| Title | Renewable Energy Forecasting Using Artificial Neural Networks for Smart Grid Applications |
| Research Area | Renewable Energy |
| Abstract | The increasing penetration of renewable energy sources such as solar and wind into modern power grids introduces significant uncertainty due to their dependence on weather conditions. Accurate energy forecasting is essential for grid stability, efficient energy management, and reduction of power losses. Artificial Neural Networks (ANNs) have emerged as powerful tools for modeling non-linear relationships between environmental factors and renewable energy generation. This paper presents a comprehensive study of ANN-based forecasting models for solar and wind energy prediction. The proposed approach uses historical weather data and power generation records to train neural networks capable of short-term and medium-term energy forecasting. Experimental results demonstrate that ANN-based models significantly outperform traditional statistical techniques, improving forecasting accuracy by 15–20%. The study highlights the importance of renewable energy forecasting in smart grid operations and discusses challenges related to data quality and scalability. |
| Keywords | Renewable Energy, Artificial Neural Networks, Energy Forecasting, Smart Grid, Solar Power, Wind Energy |
| Paper Status | Published |
| Volume | 1 |
| Issue | 6 |
| Published On | 03/12/2025 |
| Published File |
IJSRTD_4865.pdf
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