AI-Driven Multi-Agent Demand Response Framework for Residential Load Optimization Using CrewAi

The rising electricity demand, particularly in the residential sector, necessitates innovative strategies to manage peak loads effectively. This research proposes a novel Automatic Demand Response (ADR) framework using a multi-agent system called CrewAi, designed to optimize residential energy consumption during peak periods. The framework consists of five autonomous agents, coordinated by a central Client Agent, aimed at reducing peak demand and enhancing grid stability. A key component of CrewAi is the Load Forecasting Agent (LFA), which leverages Large Language Models (LLMs) to predict residential energy demand with high accuracy. The model excels in clustering data patterns, enabling precise, achieving superior accuracy for 1-day, 2-day, and 3-day predictions during peak demand periods. The Demand Response Optimizer (DRO) classifies energy loads into shiftable, adaptable, and interruptible loads, facilitating dynamic load shifting from peak to off-peak hours. To Ensure the effective of CrewAi in Prediction, it compared with Long Short-Term Memory (LSTM) networks and Optimized-LSTM. The proposed CrewAi approach results of 14% reduction in residential energy consumption without compromising user comfort., demonstrating enhanced scalability and adaptability. The proposed framework marks a paradigm shift from traditional rule-based ADR systems to intelligent, interconnected agent networks, offering a robust solution to address modern grid complexities and uncertainties in residential energy management.