bluuebunny/crossref_metadata_2025_split huggingface dataset:
Performance measurements on WISC collectors under artificial environmental conditions
Authors: Pauletta S , Duret A , Dupont G , Jobard X 
Published: December 2023

Abstract
In the framework of the TriSolHP project, aimed at estimating the impact of improved heat pump technologies for decarbonization of non-retrofitted multifamily buildings in urban districts, a new type of PVT WISC (a photovoltaic-thermal wind and/or infrared sensitive collector) has been extensively tested in a climatic room without light input. The measurement campaign focused on the impact of airflow over the collector heat transfer coefficient for different installation layouts and when covered in ice. Measurement data were compared to TRNSYS simulations to gain an insight over the uncertainties affecting the performance models available in literature for this type of WISC.
Value stacking flexibility services in neighborhoods participating in fast frequency reserve markets
Authors: Stai Peter , Bjarghov Sigurd , Thorvaldsen Kasper E. , Backe Stian 
Published: December 2023

Abstract
Neighborhoods are responsible for considerable amounts of the total energy demand in Europe, and increased shares of variable renewable energy sources will require energy balancing services. Local flexibility resources in neighborhoods can help provide this. However, there is a lack of insight into the economic incentives and operational consequences for property owners to adopt prosumer qualities. Using a linear program that minimizes total electricity costs, this paper evaluates annual cost savings for a Norwegian university campus when value stacking the following flexibility services: responding to electricity spot prices, grid tariffs, and provision of fast frequency reserve (FFR). Several flexibility resources are addressed in this study, including a stationary battery, electric vehicle charging stations, and a vehicle-to-grid charging station. The results found an average 6.8% yearly cost decrease by FFR participation, supporting the notion that there is a significant economic potential in applying flexible resources from prosumers in fast frequency reserve markets, without significant conflicts with other flexibility services.
Using Machine Learning to Predict Window Opening Position in a Naturally Ventilated Building
Authors: Wong J. , Donges J. , Gasparella A. , Rysanek A. 
Published: December 2023

Abstract
Advancements in machine learning have faciliated its use in many domains. In this work we apply it to building sector, where mechanical ventilation systems are prevalent. While natural ventilation still can be suitable in many situations, the difficulty in estimating airflows and long computational simulation times prevents its adoption. Since ventilation rate depends heavily on window opening angle, we employ a computer vision techniques to estimate the states. We train a Fully-Connected Neural Network on images of European-style tilt-and-turn windows set at discrete positions, achieving over 95% average F1-Score. We highlight potential drawbacks with the method and identify steps forward on the path to real-world implementation.
Comparison of different coupling variants for building and HVAC simulation
Authors: Huang C , Eckstädt E 
Published: December 2023

Abstract
Coupled building and HVAC simulations applying the FMI standard promise many advantages in terms of system design and optimisation. In this paper, a systematic analysis of possible options for such couplings is presented. For one of these options, a case study is carried out where a quantitative comparison between the uncoupled reference system and the corresponding coupled system with different communication step sizes is carried out. Based on the simulation results, recommendations and challenges for such couplings are derived. In particular, the correlation between the communication step size of the FMUs and the computing time, as well as the deviations of the simulation results compared to the results of an uncoupled simulation are analysed.
Comparison of two deep reinforcement learning algorithms towards an optimal policy for smart building thermal control
Authors: Silvestri Alberto , Coraci Davide , Wu Duan , Borkowski Esther , Schlueter Arno 
Published: December 2023

Abstract
Heating, Ventilation, and Air Conditioning (HVAC) systems are the main providers of occupant comfort, and at the same time, they represent a significant source of energy consumption. Improving their efficiency is essential for reducing the environmental impact of buildings. However, traditional rule-based and model-based strategies are often inefficient in real-world applications due to the complex building thermal dynamics and the influence of heterogeneous disturbances, such as unpredictable occupant behavior. In order to address this issue, the performance of two state-of-the-art model-free Deep Reinforcement Learning (DRL) algorithms, Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC), has been compared when the percentage valve opening is managed in a thermally activated building system, modeled in a simulated environment from data collected in an existing office building in Switzerland. Results show that PPO reduced energy costs by 18% and decreased temperature violations by 33%, while SAC achieved a 14% reduction in energy costs and 64% fewer temperature violations compared to the onsite Rule-Based Controller (RBC).
Flexibility implications of optimal PV design: building vs. community scale
Authors: Li Q , Vulic N , Cai H , Heer P 
Published: December 2023

