Supervisor
Dr. Shree Acharya
Programme
MSc in Data Analytics
Subject
Computer Science
Abstract
This study presents an AI-driven framework for optimising energy retrofit interventions in Irish residential buildings, combining Q-Learning (QL), surrogate models (ANN, RF), and a Genetic Algorithm (GA) optimiser. Q-Learning simulated sequential retrofit decisions, generating a dataset used to train surrogate models for predicting Building Energy Rating (BER) and CO₂ outcomes. The GA explored retrofit combinations to maximise BER improvement, CO₂ reduction, and financial returns. The framework identified strategies achieving up to 928 kWh/m² BER reduction and 1,563 kg CO₂ reduction per building, with strong ROI and payback within 15 years. The modular, interpretable pipeline demonstrates the practical value of reinforcement learning for multi-step, multi-criteria building retrofit optimisation.
Date of Award
2025
Full Publication Date
2025
Access Rights
open access
Document Type
Capstone Project
Resource Type
thesis
Recommended Citation
Tserendorj, E.
(2025) Data-Driven Decarbonisation Strategy for Residential Buildings: A Q-Learning-Based Simulation Approach. CCT College Dublin.
DOI: https://doi.org/10.63227/652.299.91