Dhanalaxmi H. R., Anitha G. S., Sunil Kumar A. V.
The rapid growth of renewable energy generation in the power grid, notably from wind and solar energy resources, has made these generators a major source of uncertainty in recent years, with load behavior being the largest driver of unpredictability. Generation and load balancing are critical in the economic scheduling of manufacturing units and energy market activities. Energy forecasting can help to alleviate some of the problems that come with resource unpredictability. Solar and wind energy projections attract the scientific community and numerous research articles are provided. However, the clarity and robustness of existing models may still be improved. For solar and wind power short-term forecasting (STF), this paper proposes a Resilient Back Propagation Neural Network (RBPN) model. Because solar irradiation and wind speed are not linear and unexpected, STF is difficult to complete under changing weather circumstances. However, a Resilient Back Propagation Neural Network (RBPN) is presented and is appropriate for STF modeling. It also improves power quality in various situations, including voltage imbalance correction, active and reactive power control, and voltage regulation. Simulations performed with Matlab Simulink software are used to validate the performance of the proposed forecasting system. The suggested method also includes a sensitivity analysis of numerous input variables for the optimal model selection and model performance comparison with multiple linear regression and persistence models.
Resilient Back Propagation Neural Network, Short Term Forecasting, Solar Forecast, Wind Forecast and THD
Cite this paper
Dhanalaxmi H. R., Anitha G. S., Sunil Kumar A. V.. (2021) Resilient Back propagation Neural Network-Based Hybrid Wind and Photovoltaic Short Term Forecasting System. International Journal of Power Systems, 6, 94-104