The paper presents a framework for probabilistic assessment of likelihood of infection from airborne diseases in confined spaces which are continuously occupied for relatively long periods (e.g., school classrooms). The proposed approach is based on a combination of computational fluid dynamics (CFD) pre-calculations and analytical post-processing, to define relevant indices of infection probability. The practical applicability of the method is demonstrated through a case study, where different ventilation scenarios are considered for a school classroom. Corresponding infection probability indices are determined globally for the group of occupants. Furthermore, since the method does not rely on the well-mixing assumption, local probability indices are determined for each occupied location. The obtained results confirm the intuition that an increase of ventilation and/or air filtration reduces the overall likelihood of infection, though the observed positive effect is not uniform within the space. The presented methodology can also be considered as complementary to simpler approaches.

For achieving carbon neutrality, new buildings must be built according to very high-performance standards, such that their energy needs can be covered predominantly by onsite renewables. Although the technical solutions for efficient envelopes are well established, heat pumps together with PV have proven to be a reliable HVAC system, the discussion is ongoing regarding the optimal system concept and design for NZEB. Furthermore, the real performance strongly depends on the design, implementation, commissioning and operation. Given the complexity of building and HVAC with the influence of climate and users, a benchmark for optimal operation requires the combination of monitoring analysis and validated component and system models. Different steps and aspects of the calibration and optimization process are presented. The model, representing a step towards a digital twin, was used to improve the operation and derive guidelines for future optimal NZEB HVAC design, commissioning and operation for maximum primary energy savings. HIGHLIGHTS Integrated simulation-based design, commissioning and optimization of Net-zero energy residential buildings Calibrated component as well as building and HVAC system simulation models Simulation assisted commissioning and optimization leads to relevant energy savings NZE Multi-Apartment Buildings possible with optimized system design and control Monthly evaluation of primary energy highlights the need for reduced winter energy demand

The COVID-19 pandemic has underscored the need for effective ventilation control in public buildings. This study develops and evaluates a smart ventilation control algorithm (SIREN) that dynamically adjusts zone and system-level HVAC operation to maintain an acceptable COVID-19 infection risk and HVAC energy efficiency. SIREN uses real-time building operation data and Trim & Respond control logic to determine zone primary and system outdoor airflow rates. An EnergyPlus and CONTAM co-simulation framework was developed to assess its performance across various control scenarios and US climate zones. Results show that SIREN can flexibly control infection risk within a customized threshold (e.g. 3%) for every zone, while traditional controls cannot. At the building level, SIREN’s HVAC energy consumption is comparable to a fixed 70% outdoor airflow fraction scenario, while its infection risk is lower than the 100% outdoor airflow scenario, illustrating its potential for safe and energy-efficient HVAC operation during pandemics.

This paper investigates how a simulated room’s energy and temperature performance are affected if its underfloor heating control is modelled with increasing detail. Experiments were performed to develop and calibrate an empirical model of wax motor and to calibrate the valve curve. These models were used to implement and test the On/Off and proportional-integral (PI) control processes at various levels of modelling detail. Controllers were implemented by gradually adding optimized control parameters, signal delay, calibrated valve curve, signal modulation, and actuator modelling. The On/Off control dead band and PI parameters exhibited the largest impact, reducing energy use (1%–5%) and temperature fluctuations (ca 1 K). Modulating the PI output signal increased temperature fluctuations to the same amplitude as On/Off with 0.5 K dead band, increasing space heating demand by 1.3%. The wax actuator counted for less than 1%; however, it increased time delays to maximally 7 min and remarkably changed the mass flows.

In a chiller plant, primary or critical sensors are used to control the system operation while secondary sensors are installed to monitor the performance/health of individual equipment. Current sensor fault detection and diagnosis (SFDD) approaches are not applicable to secondary sensors which usually are not involved in the system control. Consequently, a hybrid multiple sensor fault detection, diagnosis and reconstruction (HMSFDDR) algorithm for chiller plants was developed. Machine learning and pattern recognition were used to predict the primary sensor faults through the comparison of the weekly performance curves. With the primary sensor signals reconstructed, the secondary sensor faults were estimated based on mass and energy balance. By applying the algorithm with various logged plant data and comparison with site checking results, a maximum of 75% effectiveness could be achieved. The merits of the present approach were further justified through off-site sensor testing which reinforced the usefulness of proposed HMSFDDR algorithm.

