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Enhancing Building-Integrated Photovoltaic Power Forecasting with a Hybrid Conditional Generative Adversarial Network Framework

Author

Listed:
  • Dong Ha Choi

    (School of Computer Science, The Usiversity of Sydney, Sydney, NSW 2006, Australia)

  • Wei Li

    (School of Computer Science, The Usiversity of Sydney, Sydney, NSW 2006, Australia)

  • Albert Y. Zomaya

    (School of Computer Science, The Usiversity of Sydney, Sydney, NSW 2006, Australia)

Abstract

This paper presents a novel framework that integrates Conditional Generative Adversarial Networks (CGANs) and TimeGAN to generate synthetic Building-Integrated Photovoltaic (BIPV) power data, addressing the challenge of data scarcity in this domain. By incorporating time-related attributes as conditioning information, our method ensures the preservation of chronological order and enhances data fidelity. A tailored learning scheme is implemented to capture the unique characteristics of solar power generation, particularly during sunrise and sunset. Comprehensive evaluations demonstrate the framework’s effectiveness in generating high-quality synthetic data, evidenced by a 79.58% improvement in the discriminative score and a 13.46% improvement in the predictive score compared to TimeGAN. Moreover, integrating the synthetic data into forecasting models resulted in up to 23.56% improvement in mean absolute error (MAE) for BIPV power generation predictions. These results highlight the potential of our framework to enhance prediction accuracy and optimize data utilization in renewable energy applications.

Suggested Citation

  • Dong Ha Choi & Wei Li & Albert Y. Zomaya, 2024. "Enhancing Building-Integrated Photovoltaic Power Forecasting with a Hybrid Conditional Generative Adversarial Network Framework," Energies, MDPI, vol. 17(23), pages 1-24, November.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:23:p:5877-:d:1527589
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    References listed on IDEAS

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    1. Mohan, Neethu & Soman, K.P. & Sachin Kumar, S., 2018. "A data-driven strategy for short-term electric load forecasting using dynamic mode decomposition model," Applied Energy, Elsevier, vol. 232(C), pages 229-244.
    2. Qu, Jiaqi & Qian, Zheng & Pei, Yan, 2021. "Day-ahead hourly photovoltaic power forecasting using attention-based CNN-LSTM neural network embedded with multiple relevant and target variables prediction pattern," Energy, Elsevier, vol. 232(C).
    3. Jeong, Jaeik & Kim, Hongseok, 2021. "DeepComp: Deep reinforcement learning based renewable energy error compensable forecasting," Applied Energy, Elsevier, vol. 294(C).
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    Cited by:

    1. Mahdiyeh Tabatabaei & Ernesto Antonini, 2025. "Machine Learning for Optimizing Urban Photovoltaics: A Review of Static and Dynamic Factors," Sustainability, MDPI, vol. 17(18), pages 1-34, September.
    2. Hui, Tan Kah & Hwee, Ah Khoon & QingHua, Liu & Jing, Khoo Terh & Ohueri, Chukwuka Christian, 2026. "From state-of-the-art to next-generation solutions: A review for Gen-AI integration in photovoltaic maintenance," Applied Energy, Elsevier, vol. 402(PB).

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