APPLICATION OF MACHINE LEARNING IN SMART GRID ENERGY MANAGEMENT SYSTEMS

Abstract

The present invention provides a machine learning-based energy management system for smart grids, designed to enhance energy distribution efficiency, demand forecasting, and fault detection. The system comprises a data acquisition module that collects real-time data from sensors, grid metrics, and external sources, such as weather forecasts. A data processing module normalizes and prepares this data for machine learning analysis. Using supervised learning, a load forecasting module predicts short-term and long-term energy demand, enabling proactive energy distribution. A demand response optimization module, leveraging reinforcement learning, dynamically adjusts load distribution policies based on real-time grid conditions to reduce peak loads and manage energy costs. An unsupervised anomaly detection module identifies irregular patterns indicative of faults or unauthorized access, enhancing grid reliability and security. A control module coordinates load balancing, demand response actions, and fault managemen

Specification

Description:The embodiments of the present invention generally relates to the field of smart grid energy management, specifically focusing on methods and systems utilizing machine learning algorithms for optimizing and controlling energy distribution within smart grids. The invention addresses critical aspects of load forecasting, demand response, and anomaly detection, leveraging advanced data analytics and artificial intelligence techniques to enhance grid resilience, efficiency, and reliability, especially in the context of integrating renewable energy sources and managing variable consumption patterns.
BACKGROUND OF THE INVENTION
The following description of related art is intended to provide background information pertaining to the field of the disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section be used only to enhance the understanding of the reader with respect to the present disclosur , Claims:1. A machine learning-based energy management system for smart grids, comprising:
a data acquisition module configured to collect real-time data from multiple sensors and sources across the smart grid, including consumer usage data, grid performance metrics, and environmental factors;
a data processing module configured to preprocess the collected data by normalizing, handling missing values, and filtering out anomalies;
a load forecasting module that uses supervised machine learning models to predict short-term and long-term energy demand based on historical and real-time data;
a demand response optimization module utilizing reinforcement learning algorithms to dynamically adjust load distribution policies within the grid in response to real-time conditions;
an anomaly detection module employing unsupervised machine learning techniques to identify irregular patterns and potential faults in energy usage data;
a control module configured to execute load balancing, demand response, and fault management actions

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