Impact of weather variables on roof top green energy production in a multibuilding, multi-capacity ecosystem – A regression based study of 'SAI MITRA’
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Abstract
The importance of green energy in the current times cannot be understated. The ill effects of traditional forms of energy generation, make solar energy one of the most environmentally friendly alternatives. SAI MITHRA – a roof top multi building, multi-capacity solar energy generation system, executed by Sri Sathya Sai Central Trust at Prasanthinilayam in South India is a prime example for promotion of green energy. Using a regression model, this study attempts to understand the impact of weather variables on solar energy production across different production capacities using high frequency daily data. In order to provide predictive insights, the impact of weather variables with t-1 and t-2 days lags on solar energy generation have also been studied. The study identifies the three important weather variables that have an impact on solar energy production – atmospheric pressure, relative humidity and dew point temperature. The insights from the paper are relevant for multi-capacity solar energy systems for improving operational efficiencies and promoting green energy ecosystems.
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References
Alexander, R. A. (1990). A note on averaging correlations. Bulletin of the Psychonomic Society, 28(4), 335-336.
Curtis, E. A., Comiskey, C., & Dempsey, O. (2016). Importance and use of correlational research. Nurse researcher, 23(6).
Detyniecki, M., Marsala, C., Krishnan, A., & Siegel, M. (2012, June). Weather-based solar energy prediction. In 2012 IEEE International Conference on Fuzzy Systems (pp. 1-7). IEEE.
Gazela, M., & Mathioulakis, E. (2001). A new method for typical weather data selection to evaluate long-term performance of solar energy systems. Solar Energy, 70(4), 339-348.
Gherboudj, I., & Ghedira, H. (2016). Assessment of solar energy potential over the United Arab Emirates using remote sensing and weather forecast data. Renewable and Sustainable Energy Reviews, 55, 1210-1224.
Hi, A., & Bouhelal, A. (2023). Machine learning-based short-term solar power forecasting: A Comparison Between Regression and Classification Approaches Using Extensive Australian Dataset.
Jain, A: (2021). Performance analysis of large scale solar PV Plants: Study of MW to MWh. Retrieved from: https://www.energyforum.in/home/2021/20210401-performance-analysis-of-large-scale-solar-pv/ on 15th Jan 2024
Kadampur, M. A. B. (2024). Solar Energy Data Analysis & Predictive Modeling: A Case Study on Open Data of Saudi Arabian Solar Energy. Retrieved from https://www.preprints.org/manuscript/202407.1971/v1
Kim, J. G., Kim, D. H., Yoo, W. S., Lee, J. Y., & Kim, Y. B. (2017). Daily prediction of solar power generation based on weather forecast information in Korea. IET Renewable Power Generation, 11(10), 1268-1273.
Köhler, C., Steiner, A., Saint-Drenan, Y. M., Ernst, D., Bergmann-Dick, A., Zirkelbach, M., ... & Ritter, B. (2017). Critical weather situations for renewable energies–Part B: Low stratus risk for solar power. Renewable Energy, 101, 794-803.
Liu, D., Xu, Y., Wei, Q., & Liu, X. (2017). Residential energy scheduling for variable weather solar energy based on adaptive dynamic programming. IEEE/CAA Journal of Automatica Sinica, 5(1), 36-46.
Maulud, D., & Abdulazeez, A. M. (2020). A review on linear regression comprehensive in machine learning. Journal of applied science and technology trends, 1(2), 140-147.
Nicoletti, F., & Bevilacqua, P. (2024). Hourly Photovoltaic Production Prediction Using Numerical Weather Data and Neural Networks for Solar Energy Decision Support. Energies, 17(2), 466.
Ozili, P. K. (2023). The acceptable R-square in empirical modelling for social science research. In Social research methodology and publishing results: A guide to non-native english speakers (pp. 134-143). IGI global.
Purohit, I., Purohit, P., & Shekhar, S. (2013). Evaluating the potential of concentrating solar power generation in Northwestern India. Energy policy, 62, 157-175.
Rana, M., Koprinska, I., & Agelidis, V. G. (2016, July). Solar power forecasting using weather type clustering and ensembles of neural networks. In 2016 International Joint Conference on Neural Networks (IJCNN) (pp. 4962-4969). IEEE.
Salemdeeb, M., & Wadi, M. (2024, September). Estimation of Solar Systems Energy Generation Based on Machine Learning. In 2024 8th International Artificial Intelligence and Data Processing Symposium (IDAP) (pp. 1-6). IEEE.
Seeram, E. (2019). An overview of correlational research. Radiologic technology, 91(2), 176-179.
Sharma, C., Sharma, A. K., Mullick, S. C., & Kandpal, T. C. (2015). Assessment of solar thermal power generation potential in India. Renewable and Sustainable Energy Reviews, 42, 902-912.
Sun, M., Feng, C., & Zhang, J. (2020). Probabilistic solar power forecasting based on weather scenario generation. Applied Energy, 266, 114823.
Sureiman, O., & Mangera, C. M. (2020). F-test of overall significance in regression analysis simplified. Journal of the Practice of Cardiovascular Sciences, 6(2), 116-122.
Tarawneh, Q. Y., & Faraj, T. K. (2020). The effect of anthropogenic activity on the extreme climate events and solar irradiation in Saudi Arabia. Arabian Journal of Geosciences, 13, 1-14.
Tursunov, M. N., Sabirov, K., Axtamov, T. Z., Abdiyev, U. B., Chariyev, M. M., Yuldoshov, B. A., & Toshpulatov, S. F. (2023). Capacity Utilization Factor (CUF) of the 70kW on-grid solar station in the dry climate of Termez. In E3S Web of Conferences (Vol. 401, p. 02059). EDP Sciences.
Vasisht, M. S., Srinivasan, J., & Ramasesha, S. K. (2016). Performance of solar photovoltaic installations: Effect of seasonal variations. Solar Energy, 131, 39-46.
Wang, Z., Koprinska, I., & Rana, M. (2017, May). Solar power prediction using weather type pair patterns. In 2017 International Joint Conference on Neural Networks (IJCNN) (pp. 4259-4266). IEEE.
Zainaa, S., Sadaf, S., Kunjuc, A., Meraj, M., Unal, D., & Touati, F. (2021). Statistical Assessment of Renewable Energy Generation in Optimizing Qatar Green Buildings. arXiv preprint arXiv:2103.07881.
Zhou, C., Doroodchi, E., Munro, I., & Moghtaderi, B. (2011). A feasibility study on hybrid solar–geothermal power generation. In New Zealand geothermal workshop (pp. 1-7).