Energy Audit Reporting Automation with Artificial Intelligence (AI) and Machine Learning (ML)

Currently, energy audits at Dillon Consulting Limited take 2 to 3 days to generate comprehensive reports, consuming significant auditor time due to the manual process. This inefficiency diverts auditors from critical tasks and introduces inconsistencies, especially in buildings with multiple facilities. It limits Dillon Consulting’s ability to meet increasing demand for energy management practices. There is a critical need for advanced AI techniques capable of processing inputted data, generating human-quality reports, and adapting to evolving needs. This project addresses this gap by developing an automated system using AI to transform the manual audit report process. The proposed system automates data transfer from site visits into pre-formatted Word templates using AI to generate unique descriptions. By leveraging past reports for references, the software will retrieve relevant data for similar building types and systems, automating 75–80% of the report. Auditors will only need minimal adjustments, reducing workload and improving consistency. Over time, the AI tool will adapt and improve with new data, creating a robust, efficient, and scalable solution for energy auditing.

Faculty Supervisor:

Alan Fung

Student:

Partner:

Dillon Consulting Limited

Discipline:

Engineering

Sector:

Professional, scientific and technical services

University:

Toronto Metropolitan University

Program: