Transforming Building Energy Management - Sparse, Interpretable, and Transparent Hybrid Machine Learning for Probabilistic Classification and Predictive Energy Modelling

Aug 5, 2026

The building sector, responsible for 40% of global energy consumption, faces rising sustainability and efficiency demands, making reliable energy forecasting essential for performance optimization and reduced environmental impact. This study proposes a hybrid Sparse, Interpretable, and Transparent (SIT) machine learning framework for building energy management using the REFIT Smart Home Dataset. It analyzes occupancy patterns, predicts appliance-level energy, performs probabilistic uncertainty quantification, and identifies three household profiles via K-means and Gaussian Mixture Models. A Random Forest classifier highlights key appliances, while uncertainty and time-series decomposition improve interpretability. Comparative results show superior predictive accuracy and transparency versus SVM and XGBoost, supporting adaptive energy management.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages24

Reading Time1 h 0 min

AccessFree to download

Introduction

Background

Methodology

Hybrid SIT framework and dataset

Occupancy clustering and household profiling

Appliance importance and probabilistic uncertainty analysis

Experimental Evaluation

Predictive accuracy and transparency comparisons

Interpretability through decomposition and context

Article Transforming Building Energy Management: Sparse, Interpretable, and Transparent Hybrid Machine Learning for Probabilistic Classification and Predictive Energy Modelling Yiping Meng 1,*, Yiming Sun 2, Sergio Rodriguez 1 and Binxia Xue 3,4, * Academic Editors: Narjes Abbasabadi and Mehdi Ashayeri Received: 2 March 2025 Revised: 19 March 2025 Accepted: 30 March 2025 Published: 31 March 2025 Citation: Meng, Y.; Sun, Y.; Rodriguez, S.; Xue, B. Transforming Building Energy Management: Sparse, Interpretable, and Transparent Hybrid Machine Learning for Probabilistic Classification and Predictive Energy Modelling. Architecture 2025, 5, 24 . [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)architecture5020024 Copyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)) . 1 School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, Tees Valley TS1 3BX, UK; [s.rodriguez@tees.ac.uk](s.rodriguez@tees.ac.uk) 2 School of Electrical and Electronic Engineering, University of Sheffield, Western Bank, Sheffield S10 2TN, UK; [yiming.sun@sheffield.ac.uk](yiming.sun@sheffield.ac.uk) 3 School of Architecture and Design, Harbin Institute of Technology, Harbin 150001, China 4 Key Laboratory of Cold Region Urban and Rural Human Settlement Environment Science and Technology, Ministry of Industry and Information Technology, Harbin Institute of Technology, Harbin 150001, China * [Correspondence: y.meng@tees.ac.uk](Correspondence: y.meng@tees.ac.uk) (Y.M.); [binxia68@126.com](binxia68@126.com) (B.X.) Abstract: The building sector, responsible for 40% of global energy consumption, faces increasing demands for sustainability and energy efficiency. Accurate energy consumption forecasting is essential to optimise performance and reduce environmental impact. This study introduces a hybrid machine learning framework grounded in Sparse, Interpretable, and Transparent (SIT) modelling to enhance building energy management. Leveraging the REFIT Smart Home Dataset, the framework integrates occupancy pattern analysis, appliance-level energy prediction, and probabilistic uncertainty quantification. The framework clusters occupancy-driven energy usage patterns using K-means and Gaussian Mixture Models, identifying three distinct household profiles: high-energy frequent occupancy, moderate-energy variable occupancy, and low-energy irregular occupancy. A Random Forest classifier is employed to pinpoint key appliances influencing occupancy, with a drop-in accuracy analysis verifying their predictive power. Uncertainty analysis quantifies classification confidence, revealing ambiguous periods linked to irregular appliance usage patterns. Additionally, time-series decomposition and appliance-level predictions are contextualised with seasonal and occupancy dynamics, enhancing interpretability. Comparative evaluations demonstrate the framework’s superior predictive accuracy and transparency over traditional single machine learning models, including Support Vector Machines (SVM) and XGBoost in Matlab 2024b and Python 3.10 . By capturing occupancydriven energy behaviours and accounting for inherent uncertainties, this research provides actionable insights for adaptive energy management. The proposed SIT hybrid model can contribute to sustainable and resilient smart energy systems, paving the way for efficient building energy management strategies. Keywords: hybrid machine learning; sparse interpretable transparent (SIT) model; energy prediction; uncertainty quantification; sustainable energy management 1. Introduction 1.1. Background The building sector is at the forefront of the global challenge to

FAQs

What is the proposed hybrid SIT machine learning framework for building energy management?

It is a Sparse, Interpretable, and Transparent (SIT) hybrid model that combines occupancy pattern analysis, appliance-level energy prediction, and probabilistic uncertainty quantification to support energy management decisions.

How are household occupancy-driven energy usage patterns identified?

The framework clusters occupancy-driven energy usage using K-means and Gaussian Mixture Models, resulting in three distinct household profiles: high-energy frequent occupancy, moderate-energy variable occupancy, and low-energy irregular occupancy.

How does the study ensure interpretability and handle uncertainty?

A Random Forest classifier pinpoints key appliances affecting occupancy, while uncertainty analysis quantifies classification confidence and links ambiguous periods to irregular appliance usage patterns; time-series decomposition further contextualizes predictions with seasonal and occupancy dynamics.

Aug 5, 2026

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