AI-Based Load Forecasting for Renewable Energy Optimization in Smart Grids

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@article{1710266,
      author = {Rasheedah Fola Abioye, Gloria Siwe Usiagu, Sadat Itohan Ihwughwavwe},
      title = {AI-Based Load Forecasting for Renewable Energy Optimization in Smart Grids},
      journal = {Iconic Research And Engineering Journals},
      year = {2020},
      volume = {4},
      number = {3},
      pages = {229-247},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1710266.pdf},
      abstract = {The rapid growth of renewable energy sources such as solar and wind power has introduced significant variability and uncertainty into modern power systems, particularly within smart grids. Accurate load forecasting has thus become a critical component for ensuring grid stability, optimizing renewable energy utilization, and maintaining efficient energy operations. This explores the emerging role of Artificial Intelligence (AI) in enhancing load forecasting accuracy for renewable energy optimization in smart grids. By leveraging advanced machine learning and deep learning techniques, AI-based models can effectively capture nonlinear relationships and complex temporal patterns among various influencing factors, including historical electricity consumption, weather conditions, renewable generation profiles, and socioeconomic variables.This reviews state-of-the-art AI methodologies employed for short-term, medium-term, and long-term load forecasting, such as support vector machines (SVM), random forests, artificial neural networks (ANN), and long short-term memory (LSTM) networks. Special attention is given to hybrid and ensemble approaches that combine multiple algorithms to further improve prediction performance. Additionally, this discusses critical data preprocessing techniques, including normalization, feature selection, and missing data handling, which are essential for robust AI model development.The integration of AI-based forecasting with renewable energy optimization strategies is also examined, highlighting its applications in dynamic resource allocation, demand response programs, and energy storage management. This identifies several challenges, including data availability, model interpretability, and computational demands, which must be addressed for broader deployment. Case studies from smart grid projects worldwide demonstrate the effectiveness of AI-driven forecasting in enhancing grid flexibility and renewable energy penetration. This concludes with future research directions, emphasizing explainable AI, edge computing integration, and federated learning for privacy-preserving forecasting. Overall, AI-based load forecasting presents a transformative opportunity for optimizing renewable energy systems and advancing the reliability, sustainability, and efficiency of smart grids.},
      keywords = {AI-based, Load forecasting, Renewable energy, Optimization, Smart grids},
      month = {September},
  }