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August 9, 2026Engineering Applications of Artificial IntelligenceOpen Access

Approximate model predictive control for microgrid energy management via imitation learning

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Authors

CLChangrui LiuSSShengling ShiAAAnıl Alan

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Overview

Randomized trial demonstrates effective energy management in microgrids, suggesting new AI techniques can enhance efficiency.

Key Points

  • This research aims to develop a fast, reliable framework for microgrid energy management using imitation learning to approximate Economic Model Predictive Control (EMPC).
  • A neural network is trained to replicate expert EMPC actions based on historical data.
  • Noise injection is used during training to enhance model robustness against uncertainties.
  • A constraint-tightening method is coupled with a projection layer for improving feasibility.
  • The learned policy reduces computation time by approximately one order of magnitude relative to optimization-based EMPC.
  • Economic performance of the policy is comparable to that of the EMPC.
  • The approach effectively adapts to variations in renewable generation and demand.

Cite This Study

Liu et al. (2026) studied this question.

synapsesocial.com/papers/6a782cb82e1896536c83f9c1https://doi.org/10.1016/j.engappai.2026.115837
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Also Consider

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

  1. 1Real‐Time Hyperstructural Adaptive Economic Model Predictive Control for Cost‐Efficient Microgrid Operation2026
  2. 2A novel data-driven NLMPC strategy for techno-economic microgrid management with battery energy storage under uncertainty2025
  3. 3Інтелектуальне прогнозувальне керування та реконфігурація мережі в реальному часі для мінімізації втрат енергії в мікромережах2026
  4. 4AI-Driven Energy Management in Smart Microgrids with Renewable Integration Using Deep Reinforcement Learning2026
  5. 5Multi-microgrid Energy Management Based on Finite Horizon Twin Delayed Deep Deterministic Policy Gradient2024