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District heating (DH) systems play a pivotal role in decarbonizing the building sector's heat supply. While innovative low-exergy DH and cooling systems are increasingly adopted in new developments, the transformation of existing DH systems remains critical, as many still depend on fossil-based heating plants. Achieving a sustainable heat supply necessitates integrating renewable energy and waste heat sources into current DH systems and enhancing operational efficiency through measures such as reduced supply temperatures and advanced control algorithms. These improvements can reduce costs and CO2 emissions but may require infrastructure adaptations, including pipe replacements and building-level system adjustments. This paper introduces a workflow for generating DH models using publicly available data and open-source tools. Such models enable comprehensive analyses of existing DH systems, allowing for the evaluation of sustainable heat integration, operational improvements, and the testing of analytical tools, such as simulation and optimization models. The workflow, detailed in this study, combines general structural data with computational estimations to create digital representations of DH systems. These models facilitate scenario-based analyses, tool benchmarking, and the identification of necessary infrastructure adaptations. Two example DH models generated using the proposed workflow are presented, followed by a discussion of the methodology's applicability and limitations. This study demonstrates how leveraging open data and tools can advance the transformation of DH systems, supporting the transition to a sustainable heat supply infrastructure.

Safety for physical human-robot interaction (pHRI) is a major concern for all application domains. While current standardization for industrial robot applications provide safety constraints that address the onset of pain in blunt impacts, these impact thresholds are difficult to use on edged or pointed impactors. The most severe injuries occur in constrained contact scenarios, where crushing is possible. Nevertheless, situations potentially resulting in constrained contact only occur in certain areas of a workspace and design or organisational approaches can be used to avoid them. What remains are risks to the human physical integrity caused by unconstrained accidental contacts, which are difficult to avoid while maintaining robot motion efficiency. Nevertheless, the probability and severity of injuries occurring with edged or pointed impacting objects in unconstrained collisions is hardly researched. In this paper, we propose an experimental setup and procedure using two pendulums modeling human hands and arms and robots to understand the injury potential of unconstrained collisions of human hands with edged objects. Pig feet are used as ex vivo surrogate samples - as these closely resemble the physiological characteristics of human hands - to create an initial injury database on the severity of injuries caused by unconstrained edged or pointed impacts. For the effective mass range of typical lightweight robots, the data obtained show low probabilities of injuries such as skin cuts or bone/tendon injuries in unconstrained collisions when the velocity is reduced to < 0.5 m/s. The proposed experimental setups and procedures should be complemented by sufficient human modeling and will eventually lead to a complete understanding of the biomechanical injury potential in pHRI., Comment: \c{opyright} 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Regarding climate change, the need to reduce greenhouse gas emissions is well-known. As building heating contributes to a high share of total energy consumption, which relies mainly on fossil energy sources, improving heating efficiency is promising to consider. Lowering supply temperatures of the heating systems in buildings offers a huge potential for efficiency improvements since different heat supply technologies, such as heat pumps or district heating, benefit from low supply temperatures. However, most estimations of possible temperature reductions in existing buildings are based on available measurement data on room level or detailed building information about the building's physics to develop simulation models. To reveal the potential of temperature reduction for several buildings and strive for a wide applicability, the presented method focuses on estimations for temperature reduction in existing buildings with limited input data. By evaluating historic heat demand data on the building level, outdoor temperatures and information about installed heaters, the minimal actual necessary supply temperature is calculated for each heater in the building using the LMTD approach. Based on the calculated required supply temperatures for each room at different outdoor temperatures, the overall necessary supply temperatures to be provided to the building are chosen. Thus, the minimal heatcurve possible for an existing building is deduced. The method described is applied to multiple existing office buildings at the campus of Forschungszentrum Juelich, Germany, demonstrating the fast application for several buildings with limited expenditure. Furthermore, a developed adapted heatcurve is implemented in one real building and evaluated in relation to the previously applied heatcurve of the heating system., Comment: 11 pages, 1 table, 10 figures

Decarbonizing the energy sector is one of the main challenges to combat the climate crisis. Cities play an important role to reach climate neutrality as more than 70% of global CO2 emissions originate from urban areas. Decarbonization of energy supply systems can be achieved through various means, including the use of renewable energy sources, improving the efficiency of technologies, the coupling of different energy sectors, and the use of flexibility considering individual prosumer behaviour. This leads to an increasingly decentralized energy system, which is challenging to operate in a robust and cost-effective way. The evaluation of technologies and subsystems can only be done from the perspective of the system in which it is embedded and it is highly dependent on their networking and application scenarios. Therefore, the design and operation of energy systems require adequate computation and evaluation tools, which offer a holistic view of all interconnected components. The currently available optimization tools have limitations, such as limited scope of technologies and sectors, high requirements on data, high computational cost, and difficulty in handling multi-objective optimization. To overcome these limitations a software framework called FOCUS for the flexible and dynamic modeling of any urban sector-coupled energy system is developed. The framework includes a library containing models for different technologies and offers a variety of parameter sets for each technology. FOCUS can handle multi-objective problems by returning Pareto-optimal fronts, which helps users to discover the trade-off between criteria and objectives. The developed tool can identify new flexibility potentials in the energy system, actively support companies in the respective field to optimize urban energy system planning solutions, and determine possible threads to the stable operation of such systems.

