Data-driven optimisation of residential air-to-water heat pump performance using IoT and machine learning

Ayoola, Rasheed B. and Ilori, Olusegun M. and Perera, Noel and Mateo-Garcia, Monica and Akinyemi, Kabir and Boyd, David and Leonard, Mike (2025) Data-driven optimisation of residential air-to-water heat pump performance using IoT and machine learning. Energy and Buildings, 348. p. 116352. ISSN 0378-7788

Preview

Text 1-s2.0-S0378778825010825-main.pdf - Published Version Available under License Creative Commons Attribution. Download (19MB)

Abstract

Residential heating accounts for about 27 % of the UK’s energy consumption. While residential heat pumps (RHPs) are central to the transition toward sustainable energy, optimising their real-world performance requires robust experimental monitoring and predictive modelling. This study presents a data-driven approach for evaluating and optimising the performance of residential air-to-water heat pumps (A2WHPs) using real-time data and machine learning (ML). A full-scale experimental setup was deployed in a UK-based end-terrace building, incorporating IoT-enabled sensors to capture 275 days of operational data that was processed into a 6,600-hour dataset. Key thermal, electrical, and environmental parameters were measured at high temporal resolution and used to develop predictive models for the system’s coefficient of performance (COP). Several ML models, including Random Forest, Support Vector Regression (SVR), eXtreme Gradient Boosting (XGBoost), Artificial Neural Networks (ANN), and Long Short-Term Memory (LSTM), were evaluated using rigorous preprocessing, principal component analysis, and GridSearchCV hyperparameter tuning. LSTM, XGBoost, and ANN achieved the highest prediction accuracy with low error values across MAE, MSE, RMSE, CVRMSE, and NMBE. Diagnostic plots and residual analysis further confirmed the generalisability of the models and their sensitivity to non-linear operational behaviours. The findings demonstrate that integrating ML with real-world data can provide a robust predictive framework for operational diagnostics, performance evaluation, and efficiency improvement in residential heat pumps. This approach supports scalable, data-driven energy management and contributes to decarbonising the built environment.

Item Type:	Article
Identification Number:	10.1016/j.enbuild.2025.116352
Dates:	Date Event 21 August 2025 Accepted 24 August 2025 Published Online
Uncontrolled Keywords:	Air-to-water heat pump, Field data, Machine learning, Grid search, Hyperparameter tuning, Feature engineering
Subjects:	CAH10 - engineering and technology > CAH10-01 - engineering > CAH10-01-02 - mechanical engineering
Divisions:	Architecture, Built Environment, Computing and Engineering > Engineering
Depositing User:	Gemma Tonks
Date Deposited:	14 Oct 2025 13:54
Last Modified:	14 Oct 2025 13:54
URI:	https://www.open-access.bcu.ac.uk/id/eprint/16673

Actions (login required)

View Item