Abstract
The primary aim of this paper is to analyze solar power variability. Ground-based measurements of solar photovoltaic power are used for the forecasting of 43-kW A-Si SPV system. In this study, we describe the variability in the power production of solar photovoltaic plant at IIT, Jodhpur. Solar PV generation forecasting is playing a key role in accurate solar power dispatchability as well as scheduling of PV power for hybrid power generation systems. The actual power produced by a PV power system varies according to variation in meteorological parameters and efficiency of PV system components. For the purpose of forecasting as per the schedule in the Indian power sector, a time slot of 15 min is considered for each forecasting. The proposed generalized neural network technique will be appropriated for modeling of solar power variability forecasting. In this paper, we used generalized neural network for forecasting the PV power variability.
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Acknowledgments
The authors would like to acknowledge the IIT Jodhpur for providing PV generation data and Central Power Research Institute, Bangalore, for financial support.
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Singh, V.P., Vijay, V., Ravindra, B., Jothi Basu, S., Chaturvedi, D.K. (2015). Ground-Based Measurement for Solar Power Variability Forecasting Modeling Using Generalized Neural Network. In: Vijay, V., Yadav, S., Adhikari, B., Seshadri, H., Fulwani, D. (eds) Systems Thinking Approach for Social Problems. Lecture Notes in Electrical Engineering, vol 327. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2141-8_5
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DOI: https://doi.org/10.1007/978-81-322-2141-8_5
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