digital services for remote monitoring and optimization of energy efficiency of residential heat pumps
Heat pumps play a crucial role in the decarbonization of the building sector. However, many heat pumps consume a considerable amount of electricity because they operate below the energy efficiency specified by the manufacturer. The majority of installed systems are not connected to the internet, and even the latest models lack an interface for standardized services. This means that a high potential for energy and monetary optimization remains hidden and is lost, which leads to high operating costs for heat pump owners and puts a strain on the electricity grid due to increased total and peak electricity demand.
Therefore, as a PhD candidate in the Bits to Energy Lab at ETH Zurich, one area of focus for my work is analyzing data and developing machine learning algorithms to remotely monitor and optimize residential heat pumps in the field. In this context, I work with time series data from smart electricity meters (typically in 15-min resolution) and sensor data delivered by the heat pump directly (with typical resolutions in the range of a few seconds).
For my research, I currently collaborate with multiple partners across industry and the public sector: Swiss Federal Office of Energy, Bosch, EKZ, Enerlytica, and Hoval. Additionally, my algorithms are already being used in several digital energy consulting services to help Swiss households achieve higher levels of energy efficiency (e.g., PERLAS). For further information, please find a short interview in German language about the research project here and a short science slam video (also in German language) here, in which I present my work to a non-scientific audience.
Example: Heat maps showing patterns of electricity uptake of heat pumps identified as atypical and typical in terms of their on-off behavior.
Example: Energy efficiency of a population of heat pumps benchmark against each other in terms of coefficient of performance.
References
2024
Energy efficiency and behavior of heat pumps in residential buildings under real conditions
As heat pumps become more prevalent in residential buildings, effective performance monitoring is essential. Design flaws, incorrect settings, and faults can escalate energy consumption and costs, leading to discrepancies in user expectations and hindering the widespread adoption of this technology crucial for the heating transition. However, field studies using large data sets to offer insights into real-world performance and methods for identifying low-performing systems in practical, scalable applications are lacking. In the largest field study to date, we analyze sensor data from 1,023 heat pumps across Central Europe monitored over two years. Based on existing approaches for controlled laboratory conditions, we derive methods to evaluate and classify real-world performance using operational data. Applying these methods, we find that 17% of air-source and 2% of ground-source heat pumps do not meet existing efficiency standards. Additionally, around 10% of systems are oversized, while approximately 1% are undersized. This underscore the need for standardized post-installation performance evaluation procedures and digital tools to provide actionable feedback for users and installers to enhance operational efficiency and guide future installations.
@article{BRUDERMUELLER/HPEfficiency,title={Energy efficiency and behavior of heat pumps in residential buildings under real conditions},journal={Currently in revision},year={2024},author={Brudermueller, Tobias and Potthoff, Ugne and Fleisch, Elgar and Staake, Thorsten and Wortmann, Felix},keywords={Heat Pump, Energy Efficiency, Performance, Residential Building, Real-World Operation, Appliance Monitoring, Machine Learning, Sensor Data},}
2023
Large-scale monitoring of residential heat pump cycling using smart meter data
Heat pumps play an essential role in decarbonizing the building sector, but their electricity consumption can vary significantly across buildings. This variability is closely related to their cycling behavior (i.e., the frequency of on–off transitions), which is also an indicator for improper sizing and non-optimal settings and can affect a heat pump’s lifetime. Up to now it has been unclear which cycling behaviors are typical and atypical for heat pump operation in the field and importantly, there is a lack of methods to identify heat pumps that cycle atypically. Therefore, in this study we develop a method to monitor heat pumps with energy measurements delivered by common smart electricity meters, which also cover heat pumps without network connectivity. We show how smart meter data with 15-minute resolution can be used to extract key indicators about heat pump cycling and outline how atypical behavior can be detected after controlling for outdoor temperature. Our method is robust across different building characteristics and varying times of observation, does not require contextual information, and can be implemented with existing smart meter data, making it suitable for real-world applications. Analyzing 503 heat pumps in Swiss households over a period of 21 months, we further describe behavioral differences with respect to building and heat pump characteristics and study the relationship between heat pumps’ cycling behavior, energy efficiency, and appropriate sizing. Our results show that outliers in cycling behavior are more than twice as common for air-source heat pumps than for ground-source heat pumps.
