the literature.

Keywords: solar energy; prediction; forecasting; open data; artificial neural networks; deep learning; IoT; artificial intelligence (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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