Robust Estimation for Solar Radiation in Renewable Energy Systems
Abstract views: 68
/ 
PDF downloads: 44
DOI:
https://doi.org/10.59287/ijanser.1415Keywords:
Artificial, İntelligence, Machine Learning, Prediction, Solar Radiation, Renewable EnergyAbstract
Renewable energy is gaining huge respect due to the increase in demand for energy globally, low cost production, and sustainability of the environment. The one to mention out of all renewable energies is solar energy. Due to its availability in abundance. A wide range of people are driven to invest in this industry. Because of its high co-linearity, it's important that proper estimation of solar radiation is necessary for long-term performance, management of energy, and economic aspects. In this paper global horizontal irradiance is predicted from input parameters namely, air temperature, relative humidity, 5 wind speed, wind spend of ghust, Wind direction in degrees north counted clockwise (standard deviation), Wind direction in degrees north counted clockwise, day length, and air pressure. Based on these factors the paper investigates the performance of different machine-learning models for solar radiation estimation. R2(coefficient of determination) of the models is compared, and the best one, i.e., gradient boost regressor model is selected which predictssolar radiation in Pakistan from metrological data. Considering wind speed of gust and wind direction in a single model with high accuracy, and aids knowledge to predictions using machine learning models. The paper is part of an ongoing effort in the research community to predict solar radiation with ease and accuracy. As real-time data is difficult and costly due to high calibration costs.
Downloads
References
. A. Mosavi, M. Salimi, SF. Ardabili, T. Rabczuk, S Shamshirband, AR. Varkonyi-Koczy “State of the art of machine learning models in energy systems, a systematic review” Energies, vol. 12, pp. 1301-1313, 2020.
. SP. Mishra, PK. Dash “Short term wind speed prediction using multiple kernel pseudo inverse neural network.” Int J Autom Comput, vol. 15, pp. 66–83, 2017.
. M. Wiston, KM. Mphale “Weather forecasting: from the early weather wizards to modern–day weather forecasting.” J Climatol Weather Forecast, vol. 6, no. 2, pp. 1–9, 2018.
. G. Memarzadeh, F. Keynia “A new short term wind speed forecasting method based on fine-tuned LSTM neural network and optimal input sets.” Energy Convers Manag, vol. 213, pp. 112824-112840, 2020.
. RP. Shetty, A. Satyabhama, PS. Pai “Wind speed prediction and modelling: a comparison of feed forward neural networks.” Int J Mech Product Eng Res Develop, vol. 10, pp. 116–127, 2020.
. SR. Moreno, RG. da Silva, VC. Mariani, LS. Coelho “Multi- step wind speed forecasting based on hybrid multi-stage decompo- sition model and long short-term memory neural network.” Energy Convers Manage, vol. 213, pp. 12869-12882, 2020.
. AH. Elsheikh, SW. Sharshir, AE. Mohamad, AE. Kabeel, W. Guilan, Z. Haiou “Modeling of solar energy system using artificial neural network: a comprehensive review”. Sol Energy, vol. 180, pp. 622–639, 2019.
. JM. Barrera, Reina A, Maté A, Trujillo JC “Solar energy prediction model based on artificial neural networks and open data”, Sustainability vol. 12, pp. 6915-6924, 2020.
. H. Wang, Y. Liua, B. Zhoub, C. Lib, G. Caoa, N. Voropaic, E. Barakht- enkoc “Taxonomy research of artificial intelligence for deterministic solar power forecasting.” Energy Convers Manage, vol. 214, pp. 1–17, 2020.
. J. Chorowski, JM. Zurada “Extracting rules from neural networks as decision diagrams.” IEEE Trans Neural Networks, vol. 22(12), pp. 2435–2446, 2011.
. SK. Biswas, N. Sinha, B. Purkayastha, L. Marbaniang “Weather prediction by recurrent neural network dynamics.” Int J Intell Eng Inf, vol. 2, no.2/3, pp. 166–180, 2014.
. V. Wu, G. Huang, J. Fan, F. Zhang, X. Wang, W. Zeng. “Potential of kernel-based non- linear extension of Arps decline model and gradient boosting with categorical features support for predicting daily global solar radiation in humid regions.” Energy Convers Manag, vol. 183, pp. 280–95, 2019.
. M. Marzouq, Z. Bounoua, H. El Fadili, A. Mechaqrane, K. Zenkouar, Z. Lakhliai. “New daily global solar irradiation estimations model based on automatic selection of input parameters using evolutionary artificial neural networks.” J Clean Prod, vol. 209, pp. 1105–18, 2019.
. L. Wang, O. Kisi, M. Zounemat-Kermani, GA. Salazar, Z. Zhu, W. Gong. “Solar radiation prediction using different techniques: model evaluation and comparison.” Renew Sustain Energy Rev, vol. 61, pp. 384–97, 2016.
. Mousavi SM, Mostafavi ES, Jiao P. “Next generation prediction model for daily solar radiation on horizontal surface using a hybrid neural network and simulated an- nealing method Energy”, Convers Manag, vol. 153, pp. 671–82, 2017.
. Y.Feng,N. Cui, Y. Chen, D. Gong, X. Hu. “Development of data-driven models for prediction of daily global horizontal irradiance in northwest China”, J Clean Prod, vol. 223, pp. 136–146, 2019.
. Y. Feng, D. Gong, Q.Zhang, S.Jiang, L. Zhao, N.Cui. “Evaluation of temperature-based machine learning and empirical models for predicting daily global solar radiation”, Energy Convers Manag, vol. 198, pp.117-122, 2019.
. H. Khalid, M. Zakaria.). “Estimation of solar radiation in southern areas of Pakistan using radiation models”. Journal of Renewable and Sustainable Energy, vol. 8(4), 2016