Abstract
The Swiss Energy Strategy 2050 aims for a significant increase in renewable energy generation, with photovoltaic (PV) energy having the largest share. Such development challenges the balance between electricity supply and demand, which can be partly mitigated through optimized PV integration together with energy storage systems. This study investigates the optimal placement of rooftop and façade PV for a small energy community considering investment and operational costs, embodied and operational emissions, and financial and environmental benefits of excess on-site electricity production, as well as batteries and different heating system options. Additionally, a methodology is developed to quantify energy flexibility needs at both the building and community scale. A case study neighborhood, comprising three single-family and two multi-family homes is optimized across different scenarios. Results show that when individual self-consumption is considered, installing an air-to-water heat pump system with a heating buffer tank allows for smaller PV systems with better grid interaction conditions. When collective self-consumption is considered, an overall reduction in PV system size is observed, contributing to reduced energy flexibility needs by effectively lowering peak feed-in power and improving the match between on-site PV production and demand.
The GENEAP project: digitalising and automating planning of district heating and cooling
Authors: Chambers Jonathan , Cozza Stefano , Patel Martin 
Published: December 2023

Abstract
The Swiss Energy Strategy 2050 implies a transformation of the provision of thermal energy services (heating and cooling) to become CO2 neutral, district heating and cooling (DHC) are an important solution, which is technically challenging to design and whose implementation entails certain risks for investment. The GENEAP (Geospatial Analysis and Planning) project developed a software that applies automation and digitalisation to address this barrier and to apply scientific research to support the work of practitioners. The software Thermal Energy System Simulation Assistant (TESSA) increases reproducibility and decreases errors, and thereby reducing overall costs. We introduced innovative algorithms for modelling heat and cooling demand, thermal energy supplies, and the cost of thermal network technologies under different retrofit and climate change scenarios. We developed graph theory-based algorithms for computationally efficient routing of thermal network pipes. The goal of the GENEAP project was to validate the models and to establish how practitioners would find value in such a tool. Through workshops and interviews we established the need for digital tools due to the rapidly increasing need for new projects, the complexity and long time frames of projects, and the limited number of available experts to do this work. We found a gap between the high level energy strategy (Plan Directeur Energetique) and the detailed engineering design stage. In between these two stages we find the need for a flexible approach that can integrate cities and societies needs into the engineering design with non-energy requirements (e.g. political context, urban constraints and opportunities, etc).
The more the better? Archetype segmentation in urban building energy modelling
Authors: Le Hong Z , Berzolla Z , Reinhart C 
Published: December 2023

Abstract
Urban building energy modelling is gaining traction as a planning tool to support widespread decarbonization of the built environment. Building-scale models allow for the evaluation of specific emission reduction policies at an urban scale. Given the limited availability of building-by-building data on construction standard and program, aggregating building information through archetypes is key, but a poorly understood step in the urban energy modelling process. In this study, different levels of archetype segmentation are explored for the city of Oshkosh, WI (∼13,000 buildings). A comparison of actual, city-level energy with UBEM simulations suggests higher levels of archetype segmentation do not necessarily lead to higher accuracy, leading to models that are both accurate and nimble enough to explore a variety of upgrade scenarios. Informing archetypal segmentation with policy-informed metrics is beneficial, but pursuing increased detail could dangerously reduce accuracy without ground-truth data.
Automatic detection of control loops in existing buildings
Authors: Stinner Florian , Gorgis David , Kümpel Alexander , Müller Dirk 
Published: December 2023

Abstract
Improving control loops in buildings is cost-intensive due to the need of expert time. With well structured data, the development of data-driven algorithms is promising to minimize the required time. But data stream names across buildings do not follow a uniform naming schema.
We developed a method to identify temperature control loops (sensors and set points) based on data stream names. The methodology starts with narrowing down to three potential sensors per set point using string distances and their combination, before estimating the probability that the first sensor is the correct paired sensor to the set point. We used data from three buildings: A large data set composed of over 12,000 translated data streams for the development and adjustment of the methodology and two smaller data sets for the verification of the transferability of the developed method.
The results are strongly dependent on data stream name structure. The results indicate that within the test data set, the pairing sensor to a set point can be automatically limited to three or five sensor candidates, and the algorithm can identify a high number of control loops. For the transfer to the two validation sets, the limitation of sensor candidates is still possible, but identifying pairings and control loops is difficult.
Planning the Design and Operation of Urban Energy Systems with Limited Data Availability: A Holistic Open-Source Tool Chain
Authors: Schreiber T , Beckhölter T , Derzsi K , Droste K , Karuvingal R , Nie Y , Wackerbauer D , Wirtz M , Welter S , Zhang Y , Müller D 
Published: December 2023

