• Energy Research

Smart charging systems can reduce the stress on the power grid from electric vehicles by coordinating the charging process. To meet user requirements, such systems need input on charging demand, i.e., departure time and desired state of charge. Deriving these parameters through predictions based on past mobility patterns allows the inference of realistic values that offer flexibility by charging vehicles until they are actually needed for departure. While previous studies have addressed the task of charging demand predictions, there is a lack of work investigating the heterogeneity of user behavior, which affects prediction performance. In this work we predict the duration and energy of residential charging sessions using a dataset with 59,520 real-world measurements from 267 electric vehicles. While replicating the results put forth in related work, we additionally find substantial differences in prediction performance between individual vehicles. An in-depth analysis shows that vehicles that on average start charging later in the day can be predicted better than others. Furthermore, we demonstrate how knowledge that a vehicles charges over night significantly increases prediction performance, reducing the mean absolute percentage error of plugged-in duration predictions from over 200 % to 15 %. Based on these insights, we propose that residential smart charging systems should focus on predictions of overnight charging to determine charging demand. These sessions are most relevant for smart charging as they offer most flexibility and need for coordinated charging and, as we show, they are also more predictable, increasing user acceptance. Applied Energy, 370 ISSN:0306-2619 ISSN:1872-9118

Abstract 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 1023 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 underscores 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.

In the light of global sustainability efforts, heat pumps offer environmental benefits, but their complexity and potential misconfigurations often lead to homeowner dissatisfaction due to inaccurate heating and lower-than-expected efficiency. Among the most important and complex settings is the heating curve and yet there are no easy-to-use methods to optimize it after its initial set-up. This study aims to develop ready-to-use guidelines for optimizing the heating curve with energy-efficient adjustments that improve room comfort and prevent suboptimal user changes, all without requiring additional sensors like room thermostats. Based on interpretable linear models, estimated on 3995 air-to-water heat pumps, located in Central Europe, we select the least energy-intensive heating curve shift for each outdoor temperature, needed to meet room thermal comfort. We find that the standard parallel shift of the heating curve is only the optimal approach when the average outdoor temperature is between 2 ⁰C and 5 ⁰C. Outside this range, the heating curve should be moved at its starting or the endpoint. Simulation shows that by translating user input to the room controller with our proposed changes, 84.42 % of the heating curves can be improved, reducing the share of misconfigured heating curves from 24.01 % to 7.08 %. This leads to an average reduction in yearly energy consumption of 4.02 % and an increase in the seasonal coefficient of performance by 2.59 % on average. By introducing ready-to-use heating curve improvement guidelines, we aim to increase efficiency and confidence in heat pump technology, ensuring its adoption to meet carbon emission targets. Applied Energy, 389 ISSN:0306-2619 ISSN:1872-9118

Data is provided to reconstruct the key figures (Figure 5, Figure 7, Figure 8 and Figure 9) of the paper “Optimization of heating curves for heat pumps in operation: outdoor temperature ranges for energy-efficient heating curve shifts”. Where Figures 7, 8 and 9 display simulated data of the study and were not directly measured. This data does not allow for a direct evaluation of the manufacturer or the heat pumps themselves, as the measurements and simulations were not designed as a controlled experiment. Potential measurement errors and uncertainties in the simulations have not been systematically validated.

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. Applied Energy, 350 ISSN:0306-2619 ISSN:1872-9118

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 R^2 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. BuildSys '23: Proceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation ISBN:979-8-4007-0230-3

Abstract Heat pumps are essential for decarbonizing residential heating but consume substantial electrical energy, impacting operational costs and grid demand. Many systems run inefficiently due to planning flaws, operational faults, or misconfigurations. While optimizing performance requires skilled professionals, labor shortages hinder large-scale interventions. However, digital tools and improved data availability create new service opportunities for energy efficiency, predictive maintenance, and demand-side management. To support research and practical solutions, we present an open-source dataset of electricity consumption from 1,408 households with heat pumps and smart electricity meters in the canton of Zurich, Switzerland, recorded at 15-minute and daily resolutions between 2018-11-03 and 2024-03-21. The dataset includes household metadata, weather data from 8 stations, and ground truth data from 410 field visit protocols collected by energy consultants during system optimizations. Additionally, the dataset includes a Python-based data loader to facilitate seamless data processing and exploration. Data Description Paper To use the dataset, please refer to the description provided in the current preprint. Note that this manuscript on arXiv is a preprint and is currently under peer review. The dataset and dataloader are available in their initial version, but future updates may occur. If you use the dataset in its current form, please cite the following arXiv paper: https://arxiv.org/abs/2503.16993 Code Availability A Python-based dataloader and data usage instructions can be found on GitHub: https://github.com/tbrumue/heapo