Prediction of Building Energy Consumption Using Machine Learning

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Insinööritieteiden korkeakoulu | Bachelor's thesis
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Date

2024-09-06

Department

Major/Subject

Laskennallinen tekniikka

Mcode

ENG3082

Degree programme

Aalto Bachelor’s Programme in Science and Technology

Language

en

Pages

43

Series

Abstract

Building energy consumption (BEC), which is responsible for more than 36% of global energy consumption and greenhouse gas emissions, poses a significant challenge to global environmental sustainability. Building energy consumption prediction (BECP) is critical for optimizing building design and improving building management to reduce energy usage and CO2 emissions. Although there are numerous studies on ML-based BECPs, and many literature reviews have emerged from these studies; a comprehensive evaluation of deep reinforcement learning (DRL) methods for BECP is missing in these literature reviews. To fill this gap, our state-of-the-art literature review provides a detailed discussion on the performance and potential of DRL methods applied to BECP, while updating the optimal ML model and effective features from the latest research on ML-based BECP from 2020 to 2024. This study presents a review of nine high-quality articles, each with an average annual citation rate of almost 50, covering 31 ML algorithms, with a focus on the analysis of applications, data properties, and ML methods in the selected articles. This study identifies the kCNN-LSTM model as the most effective ML method, achieving a root mean square error (RMSE) of only 0.0036 and an average computational time of merely 68.33 seconds, demonstrating an excellent balance between prediction accuracy and computational efficiency. This study also highlights the substantial potential of DRL to improve the accuracy of BECP and extend to other fields of prediction, despite its higher computational time compared to some other ML methods.

Description

Supervisor

St-Pierre, Luc

Thesis advisor

Gozaliasl, Ghassem

Keywords

building energy consumption prediction, black-box models for energy prediction, building energy prediction, energy efficiency, machine learning, data driven methods

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