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

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.