Abstract
In this paper, we concentrate to optimize the power demand using forecasting of the solar radiation power, temperature and wind speed. We propose the hybrid technique (CNN-SSO), i.e. convolutional neural network (CNN) and short-term power forecasting model, and it is combined with the social spider optimization (SSO). The SSO algorithm is used to optimize the design constraints in order to inevitably choose the suitable widespread constraint cost of the PV power estimation. The results from simulation work have shown the estimation of the CNN-SSO method that successfully selected by the appropriate operating mode to achieve streamlining of the general vitality effectiveness of the framework utilizing every external parameter. The simulation results demonstrate the viability of CNN-SSO technique in standings of computational feasibility, accuracy and increased robustness.
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Ghosh, D. (2020). Short-Term PV Power Forecasting for Renewable Energy Using Hybrid Spider Optimization-Based Convolutional Neural Network. In: Deepak, B., Parhi, D., Jena, P. (eds) Innovative Product Design and Intelligent Manufacturing Systems. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-2696-1_78
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DOI: https://doi.org/10.1007/978-981-15-2696-1_78
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