• Energy Research

Applied energy 388, 125530 (2025). doi:10.1016/j.apenergy.2025.125530 Published by Elsevier Science, Amsterdam [u.a.]

As part of its contribution to IEA Annex 60, the Institute for Energy Efficient Buildings and Indoor Climate of RWTH Aachen University will make its Modelica HouseModels library available. The scope of this paper is to provide information about the library. The first part presents the library and its functionality. In the second part a room model is evaluated by using Case 600 from the test suite provided by the American Society of Heating, Refrigerating and AirConditioning Engineers (ASHRAE) in the standard 140.

Abstract Ground-source heat pumps (GSHPs) coupled with borehole heat exchangers (BHEs) are energy-efficient technologies for heating and cooling buildings. However, these systems often fail to operate at their full potential due to discrepancies between the assumptions made during the design phase and the actual conditions during operation. To enhance overall GSHP performance, it is crucial to collect and analyze long-term monitoring data from operating BHE fields. To our knowledge, no long-term, high-resolution dataset of double U-tube BHEs is currently publicly available. Additionally, most studies typically monitor only the inlet and outlet of the entire ground heat exchanger rather than individual BHEs, hindering detailed performance analysis. With this data descriptor, we present a 6-year dataset from a BHE field comprising 40 BHEs, each with sensors for volume flow and inlet/outlet temperatures, recorded every 30 seconds. We believe this dataset will enhance understanding of individual BHE performance, provide validation for BHE models, and thus support better GSHP design and operation.

Abstract In this paper, a free running centrifugal fan inside a rectangular duct is investigated via measurements and CFD. A rectangular cuboid-shaped body is mounted downstream of the free running centrifugal fan, covering most of the space next to the fan wheel, leaving only a small channel for the air near the duct walls to pass through. We call this cube a ‘pressure regain unit’ (PRU). The cube increases the fan efficiency by reducing vortices downstream of the fan and tranfering a larger part of the kinetic energy into static pressure. Experiments are conducted with several PRU geometry variations. The measurements show that an increase in efficiency of 10% is possible compared to a free running fan in an empty duct. Subsequently, a numerical analysis is performed to analyze the effect which leads to efficiency increase. Comparing the installation with and without PRU shows that the main difference is a recirculation area behind the fan wheel when no PRU is installed. Higher level of turbulence and strong shear layers in the empty duct cause efficiency losses. The turbulence level and the amount of shear layers are reduced by the PRU. Both experimental and numerical data show an energy saving potential by optimizing the downstream region of the free running centrifugal fan.

Abstract In this study, four approaches to model stratification in thermal energy storage (TES) units with mixed-integer linear programs are introduced. These stratification models are compared with the widely utilized capacity model, in which TES units are modeled as homogeneous volumes. The approaches are verified with a use case consisting of a single building with a monovalent heating system comprising a combined heat and power (CHP) unit and a TES unit. The objective is the minimization of the total operational costs. The results conclude that both models, capacity and stratification models, generate electricity driven schedules. In the capacity model, the minimum energy content is typically set to a constant value, mostly zero, while the layered storage model allows for implementing more accurate restrictions, such as the required flow temperature based on the building's heating curve. Consequently, the capacity model overrates the system's efficiency, thus underestimating the operating costs by 6–7%.