@article{BRUDERMUELLER/2023121734,title={Large-scale monitoring of residential heat pump cycling using smart meter data},journal={Applied Energy},volume={350},pages={121734},year={2023},issn={0306-2619},doi={https://doi.org/10.1016/j.apenergy.2023.121734},author={Brudermueller, Tobias and Kreft, Markus and Fleisch, Elgar and Staake, Thorsten},keywords={Smart meter data, Heat pump, Cycling behavior, outlier detection, Residential buildings, Energy efficiency, Appliance monitoring, Real-world operation, Machine learning},}
Disaggregation of Heat Pump Load Profiles From Low-Resolution Smart Meter Data
As the number of heat pumps installed in residential buildings increases, their energy-efficient operation becomes increasingly important to reduce costs and ensure the stability of the power grid. The deployment of smart electricity meters results in large amounts of smart meter data that can be used for heat pump optimization. However, sub-metering infrastructure to monitor heat pumps’ energy consumption is costly and rarely available in practice. Non-intrusive load monitoring addresses this issue and disaggregates appliance-level consumption from aggregate measurements. However, previous studies use high-resolution data of active and reactive power and do not focus on heat pumps. In this context, our study is the first to disaggregate heat pump load profiles using commonly available smart meter data with energy measurements at 15-minute resolution. We use a sliding-window approach to train and test deep learning models on a real-world data set of 363 Swiss households with heat pumps observed over a period of 8 years. Evaluating our approach with a 5-fold cross-validation, our best model achieves a mean R2 score of 0.832 and an average RMSE of 0.169 kWh, which is similar to previous work that uses high-resolution measurements of active and reactive power. Our algorithms enable real-world applications to monitor the energy efficiency of heat pumps in operation and to estimate their flexibility for demand response programs.
@inproceedings{BRUDERMUELLER/10.1145/3600100.3623731,author={Brudermueller, Tobias and Breer, Fabian and Staake, Thorsten},title={Disaggregation of Heat Pump Load Profiles From Low-Resolution Smart Meter Data},year={2023},isbn={9798400702303},publisher={Association for Computing Machinery},address={New York, NY, USA},doi={10.1145/3600100.3623731},booktitle={Proceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation},pages={228–231},numpages={4},keywords={Non-Intrusive Load Monitoring, Low Resolution, Load Disaggregation, Smart Meter Data, Heat Pump Optimization, Energy Efficiency},location={Istanbul, Turkey},series={BuildSys '23},}
2022
Automatic Differentiation of Variable and Fixed Speed Heat Pumps With Smart Meter Data
With the increasing prevalence of heat pumps in private households, the need for optimization is growing. At the same time, the growing number of active smart electricity meters generates data that can be used for remote monitoring. In this paper, we focus on the automatic differentiation between fixed speed and variable speed heat pumps using smart meter data. This distinction is relevant because it is necessary for evaluating the state or cyclic behavior of a heat pump. In addition, identifying fixed speed heat pumps is important because they are known to be the less efficient systems and therefore may be preferred targets in energy efficiency or replacement campaigns. Our methods are applied to electricity data from 171 Swiss households with a resolution of 15 minutes. In this setting, a K-Nearest Neighbor model achieves a mean AUC of 0.976 compared to 0.5 of a biased random guess model.
@inproceedings{BRUDERMUELLER/9961055,author={Brudermueller, Tobias and Wirth, Florian and Weigert, Andreas and Staake, Thorsten},booktitle={2022 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)},title={Automatic Differentiation of Variable and Fixed Speed Heat Pumps With Smart Meter Data},year={2022},volume={},number={},pages={412-418},doi={10.1109/SmartGridComm52983.2022.9961055},}
