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Soft Computing in Renewable Energy System Modeling by Paul A. Adedeji; Obafemi O. Olatunji; Nkosinathi Madushele; Abiola O. Ajayeoba is a book available to read on EtoBox.

What is Soft Computing in Renewable Energy System Modeling about?

The global drive towards mitigating the hazards associated with nonrenewable energy sources has influenced the uptake of renewable energy sources in many countries. However, notable with many renewable energy sources are their inherent intermittency and variability. These two characteristics increase the complexity of resource prediction, integration, and management in strategic and operational resource planning. Hybrid systems of renewable energy sources have also suffered setbacks from fluctuating peak and off-peak periods of generation among constituting resources in a nexus. This raises a need for optimal system sizing.In modeling the resource variability, several models have been developed to better understand this randomness. The classical methods of forecasting, such as exponential smoothing and moving average techniques like autoregressive moving average, autoregressive integrated moving average (ARIMA), which are based on statistical/mathematical approach have been applied in several studies for resource forecasting. These methods have provided significant results. However, these techniques often assume stationarity of the time series around a time-invariant mean (Wong, Ba

Author

Paul A. Adedeji; Obafemi O. Olatunji; Nkosinathi Madushele; Abiola O. Ajayeoba

Publisher

Elsevier

Published

2021

Language

EN