Abstract
The stability and reliability of power in an integrated renewable energy system vary according to changes in environmental conditions, as the radiation, temperature, and humidity change continuously; the power generation through PV system also changes, and hence, the power scheduling and operation mainly depend on the estimation of power through renewable energy sources. The power generation in PV systems initially depends on the radiation and temperature, so prediction of weather conditions helps in predicting the power generation through PV solar plant. The stability of a power system can be improved by predicting solar energy which can tell approximately how much solar power can be generated in the future at a particular location. Solar power forecasting has several methods, one of all methods is using machine learning/neural networks. In this paper, the power generation with a solar plant is forecasted by predicting the future weather generation using machine learning algorithms. The accuracy of forecasting will be checked directly with the practical data which is generated and simulated data using MATLAB/Simulink by applying various machine learning algorithms.
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References
Rupesh M, Shivalingappa TV (2019) Evaluation of optimum MPPT technique for PV system using MATLAB/simulink fig 2. Equivalent circuit of PV cell. 5:1403–1408
Revana G, Kota VR (2020) Simulation and implementation of resonant controller based PV fed cascaded boost-converter three phase five-level inverter system. J King Saud Univ—Eng Sci 32(7):411–424
Trivedi S (2021) Evaluation of the use of artificial neural networks to predict the photovoltaic power generation factors by using feed forward back propagation (FFBP) technique. Int J Curr Sci Res Rev 04(02):113–119
Jawaid F, Nazirjunejo K (2017) Predicting daily mean solar power using machine learning regression techniques. 2016 6th Int Conf Innov Comput Technol INTECH 2016:355–360
Wu YK, Chen CR, Abdul Rahman H (2014) A novel hybrid model for short-term forecasting in PV power generation. Int J Photoenergy 2014
Coelho JP, Boaventura-Cunha J (2014) Long term solar radiation forecast using computational intelligence methods. Appl Comput Intell Soft Comput 2014(December):1–14
Gupta A, Kumar P, Pachauri RK, Chauhan YK (2014) Performance analysis of neural network and fuzzy logic based MPPT techniques for solar PV systems. 2014 6th IEEE Power India Int Conf 1–6
Khan I, Zhu H, Khan D, Panjwani MK (2018) Photovoltaic power prediction by cascade forward artificial neural network. 2017 Int Conf Inf Commun Technol ICICT 2017 2017(December):145–149
Ahmed R, Sreeram V, Mishra Y, Arif MD (2020) A review and evaluation of the state-of-the-art in PV solar power forecasting: techniques and optimization. Renew Sustain Energy Rev 124(June 2019):109792
Aljanad A, Tan NML, Agelidis VG, Shareef H (2021) Neural network approach for global solar irradiance prediction at extremely short-time-intervals using particle swarm optimization algorithm. Energies 14(4)
Shekher A, Khanna V (2016) Modelling and prediction of 150KW PV array system in Northern India using artificial neural network. 5(5):18–25
Kabilan R, et al. (2021) Short-term power prediction of building integrated photovoltaic (BIPV) system based on machine learning algorithms. Int J Photoenergy 2021
(2015) Multi-layer neural network neural network model. http://deeplearning.stanford.edu/tutorial/supervised/MultiLayerNeuralNetworks/. pp 1–6
Shaik NB, Pedapati SR, Ammar Taqvi SA, Othman AR, Abd Dzubir FA (2020) A feed-forward back propagation neural network approach to predict the life condition of crude oil pipeline. Processes 8(6)
Choudhary A, Pandey D, Bhardwaj S (2020) Artificial neural networks based solar radiation estimation using backpropagation algorithm. Int J Renew Energy Res 10(4):1566–1575
Acknowledgements
The authors would like to thank BVRIT, Narsapur Solar Plant in charge Mr. N. Ramchandar, Associate Professor, EEE, BVRIT, Narsapur, and Mr. M. Sudheer Kumar, Assistant Professor, BVRIT HYDERABAD College of Engineering for Women, Hyderabad.
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Rupesh, M., Swathi Chandana, J., Aishwarya, A., Anusha, C., Meghana, B. (2022). Prediction of Solar Power Using Machine Learning Algorithm. In: Hemanth, D.J., Pelusi, D., Vuppalapati, C. (eds) Intelligent Data Communication Technologies and Internet of Things. Lecture Notes on Data Engineering and Communications Technologies, vol 101. Springer, Singapore. https://doi.org/10.1007/978-981-16-7610-9_39
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DOI: https://doi.org/10.1007/978-981-16-7610-9_39
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