Modeling, simulation, and prediction of global energy indices: a differential approach

Nnamchi, Stephen Ndubuisi; Nnamchi, Onyinyechi Adanma; Busingye, Janice Desire; Ijomah, Maxwell Azubuike; Obasi, Philip Ikechi

doi:10.1007/s11708-021-0723-6

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Cited by 2 publications

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Solar energy is becoming more and more incorporated into the global power grid. As a result, enhancing the accuracy of solar energy projections is crucial for effective power grid planning, control, and operations. A fast, accurate and advanced estimation method is desperately needed to prevent PV's detrimental consequences on electricity and energy networks. For the optimum integration of solar technology into existing power systems, which benefits both grids and station operators, accurate prediction of solar production is crucial. The purpose of this research is to test the effectiveness of the machine learning model for projecting PV solar output. Using ANN in this research, weather parameters with the Power Generation for the next day appear to have been predicted. The evaluation findings suggest that the models' accuracy is sufficient to be employed with existing works and their approaches. Machine learning was shown to be capable of accurately predicting power while removing the difficulties associated with predicted solar irradiance data in this study.

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Modeling, simulation, and prediction of global energy indices: a differential approach

Cited by 2 publications

References 30 publications

State-owned capital and quality of green innovation: Evidence from Chinese listed private firms

State-owned capital and quality of green innovation: Evidence from Chinese listed private firms

Power Generation Prediction in Solar PV system by Machine Learning Approach

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