Abstract
Urban energy systems are becoming more complex due to the integration of renewable energy sources and electrification of heat supply. Dynamic planning and operation strategies are necessary to optimally satisfy energy demands, especially in the 5th generation of heating and cooling networks, which efficiently address these challenges through decentralized water-water heat pumps. An end-to-end tool chain is presented which enables to analyze and evaluate such systems in early planning phases with limited data availability. It includes thermal building simulations, heating network simulations, optimization, and incorporation capabilities for model-based operation algorithms and AI-based forecasting algorithms. User acceptance of the energy system can also be considered in the design phase in future. This paper presents the interaction of the open-source tools: TEASER, which generates high-resolution time-series of heating and cooling demands of buildings; uesgraphs, which generates and simulates thermal networks from geodata; and EHDO, which optimizes energy hub designs. Future research pathways include integrating AI-based operation optimization and surveys to support user feedback integration. Work will also address substation models for the 5th generation heating networks and novel data infrastructures. The tool chain has already been successfully used in many scenarios, but open questions remain around integrating the methods into the operation phase and further developing the complex energy models the tool chain is built upon.
Review of natural language processing techniques for characterizing positive energy districts
Authors: Han Mengjie , Shah Juveria , Zhang Xingxing 
Published: December 2023

Abstract
The concept of Positive Energy Districts (PEDs) has emerged as a crucial aspect of endeavours aimed at accelerating the transition to zero carbon emissions and climate-neutral living spaces. The focus of research has shifted from energy-efficient individual buildings to entire districts, where the objective is to achieve a positive energy balance over a specific timeframe. The consensus on the conceptualization of a PED has been evolving and a standardized checklist for identifying and evaluating its constituent elements needs to be addressed. This study aims to develop a methodology for characterizing PEDs by leveraging natural language processing (NLP) techniques to model, extract, and map these elements. Furthermore, a review of state-of-the-art research papers is conducted to ascertain their contribution to assessing the effectiveness of NLP models. The findings indicate that NLP holds significant potential in modelling the majority of the identified elements across various domains. To establish a systematic framework for AI modelling, it is crucial to adopt approaches that integrate established and innovative techniques for PED characterization. Such an approach would enable a comprehensive and effective implementation of NLP within the context of PEDs, facilitating the creation of sustainable and resilient urban environments.
Heat vulnerability digital mapping at neighbourhood level in the compact city
Authors: Morganti M , Lopez-Ordoñez C , Ciardiello A 
Published: December 2023

Abstract
This paper analyses the impact of urban form and vegetation on one of the most significant parameters that affect people’s thermal comfort and an indicator of urban heat vulnerability: the mean radiant temperature (MRT). To obtain spatialized results and understand in detail the current thermal situation of different public spaces that are part of the city, we combined the SOLWEIG calculation model included in the UMEP tool for QGIS with Urban Weather Generator for Rhino. Six neighbourhoods of Rome (IT) and the associated areas with typical compact urban forms, ranging from historical centre to modern suburbs, have been analysed in the warmest week of the year (August 03-09) during the most critical hours of the day (10 a.m. - 4 p.m.). Georeferenced maps with the mean values of MRT for the studied period allow us to analyse the thermal behaviour of each public square and neighbourhood and locate possible urban havens during heatwaves. This study is part of a larger work that seeks to define a more accurate approach to quantify heat vulnerability within the urban vulnerability indexes, in light of the climate crisis facing cities.
Assessing outdoor comfort near graduation towers: An experimental calibration study in a public open space
Authors: Görgen F , Rossi-Schwarzenbeck M 
Published: December 2023

Abstract
This study focuses on the experimental calibration of a numerical model to quantify the cooling effects of graduation towers under heat wave conditions. Given the increasing number of hot days, tropical nights and ongoing urbanization, simulation tools for predicting and enhancing outdoor comfort have become increasingly important. To investigate the influence of the water mist emitted by the graduation towers, a 3D model was constructed using the microclimate simulation tool ENVI-met and calibrated against in-situ measurements. By means of evaporation, both air temperature and PET (Physiological Equivalent Temperature) could be reduced by up to 14.8K and 9.5K, respectively, resulting in no thermal stress in the vicinity of the graduation towers. The calibrated model can assist municipalities in assessing the cooling performance of graduation towers, enabling them to adjust water usage according to weather conditions and enhance outdoor comfort for the well-being of the population in recreational areas.
Experimental study to understand the thermal environment of an office cooled by radiant ceiling panels and dedicated outdoor air system
Authors: Chen K W , Izuhara I , Merchant C , Meggers F , Pantelic J 
Published: December 2023

