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

Author

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  • Jose Manuel Barrera

    (Department of Software and Computing Systems, University of Alicante, 03690 Alicante, Spain
    Lucentia Lab, C/ Pintor Pérez Gil, N-16, 03540 Alicante, Spain)

  • Alejandro Reina

    (Department of Software and Computing Systems, University of Alicante, 03690 Alicante, Spain
    Lucentia Lab, C/ Pintor Pérez Gil, N-16, 03540 Alicante, Spain)

  • Alejandro Maté

    (Department of Software and Computing Systems, University of Alicante, 03690 Alicante, Spain
    Lucentia Lab, C/ Pintor Pérez Gil, N-16, 03540 Alicante, Spain)

  • Juan Carlos Trujillo

    (Department of Software and Computing Systems, University of Alicante, 03690 Alicante, Spain
    Lucentia Lab, C/ Pintor Pérez Gil, N-16, 03540 Alicante, Spain)

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.

Suggested Citation

  • Jose Manuel Barrera & Alejandro Reina & Alejandro Maté & Juan Carlos Trujillo, 2020. "Solar Energy Prediction Model Based on Artificial Neural Networks and Open Data," Sustainability, MDPI, vol. 12(17), pages 1-20, August.
  • Handle: RePEc:gam:jsusta:v:12:y:2020:i:17:p:6915-:d:403936
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    References listed on IDEAS

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    1. Rizwan, M. & Jamil, Majid & Kirmani, Sheeraz & Kothari, D.P., 2014. "Fuzzy logic based modeling and estimation of global solar energy using meteorological parameters," Energy, Elsevier, vol. 70(C), pages 685-691.
    2. Amrouche, Badia & Le Pivert, Xavier, 2014. "Artificial neural network based daily local forecasting for global solar radiation," Applied Energy, Elsevier, vol. 130(C), pages 333-341.
    3. Kim, Sungil & Kim, Heeyoung, 2016. "A new metric of absolute percentage error for intermittent demand forecasts," International Journal of Forecasting, Elsevier, vol. 32(3), pages 669-679.
    4. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    5. Li, Huashan & Ma, Weibin & Wang, Xianlong & Lian, Yongwang, 2011. "Estimating monthly average daily diffuse solar radiation with multiple predictors: A case study," Renewable Energy, Elsevier, vol. 36(7), pages 1944-1948.
    6. Julián Ignacio Monís & Rafael López-Luque & Juan Reca & Juan Martínez, 2020. "Multistage Bounded Evolutionary Algorithm to Optimize the Design of Sustainable Photovoltaic (PV) Pumping Irrigation Systems with Storage," Sustainability, MDPI, vol. 12(3), pages 1-17, January.
    7. Fadare, D.A., 2009. "Modelling of solar energy potential in Nigeria using an artificial neural network model," Applied Energy, Elsevier, vol. 86(9), pages 1410-1422, September.
    8. Peter King & Christopher Sansom & Paul Comley, 2019. "Photogrammetry for Concentrating Solar Collector Form Measurement, Validated Using a Coordinate Measuring Machine," Sustainability, MDPI, vol. 12(1), pages 1-20, December.
    9. Joseph Kiesecker & Sharon Baruch-Mordo & Mike Heiner & Dhaval Negandhi & James Oakleaf & Christina Kennedy & Pareexit Chauhan, 2019. "Renewable Energy and Land Use in India: A Vision to Facilitate Sustainable Development," Sustainability, MDPI, vol. 12(1), pages 1-14, December.
    10. Chineke, Theo Chidiezie, 2008. "Equations for estimating global solar radiation in data sparse regions," Renewable Energy, Elsevier, vol. 33(4), pages 827-831.
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    Cited by:

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    6. Natei Ermias Benti & Mesfin Diro Chaka & Addisu Gezahegn Semie, 2023. "Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects," Sustainability, MDPI, vol. 15(9), pages 1-33, April.
    7. Hossein Moayedi & Amir Mosavi, 2021. "An Innovative Metaheuristic Strategy for Solar Energy Management through a Neural Networks Framework," Energies, MDPI, vol. 14(4), pages 1-18, February.

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