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Estimation of hourly global solar radiation using Multivariate Adaptive Regression Spline (MARS) – A case study of Hong Kong

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  • Li, Danny H.W.
  • Chen, Wenqiang
  • Li, Shuyang
  • Lou, Siwei

Abstract

Solar energy is the most popular resource for power generation among the various available renewable energy alternatives. Solar radiation data are important for solar systems and energy-efficient building designs. Due to the unavailability of measurement, solar radiation prediction models are required. Recently, machine learning techniques were successfully used for predicting solar radiation. However, previous works were mainly focusing on monthly average daily or daily solar radiation. In this study, models for predicting hourly global solar radiation on a horizontal surface were developed based on Multivariate Adaptive Regression Spline (MARS) method. Hourly meteorological data measured in 7 years were used for the study. Sensitivity analysis was conducted using MARS algorithm and the most important variables were selected as inputs of the proposed models. 16 MARS models with different combinations of input variables were proposed. Logistic regression and Artificial Neural Networks (ANN) methods were also used to develop models for comparative study. Finally, the proposed models were evaluated against measurements and compared with existing models. The results showed that the proposed MARS models have good performance in both prediction accuracy and interpretability. The proposed models could be used to estimate effectively the hourly solar radiation according to different combinations of measured variables.

Suggested Citation

  • Li, Danny H.W. & Chen, Wenqiang & Li, Shuyang & Lou, Siwei, 2019. "Estimation of hourly global solar radiation using Multivariate Adaptive Regression Spline (MARS) – A case study of Hong Kong," Energy, Elsevier, vol. 186(C).
    • Handle: RePEc:eee:energy:v:186:y:2019:i:c:s0360544219315294
    DOI: 10.1016/j.energy.2019.115857
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    Cited by:

    1. Ghimire, Sujan & Deo, Ravinesh C. & Casillas-Pérez, David & Salcedo-Sanz, Sancho, 2022. "Boosting solar radiation predictions with global climate models, observational predictors and hybrid deep-machine learning algorithms," Applied Energy, Elsevier, vol. 316(C).
    2. Majid Mohammady, 2023. "Badland erosion susceptibility mapping using machine learning data mining techniques, Firozkuh watershed, Iran," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 117(1), pages 703-721, May.
    3. Li, Danny H.W. & Aghimien, Emmanuel I. & Tsang, Ernest K.W., 2022. "Application of artificial neural networks in horizontal luminous efficacy modeling," Renewable Energy, Elsevier, vol. 197(C), pages 864-878.
    4. Cheng, Liang & Zhang, Fangli & Li, Shuyi & Mao, Junya & Xu, Hao & Ju, Weimin & Liu, Xiaoqiang & Wu, Jie & Min, Kaifu & Zhang, Xuedong & Li, Manchun, 2020. "Solar energy potential of urban buildings in 10 cities of China," Energy, Elsevier, vol. 196(C).
    5. Caston Sigauke & Thakhani Ravele & Lordwell Jhamba, 2022. "Extremal Dependence Modelling of Global Horizontal Irradiance with Temperature and Humidity: An Application Using South African Data," Energies, MDPI, vol. 15(16), pages 1-25, August.
    6. Alabi, Tobi Michael & Aghimien, Emmanuel I. & Agbajor, Favour D. & Yang, Zaiyue & Lu, Lin & Adeoye, Adebusola R. & Gopaluni, Bhushan, 2022. "A review on the integrated optimization techniques and machine learning approaches for modeling, prediction, and decision making on integrated energy systems," Renewable Energy, Elsevier, vol. 194(C), pages 822-849.
    7. Sadeghi, Gholamabbas & Pisello, Anna Laura & Safarzadeh, Habibollah & Poorhossein, Miad & Jowzi, Mohammad, 2020. "On the effect of storage tank type on the performance of evacuated tube solar collectors: Solar radiation prediction analysis and case study," Energy, Elsevier, vol. 198(C).
    8. Xinyu Yang & Ying Ji & Xiaoxia Wang & Menghan Niu & Shuijing Long & Jingchao Xie & Yuying Sun, 2023. "Simplified Method for Predicting Hourly Global Solar Radiation Using Extraterrestrial Radiation and Limited Weather Forecast Parameters," Energies, MDPI, vol. 16(7), pages 1-16, April.
    9. Feng, Yu & Hao, Weiping & Li, Haoru & Cui, Ningbo & Gong, Daozhi & Gao, Lili, 2020. "Machine learning models to quantify and map daily global solar radiation and photovoltaic power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 118(C).
    10. Seyed Alireza Modirzadeh & Mohsen Nasseri & Mohammad Sadegh Ahadi & Farzam Pourasghar Sangachin, 2021. "Assessing GHG mitigation goals of INDCs (NDCs) considering socio-economic and environmental indicators of the parties," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 26(8), pages 1-33, December.
    11. Peng, Tian & Zhang, Chu & Zhou, Jianzhong & Nazir, Muhammad Shahzad, 2021. "An integrated framework of Bi-directional long-short term memory (BiLSTM) based on sine cosine algorithm for hourly solar radiation forecasting," Energy, Elsevier, vol. 221(C).

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