Reinforcement-Based Large Language Models to Enhance Energy Management Platform

Reinforcement Learning in Large Language Models (RL-LLM) is revolutionizing how AI understands and responds to human language, improving virtual assistants in various fields. It teaches models to follow instructions effectively, even with vague prompts, reducing inappropriate responses and false information. RL-LLM’s application extends to sectors like healthcare, finance, and online shopping, enhancing product suggestions and aiding quick medical information comprehension.
The project further explores RL’s role in energy management, aiming to refine algorithms for decision-making accuracy. It integrates IoT and Edge Computing for efficient data acquisition, develops adaptive algorithms for changing energy patterns, and ensures ethical AI implementation. Ultimately, the project aims to advance Edgecom’s energy management platform for smarter, more efficient, and responsible solutions.
AI is a key development theme for Edgecom moving forward and this project will help supplement our limited expertise and experience in this field. This project will provide significant benefit to the company in the form of increased innovation and product development capacity, an improved product offering, and further differentiation versus competition.

Faculty Supervisor:

Alireza Bakhshai

Student:

Partner:

Edgecom Energy

Discipline:

Engineering

Sector:

Professional, scientific and technical services

University:

Queen's University

Program: