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February 5, 2026Energies3 citationsOpen Access

Artificial Intelligence and Machine Learning Models for Forecasting, Optimization, and Control in Smart Energy Systems

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GDGrzegorz DudekMBMarcin Blachnik

Key Points

  • The central aim is to explore how AI and machine learning can enhance forecasting, optimization, and control in energy systems.
  • Review of existing AI and machine learning models
  • Analysis of their applications in energy systems
  • Evaluation of effectiveness in forecasting and optimization
  • AI and machine learning significantly improve the accuracy of energy demand forecasting.
  • Optimization models reduce energy consumption and costs.
  • Control mechanisms enhance integration of renewable energy sources into energy systems.

Abstract

The global energy sector is undergoing a profound and multidimensional transformation driven by decarbonization policies, increasing electrification, large-scale integration of renewable energy sources, and the growing digitalization of energy infrastructures ...

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Cite This Study

Dudek et al. (2026) studied this question.

synapsesocial.com/papers/6984346ff1d9ada3c1fb2910https://doi.org/10.3390/en19030768
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Applications of Machine Learning and Artificial Intelligence in Modern Power and Energy Systems2026
  2. 2Emerging paradigms in the energy sector: Forecasting and system control optimisation2026
  3. 3Predictive Energy: Harnessing Artificial Intelligence for Sustainable Energy Forecasting and Management2025 · 7 citations
  4. 4Artificial Intelligence in Energy Sustainability: Predicting, Analyzing, and Optimizing Consumption Trends2025 · 27 citations
  5. 5ARTIFICIAL INTELLIGENCE FOR THE OPTIMIZATION OF RENEWABLE ENERGY SYSTEMS: EMERGING METHODS AND APPLICATIONS2026