This paper presents a simulation framework for evaluating building control strategies (BCSs), developed with VOLTTRON, an opensource platform that integrates data, devices, and systems for sensing and control applications, and is based on co-simulation interfaces. It realizes an integrated environment for both testing and deploying BCSs, thereby eliminating the need for having a dedicated implementation of BCS for testing. For the first time, this framework provides critical functionalities, including scalable communication management and time-drive simulation advance, i.e., advancing the simulation based on clock time. We applied this framework to evaluating two BCSs from ASHRAE Guideline 36-2008 and ASHRAE Standard 90.1-2019. The evaluation results show that those BCSs primarily benefit the heating operation by reducing gas usage but yield insignificant savings in electricity consumption. The results also emphasize the importance of tuning the parameters of the BCSs to achieve better performances.

Despite natural light, near-façade structures’ impact on Natural Ventilation (NV) performance, reducing cooling demand, is usually ignored by designers. This article aims to characterize interactions between balconies’ design features (i.e. depth, unit level, and wind incident) and near-façade buildings on single-sided NV performance in a case study, representing medium to high-rise apartments in dominant cooling climates, utilizing validated Computational Fluid Dynamics (CFD) simulations. Results show that nearby buildings, even small structures close to the façade, significantly impact windward flow regime and indoor air movements. Nearby blocks might result in less air entering, which could be moderated by designing deeper balconies. Perpendicular wind incidents to the balcony opening may enormously improve single-sided NV performance. In contrast, wind incidents within higher degrees from perpendicular strongly reduce NV performance. This study provides designers with a method to characterize balcony design features’ effects on NV performance in apartments and improve it concerning urban design.

This paper presents a real-time implementation of model predictive control (MPC) for HVAC systems in an ice-cream factory building. The target building consists of two large open spaces served by two HVAC systems. We developed four artificial neural network (ANN) models that predict the thermal states of the supply air and indoor air of the two thermal zones and prove to be accurate enough (MBE = 2.65, CVRMSE = 9.43). The control variables employed in this study are the number of operating chillers, frequency of supply-air fan inverter and outdoor-air intake ratio. The objective function minimizes total energy use, and a constraint was set to maintain average indoor air temperatures close to set points. Real-time MPC was implemented at a sampling time of 20 min from 3 August to 30 August 2021 and could save approximately 31.7% of electricity when compared to the existing simple rule-based control.

The present study provides potentially interesting for researchers in the novel integrated method of the fuzzy set qualitative comparative analysis (fsQCA) and digital design methodology to apply in the field of energy and sustainability of socio-technical systems (STSs) used in the building energy management (BEM). The research sample is the air-conditioning system (ACS). The social and technical aspects of ACS are satisfaction and energy consumption. Ten sustainability criteria are applied to assess and monitor the fields of energy consumption and satisfaction with the aim of achieving sustainable consumption in the use of ACSs for BEM. This action can be carried out by nesting a circuit in a microprocessor and microcontroller for use in a building energy management system (BEMS). According to, the circuit obtained from the assessment of the study can be practically implemented and assembled in a BEMS as online. This work can define a sustainability-based BEMS.

Although it is widely acknowledged that reinforcement learning (RL) can be beneficial for building control, many RL-based control actions remain unexplainable in the daily practice of facility managers. This paper reports a rule reduction framework using explainable RL to enhance the practicality of the control strategy. First, deep Q-learning was applied to explore the optimal control strategies of a parallel cooling system (ice-based thermal system + geothermal heat pump system) of an existing office building. A set of modularized and interconnected data-driven models was developed using ANNs for pretraining an artificial agent. After exploring the control strategies, the decision-making rules of the agent were reduced using a decision tree. The performance of the reduced-order rule-based control proved comparable to the complex and uninterpretable control strategy of deep Q-learning. The difference in energy savings between the two is marginal at 1.2%.