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Evaluation of Machine Learning Methods on Large-Scale Spatiotemporal Data for Photovoltaic Power Prediction

Author

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  • Evan Sauter

    (Department of Electrical and Computer Engineering, Worcester Polytechnic Institute, 100 Institute Road, Worcester, MA 01609, USA
    These authors contributed equally to this work.)

  • Maqsood Mughal

    (Department of Electrical and Computer Engineering, Worcester Polytechnic Institute, 100 Institute Road, Worcester, MA 01609, USA
    These authors contributed equally to this work.)

  • Ziming Zhang

    (Department of Electrical and Computer Engineering, Worcester Polytechnic Institute, 100 Institute Road, Worcester, MA 01609, USA
    These authors contributed equally to this work.)

Abstract

The exponential increase in photovoltaic (PV) arrays installed globally, particularly given the intermittent nature of PV generation, has emphasized the need to accurately forecast the predicted output power of the arrays. Regardless of the length of the forecasts, the modeling of PV arrays is made difficult by their dependence on weather. Typically, the model projections are generated from datasets at one location across a couple of years. The purpose of this study was to compare the effectiveness of regression models in very short-term deterministic forecasts for spatiotemporal projections. The compiled dataset is unique given that it consists of weather and output power data of PVs located at five cities spanning 3 and 6 years in length. Gated recurrent unit (GRU) generalized the best for same-city and cross-city predictions, while long short-term memory (LSTM) and ensemble bagging had the best cross-city and same-city predictions, respectively.

Suggested Citation

  • Evan Sauter & Maqsood Mughal & Ziming Zhang, 2023. "Evaluation of Machine Learning Methods on Large-Scale Spatiotemporal Data for Photovoltaic Power Prediction," Energies, MDPI, vol. 16(13), pages 1-26, June.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:13:p:4908-:d:1177828
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    References listed on IDEAS

    as
    1. Tingting Zhu & Yiren Guo & Zhenye Li & Cong Wang, 2021. "Solar Radiation Prediction Based on Convolution Neural Network and Long Short-Term Memory," Energies, MDPI, vol. 14(24), pages 1-16, December.
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    3. Severiano, Carlos A. & Silva, Petrônio Cândido de Lima e & Weiss Cohen, Miri & Guimarães, Frederico Gadelha, 2021. "Evolving fuzzy time series for spatio-temporal forecasting in renewable energy systems," Renewable Energy, Elsevier, vol. 171(C), pages 764-783.
    4. Ogliari, Emanuele & Dolara, Alberto & Manzolini, Giampaolo & Leva, Sonia, 2017. "Physical and hybrid methods comparison for the day ahead PV output power forecast," Renewable Energy, Elsevier, vol. 113(C), pages 11-21.
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