Abstract The efficient and sustainable operation of building energy systems is playing an increasingly important role in most industrialized countries. At the same time, building energy systems are becoming increasingly complex; fault-free and optimal operation, under dynamic boundary conditions, is becoming more and more challenging. There are many approaches in research to address the optimal control problem of building energy systems, such as Rule-based Control, Model Predictive Control, or Adaptive Control. However, most methods rely on models of the system dynamics with high prediction accuracies. This is especially the case in Model Predictive Control, where the model is part of a continuously executed optimization problem; but models are also required when it comes to the optimal design of Rule-based Controllers, the safe pre-training of Adaptive Controllers, or model-based fault detection. A limiting factor for the manual development of physical models, for building energy systems, are the low monetary incentives for engineering services, due to the low energy prices in most countries. In addition, the creation of such models is time-consuming and error-prone, even for domain experts. Another weakness is that changes in the system dynamics are not automatically adapted within the models. These challenges are contrasted by an increasing availability of monitoring-data and computational power in recent years; with machine-learning algorithms, these resources are used in numerous application areas to achieve very promising results. Machine-learning methods can help to obtain data-driven, self-calibrating models, which can be learned from monitoring-data. In this paper, we apply methods for automated data-driven model generation. We demonstrate how machine-learning algorithms together with structured hyper-parameter tuning can be used to model individual subsystems as well as a complete energy supply system. To represent the dynamics of the supply system, it is first decomposed into simple functional relationships, which are aggregated into the overall system after training of the comparatively simple subsystem models. We evaluate the accuracy of the data-driven subsystem models using established metrics for the evaluation of regression models, namely the R2-score and the RMSE. The considered system is integrated into a district cooling network and consists of two compression chillers and an ice storage unit. Our investigations show that the dynamics of the subsystems can be learned with high accuracies, depending on the operation mode and the selected features. The prediction of the power demand of the compression chillers is learned with R2-scores between 0.94 and 0.99 and RMSE values between 2.02 kW and 3.51 kW. Also, the prediction of the percentage of ice formation within the ice storage is learned accurately with a R2-score of 1 and RMSE values between 0.08 % and 0.72 %. The dynamics of the aggregated system also show plausible behavior and can thus be used in future work. This work is part of an ongoing research project with the aim to optimize the operation of the entire campus cooling energy supply system. Our results show that, if detailed monitoring-data are available, data-driven modelling represents a viable alternative to the labor-intensive physical modelling approach. Furthermore, we emphasize the importance of structured hyper-parameter tuning, discuss the specifics of different machine-learning algorithms, and elaborate on possible future developments in this research area.

Abstract The increasing installation of volatile renewable energy sources like photovoltaics and wind enforces the need for flexibility options to match the renewable generation with the demand. One of these options is Demand Side Management (DSM) in the context of building energy systems combined with thermal storage systems. This paper discusses such concepts for DSM. A method for analyzing the flexibility that is needed to maintain the stability of the electrical grid is presented followed by the restrictions that are caused by meeting the heat demand and satisfying the comfort criteria of the residents. Approaches for simultaneously fulfilling these constraints as well as matching the flexibility needs of the electrical grid and the flexibility provided by the local building energy systems are discussed. To enhance the analysis options for the shown systems, a simulation platform that covers the electrical grid simulation, the building systems’ simulation and the control strategies is presented. This platform can be used to analyze different scenarios of building energy systems with different penetrations of renewable energy sources and different building types.

Abstract Technological improvements of HVAC-systems, buildings and other energy consuming products can support a reduction of CO2 emissions. The implementation of energy efficient technology is important to reach the climatic goals of the Paris agreement. As cities are the main contributors of greenhouse gas emissions, they offer great potential for implementation of energy efficiency measures and emission reduction. Thus, the question arises on how to support the implementation process. The main challenge is not only the implementation and usage of technologies, but also the optimisation of existing local instruments, processes and frameworks to efficiently support the implementation of energy strategies in communities. This paper summarises results of the Annex 63 − Implementation of Energy Strategies in Communities − within the Energy in Buildings and Communities Program (EBC) of the International Energy Agency (IEA). It includes procedures and best-practice examples to implement optimized energy strategies in communities. The implementation strategies deals with visions and targets, renewable energy strategies, legal frameworks, design of urban competition processes, tools supporting the decision making process, monitoring, stakeholder engagement, socio-economic criteria and organisation structures. For all of these strategic measures, specific guidelines were elaborated together with a worldwide network consisting of representatives from cities, urban and energy planners, consultants, universities and many other stakeholder groups.