The intermittent nature of renewable energy sources such as solar and wind power poses significant challenges for grid stability and energy management. Accurate forecasting is crucial for mitigating these challenges, as traditional models such as Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average (SARIMA) often fail to capture the non-linear relationships between weather patterns and energy generation. To address this limitation, this research proposes a machine learning framework leveraging Convolutional Neural Networks (CNNs) for spatial pattern recognition and Recurrent Neural Networks (RNNs) for time-series forecasting. By integrating system design parameters with meteorological data, the framework aims to enhance prediction accuracy. The potential outcomes of this framework are not just improved grid stability, optimized energy storage utilization, and reduced operational costs, but also a significant step towards the efficient integration of renewable energy into the power system, fostering a sense of optimism for the future of renewable energy forecasting.
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