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Related Experiment Videos

Machine learning approaches for resource management and forecasting in energy consumption systems.

Hesam Mohammad Asghari1, Ali Ghaffari2,3,4

  • 1Department of Computer Engineering, Faculty of Engineering and Natural Science, Istinye University, Istanbul, 34396, Turkey.

Scientific Reports
|May 9, 2026
PubMed
Summary

Artificial Intelligence (AI) enhances Renewable Energy Systems (RES) by addressing intermittency and forecasting uncertainty. AI-driven predictive maintenance reduces operational costs, while advanced models ensure grid stability and optimal resource allocation.

Keywords:
Deep learningEnergy consumption systemsLSTMMachine learningResource management

Related Experiment Videos

Area of Science:

  • Renewable Energy Systems
  • Artificial Intelligence
  • Data Science

Background:

  • Renewable Energy Systems (RES) are crucial for global sustainability but face challenges like power outages, instability, and economic losses due to intermittency, forecasting uncertainty, and grid integration issues.
  • Technological advancements focus on hybridizing deep learning models with optimization algorithms to manage RES uncertainty.
  • Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) offer solutions for predicting energy generation and optimizing maintenance.

Purpose of the Study:

  • To present an innovative AI-based approach for stabilizing Renewable Energy Systems.
  • To address technical hurdles in RES, including intermittency, forecasting uncertainty, and grid integration.
  • To reduce operational expenditure and enhance grid stability in RES.

Main Methods:

  • A four-stage scientific data cleaning method combined with a two-phase AI stabilization system.
  • Utilizing the Markowitz Model for optimal hybrid resource ratio determination (wind and solar).
  • Implementing an energy-constrained battery smoothing process for residual fluctuation reduction.
  • Applying Deep Learning models like Long Short-Term Memory (LSTM), Support Vector Machines (SVM), and Random Forest (RF) for prediction and maintenance scheduling.

Main Results:

  • AI-based predictive maintenance can decrease operational expenditure (OPEX) of RES by up to 30.0%.
  • The Long Short-Term Memory (LSTM) model achieved a high predictive accuracy with a Coefficient of Determination (R²) of up to 0.988.
  • A two-stage AI framework integrating Markowitz-based portfolio optimization and battery control reduced grid stability variance by an average of 95.4%.

Conclusions:

  • AI provides a robust framework for overcoming significant challenges in Renewable Energy Systems.
  • The proposed AI stabilization system effectively enhances grid stability and reduces operational costs.
  • Hybridizing AI with optimization techniques and advanced DL models is key to efficient and reliable RES operation.