Abstract
This research aims to better understand the radiant environment of an interior space located in the tropical climate that is cooled by Radiant Ceiling Panels (RCPs). To do so, we have designed a highly instrumented 177m2 office testbed in Singapore installed with RCPs, Dedicated Outdoor Air System (DOAS) and Fan Coil Units (FCUs). An 11-day experiment is designed to monitor the radiant environment when being cooled by 1) RCPs + DOAS and 2) FCUs + DOAS with a fixed internal load of 2kW and humidity load of 8g/hr. We alternate between these two cooling modes, with variations in panels’ water supply temperature and window blinds. We expect to see a separation of Mean Radiant Temperature (MRT) and air temperature in mode 1 with the use of improved radiant sensors. However, results showed only minor differences in the radiant environment between the two modes of cooling. There was no significant MRT and air temperature separation (<1°C). These insights provide a better understanding of our radiant environment, which can potentially improve the way we control radiant systems. With better understanding, we can support high thermal comfort performance of a radiant systems as compared to a more common air-based system.
Personal comfort models in long-term monitoring using physiological data from wearable sensors
Authors: Martins Gnecco V , Pigliautile I , Pisello A L 
Published: December 2023

Abstract
Personal Comfort Models (PCMs) propose a new approach for human-centric comfort studies overcoming the one-size-fits-all of the conventional models. This research addresses the development of PCMs based on a seven-month long-term monitoring campaign including continuous environmental and physiological data collection through wearables and daily survey submission about subjects’ sensations. To tackle the influence of subjects’ environmental exposure history, time series of environmental data of different durations were used to predict individuals’ perception via Machine Learning models with Support Vector Machine and Random Forest methods. The accuracy and F1-score values of seven different PCMs were confronted for each subject and for the whole group (nine people). The number of datapoints per subject and their answers’ consistency during time affected the models’ accuracy, and the inclusion of physiological signals improved the models’ performance. When considering the whole dataset, the comfort model accuracy decreases supporting that individual subjectivity have an important impact in the environmental perception prediction.
Exploratory analysis of the operation of a MHRV system in an overheating nZEB apartment in Spain
Authors: Otaegi Jorge , Martín-Garín Alexander , Hernández-Minguillón Rufino J. , Rodríguez-Vidal Iñigo 
Published: December 2023

Abstract
This article investigates the operation of a Mechanical Heat Recovery Ventilation (MHRV) system in an overheating nZEB apartment in Spain. The building, an apartment tower, was found to have a moderate to severe issue of overheating during extensive parts of the year. The study focuses on a flat located on the top floor of the Bolueta tower I in Bilbao, Spain, which is the world’s tallest Passivhaus building. Temperature and relative humidity data loggers were placed in various locations inside and outside the flat, as well as an amperemeter to monitor the operating regime of the MHRV unit’s fans. The study covered a period of 18 months and consisted in an exploratory analysis of the data obtained. The results show that the dwelling and its ventilation system partially correspond to the expected performance during the cold season but deviate from the usual design assumptions considered for the warm periods. The MHRV unit may have difficulties entering bypass mode and providing sufficient free cooling to maintain adequate indoor temperatures due to its placement. These findings provide insight into the thermal performance of the building and operation of the MHRV system and can inform future research.
Indoor environmental quality trade-offs due to summertime natural ventilation in London care homes
Authors: Tsoulou I , Jain N , Oikonomou E , Petrou G , Mavrogianni A , Gupta R , Howard A , Milojevic A , Davies M 
Published: December 2023

Abstract
We evaluate current and future summertime temperature and indoor air quality (IAQ) in two London care homes, occupied by seniors. We further examine the effect of natural ventilation, aiming to identify strategies that can maintain temperature, CO2 and key pollutants (PM2.5, NO2) within acceptable ranges. Data come from simulations in DesignBuilder. Results show a higher risk of overheating in the newer care home, with 85% of hourly outputs exceeding 26°C. In addition, bedrooms are much warmer than lounges in both homes, with averages expected to reach 32-35°C by 2050. In terms of IAQ, 65% of PM2.5 and NO2 hourly outputs are within range; however, without any ventilation, the high CO2 levels are expected to rise by 70-130ppm in 2050, especially in bedrooms of the newer home. Results further indicate that natural ventilation can substantially reduce temperature and CO2, but at the same time it may increase PM2.5 and NO2 coming from outdoors. Yet, these trade-offs can be reduced through a careful ventilation strategy that considers building-specific characteristics, as well as time of day and duration. Findings suggest a need to focus on the interdependencies among indoor environmental quality outputs and highlight the value of inexpensive and sustainable passive ventilation.