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Short-Term Forecasting of the Output Power of a Building-Integrated Photovoltaic System Using a Metaheuristic Approach

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  • Mehdi Seyedmahmoudian

    (School of Software and Electrical Engineering, Swinburne University of Technology, Melbourne VIC 3122, Victoria, Australia)

  • Elmira Jamei

    (College of Engineering and Science, Victoria University, Melbourne VIC 3011, Victoria, Australia)

  • Gokul Sidarth Thirunavukkarasu

    (School of Engineering, Deakin University, Geelong VIC 3216, Victoria, Australia)

  • Tey Kok Soon

    (Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia)

  • Michael Mortimer

    (School of Engineering, Deakin University, Geelong VIC 3216, Victoria, Australia)

  • Ben Horan

    (School of Engineering, Deakin University, Geelong VIC 3216, Victoria, Australia)

  • Alex Stojcevski

    (School of Software and Electrical Engineering, Swinburne University of Technology, Melbourne VIC 3122, Victoria, Australia)

  • Saad Mekhilef

    (Department of Electrical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia)

Abstract

The rapidly increasing use of renewable energy resources in power generation systems in recent years has accentuated the need to find an optimum and efficient scheme for forecasting meteorological parameters, such as solar radiation, temperature, wind speed, and sun exposure. Integrating wind power prediction systems into electrical grids has witnessed a powerful economic impact, along with the supply and demand balance of the power generation scheme. Academic interest in formulating accurate forecasting models of the energy yields of solar energy systems has significantly increased around the world. This significant rise has contributed to the increase in the share of solar power, which is evident from the power grids set up in Germany (5 GW) and Bavaria. The Spanish government has also taken initiative measures to develop the use of renewable energy, by providing incentives for the accurate day-ahead forecasting. Forecasting solar power outputs aids the critical components of the energy market, such as the management, scheduling, and decision making related to the distribution of the generated power. In the current study, a mathematical forecasting model, optimized using differential evolution and the particle swarm optimization (DEPSO) technique utilized for the short-term photovoltaic (PV) power output forecasting of the PV system located at Deakin University (Victoria, Australia), is proposed. A hybrid self-energized datalogging system is utilized in this setup to monitor the PV data along with the local environmental parameters used in the proposed forecasting model. A comparison study is carried out evaluating the standard particle swarm optimization (PSO) and differential evolution (DE), with the proposed DEPSO under three different time horizons (1-h, 2-h, and 4-h). Results of the 1-h time horizon shows that the root mean square error (RMSE), mean relative error (MRE), mean absolute error (MAE), mean bias error (MBE), weekly mean error (WME), and variance of the prediction errors (VAR) of the DEPSO based forecasting is 4.4%, 3.1%, 0.03, −1.63, 0.16, and 0.01, respectively. Results demonstrate that the proposed DEPSO approach is more efficient and accurate compared with the PSO and DE.

Suggested Citation

  • Mehdi Seyedmahmoudian & Elmira Jamei & Gokul Sidarth Thirunavukkarasu & Tey Kok Soon & Michael Mortimer & Ben Horan & Alex Stojcevski & Saad Mekhilef, 2018. "Short-Term Forecasting of the Output Power of a Building-Integrated Photovoltaic System Using a Metaheuristic Approach," Energies, MDPI, vol. 11(5), pages 1-23, May.
  • Handle: RePEc:gam:jeners:v:11:y:2018:i:5:p:1260-:d:146365
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    References listed on IDEAS

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    Cited by:

    1. Morsali, Roozbeh & Thirunavukkarasu, Gokul Sidarth & Seyedmahmoudian, Mehdi & Stojcevski, Alex & Kowalczyk, Ryszard, 2020. "A relaxed constrained decentralised demand side management system of a community-based residential microgrid with realistic appliance models," Applied Energy, Elsevier, vol. 277(C).
    2. Hossein Moayedi & Amir Mosavi, 2021. "Electrical Power Prediction through a Combination of Multilayer Perceptron with Water Cycle Ant Lion and Satin Bowerbird Searching Optimizers," Sustainability, MDPI, vol. 13(4), pages 1-18, February.
    3. He, Xinbo & Wang, Yong & Zhang, Yuyang & Ma, Xin & Wu, Wenqing & Zhang, Lei, 2022. "A novel structure adaptive new information priority discrete grey prediction model and its application in renewable energy generation forecasting," Applied Energy, Elsevier, vol. 325(C).
    4. Mehdi Seyedmahmoudian & Gokul Sidarth Thirunavukkarasu & Elmira Jamei & Tey Kok Soon & Ben Horan & Saad Mekhilef & Alex Stojcevski, 2020. "A Sustainable Distributed Building Integrated Photo-Voltaic System Architecture with a Single Radial Movement Optimization Based MPPT Controller," Sustainability, MDPI, vol. 12(16), pages 1-21, August.
    5. Lauren E. Natividad & Pablo Benalcazar, 2023. "Hybrid Renewable Energy Systems for Sustainable Rural Development: Perspectives and Challenges in Energy Systems Modeling," Energies, MDPI, vol. 16(3), pages 1-15, January.
    6. Kuen-Suan Chen & Kuo-Ping Lin & Jun-Xiang Yan & Wan-Lin Hsieh, 2019. "Renewable Power Output Forecasting Using Least-Squares Support Vector Regression and Google Data," Sustainability, MDPI, vol. 11(11), pages 1-13, May.
    7. Yosui Miyazaki & Yusuke Kameda & Junji Kondoh, 2019. "A Power-Forecasting Method for Geographically Distributed PV Power Systems using Their Previous Datasets," Energies, MDPI, vol. 12(24), pages 1-14, December.
    8. Andi A. H. Lateko & Hong-Tzer Yang & Chao-Ming Huang, 2022. "Short-Term PV Power Forecasting Using a Regression-Based Ensemble Method," Energies, MDPI, vol. 15(11), pages 1-21, June.
    9. Mehdi Tavakkoli & Jafar Adabi & Sasan Zabihi & Radu Godina & Edris Pouresmaeil, 2018. "Reserve Allocation of Photovoltaic Systems to Improve Frequency Stability in Hybrid Power Systems," Energies, MDPI, vol. 11(10), pages 1-19, September.
    10. Tang, Yugui & Yang, Kuo & Zhang, Shujing & Zhang, Zhen, 2022. "Photovoltaic power forecasting: A hybrid deep learning model incorporating transfer learning strategy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).

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