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- W3135364275 abstract "Future is envisaged to have renewable energy resources replacing conventional sources of energy like fossil fuels. In this direction solar energy is emerging out to be a vital source of green energy. Although solar energy is a promising aspect in proving clean and cheap electrical energy but one demerit is that it is intermittent nature and therefore unpredictable. This intermittent nature pose challenge maintaining the balance between generation and demand of electrical energy thus adversely effecting the system control. Also, the electrical energy companies involved in selling by participating in electricity pool market need highly accurate solar energy prediction for maximizing their profit. These issues demand a tool for accurate prediction of solar energy generation. This paper proposes a multi-layered feedforward neural network based solar energy prediction tool. For this a case study is done considering weather data of Malviya National Institute of Technology Jaipur. The data is used to train the neural network to predict the solar radiation. The trained neural network is tested with unseen data and shown to have reasonably good accuracy in predicting solar radiation. A comparative study of neural network performance is done with multiple-linear regression (MLR) technique in terms of prediction accuracy. It is shown that neural network (NN) based prediction outperforms MLR technique. It is expected that proposed methodology would find useful application with electricity companies involved in selling renewable energy generation." @default.
- W3135364275 created "2021-03-15" @default.
- W3135364275 creator A5021037856 @default.
- W3135364275 date "2020-12-16" @default.
- W3135364275 modified "2023-09-23" @default.
- W3135364275 title "Solar Power Output Prediction Using Multilayered Feedforward Neural Network: A Case Study of Jaipur" @default.
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- W3135364275 doi "https://doi.org/10.1109/isssc50941.2020.9358821" @default.
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