The development of current building energy system operation has benefited from: 1. Informational support from the optimal design through simulation or first-principles models; 2. System load and energy prediction through machine learning (ML). Through the literature review, we note that in current control strategies and optimization algorithms, most of them rely on receiving information from real-time feedback or using only predictive signals based on ML data fitting. They do not fully utilize dynamic building information. In other words, embedding dynamic prior knowledge from building system characteristics simultaneously for system control draws less attention. In this context, we propose an engineer-friendly control strategy framework. The framework is integrated with a feedforward loop that embedded a dynamic building environment with leading and lagging system information involved: The simulation combined with system characteristic information is imported to the ML predictive algorithms. ML generates step-ahead information by rolling-window feed-in of simulation output to minimize the errors of its forecasting predecessor in a loop and achieve an overall optimal. We tested it in a case for heating system control with typical control strategies, which shows our framework owns a further energy-saving potential of 15%., Comment: 6 pages, 7 figures. Accepted by PLEA2022, Presentation is avaiable at:https://github.com/chenxiachan/chenxiachan.github.io/blob/master/files/PLEA-Xia_1166_PG.pdf

We develop theoretical foundations and practical algorithms for vehicle routing with time-dependent travel times. We also provide new benchmark instances and experimental results. First, we study basic operations on piecewise linear arrival time functions. In particular, we devise a faster algorithm to compute the pointwise minimum of a set of piecewise linear functions and a monotonicity-preserving variant of the Imai-Iri algorithm to approximate an arrival time function with fewer breakpoints. Next, we show how to evaluate insertion and deletion operations in tours efficiently and update the underlying data structure faster than previously known when a tour changes. Evaluating a tour also requires a scheduling step which is non-trivial in the presence of time windows and time-dependent travel times. We show how to perform this in linear time. Based on these results, we develop a local search heuristic to solve real-world vehicle routing problems with various constraints efficiently and report experimental results on classical benchmarks. Since most of these do not have time-dependent travel times, we generate and publish new benchmark instances that are based on real-world data. This data also demonstrates the importance of considering time-dependent travel times in instances with tight time windows.

Time series forecasting is fundamental for various use cases in different domains such as energy systems and economics. Creating a forecasting model for a specific use case requires an iterative and complex design process. The typical design process includes the five sections (1) data pre-processing, (2) feature engineering, (3) hyperparameter optimization, (4) forecasting method selection, and (5) forecast ensembling, which are commonly organized in a pipeline structure. One promising approach to handle the ever-growing demand for time series forecasts is automating this design process. The present paper, thus, analyzes the existing literature on automated time series forecasting pipelines to investigate how to automate the design process of forecasting models. Thereby, we consider both Automated Machine Learning (AutoML) and automated statistical forecasting methods in a single forecasting pipeline. For this purpose, we firstly present and compare the proposed automation methods for each pipeline section. Secondly, we analyze the automation methods regarding their interaction, combination, and coverage of the five pipeline sections. For both, we discuss the literature, identify problems, give recommendations, and suggest future research. This review reveals that the majority of papers only cover two or three of the five pipeline sections. We conclude that future research has to holistically consider the automation of the forecasting pipeline to enable the large-scale application of time series forecasting.

Buildings directly and indirectly emit a large share of current CO2 emissions. There is a high potential for CO2 savings through modern control methods in building automation systems (BAS) like model predictive control (MPC). For a proper control, MPC needs mathematical models to predict the future behavior of the controlled system. For this purpose, digital twins of the building can be used. However, with current methods in existing buildings, a digital twin set up is usually labor-intensive. Especially connecting the different components of the technical system to an overall digital twin of the building is time-consuming. Piping and instrument diagrams (P&ID) can provide the needed information, but it is necessary to extract the information and provide it in a standardized format to process it further. In this work, we present an approach to recognize symbols and connections of P&ID from buildings in a completely automated way. There are various standards for graphical representation of symbols in P&ID of building energy systems. Therefore, we use different data sources and standards to generate a holistic training data set. We apply algorithms for symbol recognition, line recognition and derivation of connections to the data sets. Furthermore, the result is exported to a format that provides semantics of building energy systems. The symbol recognition, line recognition and connection recognition show good results with an average precision of 93.7%, which can be used in further processes like control generation, (distributed) model predictive control or fault detection. Nevertheless, the approach needs further research.

Existing open-source modeling frameworks dedicated to energy systems optimization typically utilize (mixed-integer) linear programming ((MI)LP) formulations, which lack modeling freedom for technical system design and operation. We present COMANDO, an open-source Python package for component-oriented modeling and optimization for nonlinear design and operation of integrated energy systems. COMANDO allows to assemble system models from component models including nonlinear, dynamic and discrete characteristics. Based on a single system model, different deterministic and stochastic problem formulations can be obtained by varying objective function and underlying data, and by applying automatic or manual reformulations. The flexible open-source implementation allows for the integration of customized routines required to solve challenging problems, e.g., initialization, problem decomposition, or sequential solution strategies. We demonstrate features of COMANDO via case studies, including automated linearization, dynamic optimization, stochastic programming, and the use of nonlinear artificial neural networks as surrogate models in a reduced-space formulation for deterministic global optimization., Comment: 24 pages, 1 graphical abstract, 13 figures, 4 tables