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Time series analysis of Bahrain's first hybrid renewable energy system

Author

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  • Bin Shams, Mohamed
  • Haji, Shaker
  • Salman, Ali
  • Abdali, Hussain
  • Alsaffar, Alaa

Abstract

The performance of multisource renewable energy system depends strongly on the meteorological parameters pertinent to the energy generating systems. Therefore, a method of modelling and forecasting meteorological and system parameters is necessary for efficient operation of the renewable energy power management system. Bahrain's first hybrid renewable energy system utilizes two renewable energy sources, namely solar irradiance through a 4.0 kWp PV (photovoltaic) panel and wind through a 1.7 kWp wind turbine. The focus of the present work is to investigate the proficiency of the Box–Jenkins based modelling approach in analysing and forecasting the daily averages of wind speed, solar irradiance, ambient air temperature, and the PV module temperature. Different non-seasonal ARIMA (Autoregressive Integrated Moving Average) models have been constructed. ARIMA(1,0,0), ARIMA(1,0,0), ARIMA(0,1,2), and ARIMA(0,1,1) have been found adequate in capturing the auto-correlative structure of the daily averages of wind speed, solar irradiance, ambient air temperature, and PV module temperature, respectively. In addition, a functional relationship that correlates the diurnal PV module temperature to the ambient air temperature and solar irradiance have been developed. Residual and forecasting analyses have been used to ensure the adequacy of the identified models.

Suggested Citation

  • Bin Shams, Mohamed & Haji, Shaker & Salman, Ali & Abdali, Hussain & Alsaffar, Alaa, 2016. "Time series analysis of Bahrain's first hybrid renewable energy system," Energy, Elsevier, vol. 103(C), pages 1-15.
    • Handle: RePEc:eee:energy:v:103:y:2016:i:c:p:1-15
    DOI: 10.1016/j.energy.2016.02.136
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    Citations

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    Cited by:

    1. Chauhan, Anurag & Saini, R.P., 2017. "Size optimization and demand response of a stand-alone integrated renewable energy system," Energy, Elsevier, vol. 124(C), pages 59-73.
    2. Sunme Park & Soyeong Park & Myungsun Kim & Euiseok Hwang, 2020. "Clustering-Based Self-Imputation of Unlabeled Fault Data in a Fleet of Photovoltaic Generation Systems," Energies, MDPI, vol. 13(3), pages 1-16, February.
    3. Brahim Belmahdi & Mohamed Louzazni & Mousa Marzband & Abdelmajid El Bouardi, 2023. "Global Solar Radiation Forecasting Based on Hybrid Model with Combinations of Meteorological Parameters: Morocco Case Study," Forecasting, MDPI, vol. 5(1), pages 1-24, January.
    4. Dai, Yeming & Wang, Yanxin & Leng, Mingming & Yang, Xinyu & Zhou, Qiong, 2022. "LOWESS smoothing and Random Forest based GRU model: A short-term photovoltaic power generation forecasting method," Energy, Elsevier, vol. 256(C).
    5. Thomas, Dimitrios & Deblecker, Olivier & Ioakimidis, Christos S., 2016. "Optimal design and techno-economic analysis of an autonomous small isolated microgrid aiming at high RES penetration," Energy, Elsevier, vol. 116(P1), pages 364-379.
    6. Luo, Yu & Shi, Yixiang & Zheng, Yi & Gang, Zhongxue & Cai, Ningsheng, 2017. "Mutual information for evaluating renewable power penetration impacts in a distributed generation system," Energy, Elsevier, vol. 141(C), pages 290-303.
    7. J. Sadhik Basha & Tahereh Jafary & Ranjit Vasudevan & Jahanzeb Khan Bahadur & Muna Al Ajmi & Aadil Al Neyadi & Manzoore Elahi M. Soudagar & MA Mujtaba & Abrar Hussain & Waqar Ahmed & Kiran Shahapurkar, 2021. "Potential of Utilization of Renewable Energy Technologies in Gulf Countries," Sustainability, MDPI, vol. 13(18), pages 1-29, September.
    8. Saumya Verma & Raja Chowdhury & Sarat K. Das & Matthew J. Franchetti & Gang Liu, 2021. "Sunlight Intensity, Photosynthetically Active Radiation Modelling and Its Application in Algae-Based Wastewater Treatment and Its Cost Estimation," Sustainability, MDPI, vol. 13(21), pages 1-28, October.

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