Title: Machine Learning-Based Forecasting for Renewable Energy Integration and Carbon Emission Mitigation in Power Systems
Authors: Jessica Obianuju Ojadi, Olumide Akindele Owulade Chinekwu Somtochukwu Odionu Ekene Cynthia Onukwulu,
Volume: 9
Issue: 3
Pages: 158-181
Publication Date: 2025/03/28
Abstract:
The increasing integration of renewable energy sources (RES) into power systems necessitates advanced forecasting techniques to manage variability and ensure grid stability. Machine learning (ML)-based forecasting models have emerged as powerful tools for predicting energy generation, demand fluctuations, and carbon emissions. This study explores the role of ML algorithms in enhancing renewable energy integration and mitigating carbon emissions in power systems. Various ML techniques, including artificial neural networks (ANNs), support vector machines (SVMs), long short-term memory (LSTM) networks, and ensemble learning methods, are analyzed for their effectiveness in forecasting renewable energy output and optimizing grid operations. Accurate forecasting of solar and wind power generation is critical for maintaining grid stability and reducing reliance on fossil fuels. ML models trained on historical weather, load demand, and power generation data provide precise predictions, enabling grid operators to make informed decisions regarding energy dispatch, storage, and load balancing. Moreover, ML-driven demand-side management strategies enhance energy efficiency by optimizing power consumption patterns and reducing peak load stress on the grid. Additionally, carbon emission forecasting plays a pivotal role in sustainable energy transition. ML models help predict emissions based on real-time energy consumption and generation data, allowing policymakers and industry stakeholders to implement targeted carbon reduction strategies. By integrating ML-based forecasting with energy storage systems and smart grids, renewable penetration can be maximized while minimizing curtailment and dependency on conventional energy sources. This study highlights key challenges in ML-based forecasting, including data quality, model interpretability, and computational complexity, while proposing solutions such as hybrid models, feature selection techniques, and real-time data assimilation. Future research directions focus on improving model robustness through federated learning, reinforcement learning, and quantum computing applications in renewable energy forecasting. In conclusion, ML-based forecasting enhances renewable energy integration by improving prediction accuracy, optimizing power system operations, and facilitating carbon emission mitigation. The implementation of advanced ML techniques in power grids contributes to a sustainable, low-carbon future while ensuring grid reliability and energy security.