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A literature review on estimating of PV-array hourly power under cloudy weather conditions

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  • Gandoman, Foad H.
  • Raeisi, Fatima
  • Ahmadi, Abdollah

Abstract

The capacity of installed photovoltaic cells (PVs) has been remarkably increased globally over past few decades. In addition, changing cloud cover (Oktas-scale) is an important component that influences on solar radiation hourly and plays a key role in electrical output power of PVs. However, it is necessary for electrical utilities to use a powerful model in order to forecast PV power systems on electrical networks. Thus, forecasting power output of PV systems increase the quality of modern power systems. This paper presents a comprehensive review on short term forecasting of solar PV power output in modern electrical networks. Additionally, a new model based on hourly measurements has been proposed in this research and was reported during the past 20 years in Sanandaj (Applied Research Center of Kurdistan Weather), located in west of Iran (14°.35′N, 46°.35′E). The results show that the possibility of changing hourly and PV output power estimate based on Oktas analysis have been calculated with minimum fault.

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  • Gandoman, Foad H. & Raeisi, Fatima & Ahmadi, Abdollah, 2016. "A literature review on estimating of PV-array hourly power under cloudy weather conditions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 63(C), pages 579-592.
    • Handle: RePEc:eee:rensus:v:63:y:2016:i:c:p:579-592
    DOI: 10.1016/j.rser.2016.05.027
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    2. Zeineb Behi & Kelvin Tsun Wai Ng & Amy Richter & Nima Karimi & Abhijeet Ghosh & Lei Zhang, 2022. "Exploring the untapped potential of solar photovoltaic energy at a smart campus: Shadow and cloud analyses," Energy & Environment, , vol. 33(3), pages 511-526, May.
    3. Gandoman, Foad H. & Abdel Aleem, Shady H.E. & Omar, Noshin & Ahmadi, Abdollah & Alenezi, Faisal Q., 2018. "Short-term solar power forecasting considering cloud coverage and ambient temperature variation effects," Renewable Energy, Elsevier, vol. 123(C), pages 793-805.
    4. VanDeventer, William & Jamei, Elmira & Thirunavukkarasu, Gokul Sidarth & Seyedmahmoudian, Mehdi & Soon, Tey Kok & Horan, Ben & Mekhilef, Saad & Stojcevski, Alex, 2019. "Short-term PV power forecasting using hybrid GASVM technique," Renewable Energy, Elsevier, vol. 140(C), pages 367-379.
    5. Li, Chengdong & Zhou, Changgeng & Peng, Wei & Lv, Yisheng & Luo, Xin, 2020. "Accurate prediction of short-term photovoltaic power generation via a novel double-input-rule-modules stacked deep fuzzy method," Energy, Elsevier, vol. 212(C).
    6. Das, Utpal Kumar & Tey, Kok Soon & Seyedmahmoudian, Mehdi & Mekhilef, Saad & Idris, Moh Yamani Idna & Van Deventer, Willem & Horan, Bend & Stojcevski, Alex, 2018. "Forecasting of photovoltaic power generation and model optimization: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 912-928.
    7. Pan Xia & Lu Zhang & Min Min & Jun Li & Yun Wang & Yu Yu & Shengjie Jia, 2024. "Accurate nowcasting of cloud cover at solar photovoltaic plants using geostationary satellite images," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    8. Dongkyu Lee & Jinhwa Jeong & Sung Hoon Yoon & Young Tae Chae, 2019. "Improvement of Short-Term BIPV Power Predictions Using Feature Engineering and a Recurrent Neural Network," Energies, MDPI, vol. 12(17), pages 1-17, August.

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