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Estimate and characterize PV power at demand-side hybrid system

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  • Li, Qian
  • Wu, Zhou
  • Xia, Xiaohua

Abstract

Power forecasting, in a hybrid photovoltaic (PV) system, is an important issue regarding to the control and optimization of energy systems. In this work, multi-clustered echo state network (MCESN) models are proposed to directly perform the forecast of PV power generation. Furthermore, data characteristics of measured and estimated PV power are qualitatively investigated via data mining approaches. These characteristics include seasonality, stationarity (or non-stationarity) and complexity analysis. Simulation results indicate that the proposed MCESN model is able to precisely forecast PV power one-hour-ahead. The performance on the 24-h-ahead forecast is competitive with the correlation coefficient 99% for sunny days, and 91–98% for cloudy days. Results of data analysis unveil that critical characteristics between the measured and estimated PV power data are analogous. Comparison studies also show that MCESN could achieve more accurate prediction, compared with auto-regressive moving average (ARMA), back propagation (BP) neural networks.

Suggested Citation

  • Li, Qian & Wu, Zhou & Xia, Xiaohua, 2018. "Estimate and characterize PV power at demand-side hybrid system," Applied Energy, Elsevier, vol. 218(C), pages 66-77.
    • Handle: RePEc:eee:appene:v:218:y:2018:i:c:p:66-77
    DOI: 10.1016/j.apenergy.2018.02.160
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    2. Zhen, Hao & Niu, Dongxiao & Wang, Keke & Shi, Yucheng & Ji, Zhengsen & Xu, Xiaomin, 2021. "Photovoltaic power forecasting based on GA improved Bi-LSTM in microgrid without meteorological information," Energy, Elsevier, vol. 231(C).
    3. Zheng, Lingwei & Liu, Zhaokun & Shen, Junnan & Wu, Chenxi, 2018. "Very short-term maximum Lyapunov exponent forecasting tool for distributed photovoltaic output," Applied Energy, Elsevier, vol. 229(C), pages 1128-1139.
    4. Haobo Shi & Yanping Xu & Baodi Ding & Jinsong Zhou & Pei Zhang, 2023. "Long-Term Solar Power Time-Series Data Generation Method Based on Generative Adversarial Networks and Sunrise–Sunset Time Correction," Sustainability, MDPI, vol. 15(20), pages 1-19, October.
    5. Amir Mosavi & Mohsen Salimi & Sina Faizollahzadeh Ardabili & Timon Rabczuk & Shahaboddin Shamshirband & Annamaria R. Varkonyi-Koczy, 2019. "State of the Art of Machine Learning Models in Energy Systems, a Systematic Review," Energies, MDPI, vol. 12(7), pages 1-42, April.

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