Accurate Neural Prophecy for Short-Term Load Forecasting of Optimal Renewable Energy

The integration of intermittent and unpredictable renewable energy sources into the power grid presents significant challenges, particularly in terms of maintaining grid stability. Accurate short-term load forecasting plays a crucial role in addressing these challenges. This research focuses on improving load forecasting accuracy by developing an advanced neural prophet-based model that effectively incorporates weather data. By utilizing historical data and relevant weather variables during the model training process, the research aims to evaluate its performance in capturing both short-term trends and long-term seasonal patterns. The findings of this study will play a substantial role for enhancing the operational planning and management of renewable energy integration into the power grid, ultimately facilitating a more efficient and reliable integration of renewable energy sources.