Advanced Machine Learning Techniques for Smart ‎Grid Optimization and Energy Management

Machine Learning Operations (MLOps) refers to a set of practices and processes that aim to effectively integrate data science and machine learning in the production and support of enterprise-level applications and products. Distributed energy resources (DERs), such as photovoltaic (PV) panels, energy storage systems, and wind turbines, play a special role in making power grids smarter. These resources contribute to a more decentralized and resilient energy infrastructure, but the probabilistic nature of their energy production poses significant challenges for grid management. Data science provides powerful tools to address these challenges and optimize the operation and scheduling of DERs, to maximize their benefits and ensure grid stability. MLOps can help manage and optimize energy consumption by using machine learning algorithms to analyze energy consumption data and identify consumption patterns. Afterwards, by predicting consumption trends and identifying areas for improvement, energy consumption reduction strategies can be implemented. For example, in industries with high energy consumption, MLOps can help develop and deploy models that automatically adjust systems for greater efficiency and, as a result, reduce energy consumption. This could include adjusting building temperatures, optimizing manufacturing processes, or even better managing city traffic to avoid excessive fuel consumption. Ultimately, using MLOps, organizations can automate and continuously improve energy consumption optimization processes.

Keywords:

Advanced Machine Learning , ‎Technique , Smart Grid Optimization , ‎Energy management.‎