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In a traditional building design process (TDP), design variables are fixed sequentially, as opposed to integrated design process (IDP) which tends to avoid sequential design phases to create more sustainable buildings. First, a reference building is introduced and an energy model based on TRNSYS is presented to determine the energy consumption and comfort in the building. The model is validated based on energy bills, certified simulations and literature. Then, the paper performs an extended sensitivity analysis (SA) of 30 design variables with respect to different performance criteria related to energy consumption and comfort, based on a TRNSYS model. Three SA techniques were used, namely standard regression coefficients (SRC), partial rank correlation coefficients (PRCC) and Sobol indices. Results show that all three techniques yielded a similar ranking of the importance of the variables for most model outputs. Interactions between variables were identified with second-order Sobol indices. In the second part of this paper, a traditional design framework was adopted in which sets of variables were fixed sequentially. A SA was performed at each phase of the process, assuming fixed values for parameters chosen in previous design phases. Results show that fixing variables during the phases of a traditional design process tends to reduce the probabilities of finding low-energy consumption designs. Moreover, the influence of some variables was found to change during the design phases. [ABSTRACT FROM AUTHOR]

The optimization of energy consumption in buildings' HVAC systems plays a crucial role in reducing greenhouse gas emissions worldwide. In new and deeply-renovated buildings, characterized by modulating terminal units and aiming at the maximum exploitation of renewables, an accurate hydraulic balance of the distribution network may become critical, resulting in a significant increase of pumping energy consumptions and a deterioration of indoor thermal comfort conditions. In the present paper, we compare the performance of traditional manual balancing valves and new pressure independent control valves (PICVs), used to balance the hydronic loop of HVAC systems. To perform this analysis, a new MATLAB-Simulink model has been specifically developed to simulate the behavior of PICVs and has been used in an application case study. The efficiency of manual and pressure independent control valves is evaluated numerically by simulating a multi-zone distribution network under variable operating conditions and considering different control strategies of the circulating pump. Results show that in off-design conditions, when some of the branches of the hydraulic network are closed, traditional manual balancing valves are not able to guarantee the nominal mass flow rate in the remaining loops. On the contrary, no significant variations of water mass flow rate are observed when PICVs are adopted, even in partial load conditions. Despite their additional cost, PICVs allow to ensure better indoor thermal comfort sensations for individuals, avoiding under/over-heating of rooms and, moreover, yield a decrease of the pump electric consumption with respect to traditional manual balancing valves. In addition, based on the obtained results, general rules concerning the adoption of PICVs are provided, depending on the extension of the hydraulic loop and the adopted pump control logic. The reported results highlight the role that PICVs can play to ensure energy savings in HVAC systems without penalizing users' indoor comfort conditions. [ABSTRACT FROM AUTHOR]

• Provided a platform to test various residential building control systems. • Used probability of occupancy to determine setback temperature when unoccupied. • Integrated optimizer with adaptive and occupancy control. • Generated dynamic electricity tariff based on wholesale electricity pricing. • Evaluated the peak load shaving potential with dynamic pricing and optimization. Energy consumption in buildings continues to rise with increased deployment of energy-consuming equipment such as Heating, Ventilation, and Air Conditioning (HVAC) amid a growing world economy. Renewable energy is projected to comprise a majority of the future electricity supply, but the intermittent nature of renewables means that consumption must respond to dynamic supply for optimal utilization. This paper proposes a novel HVAC control strategy for residential buildings using the adaptive comfort model, considering occupancy through probability and real-time information, and optimizing the HVAC schedule to reduce cost, maintain thermal comfort, and respond to the dynamic availability of renewable energy while being generalizable to different situations. To validate this approach, the Universal CPS Environment for Federation (UCEF) co-simulation platform is used to connect advanced building controls with the building energy simulation software EnergyPlus. Simulations are performed for a residential building in Sacramento, CA during a typical summer week. Economic impacts, energy consumption, and thermal comfort are analyzed for traditional, adaptive, and occupancy-based control strategies under demand-based, tiered, and fixed electric tariff systems. Simulation results show that occupancy consideration, adaptive thermal comfort, and optimization can reduce cost by 50.1 %, electricity consumption by 52.9 %, and discomfort by 56.2 % compared to traditional fixed setpoints. The ability of the proposed HVAC control strategy to shift energy consumption away from peak times under a demand-based tariff system is qualitatively analyzed and findings suggest that maximum load-shifting on a grid-scale is attained using occupancy consideration with optimized control and demand-based pricing. For individual residential buildings, similar economic benefits can be gained using the less-complex adaptive HVAC control strategy with existing tiered or simple electric tariff systems. [ABSTRACT FROM AUTHOR]

Because the building sector has become a huge energy consumer, providing useful approaches can help tackle this important issue. Windows are an important component of a building since most of the natural light inside the building is provided by passing this component. Improper design of windows and their associated components, such as shadings, overheats the space and increases energy consumption, and on the other hand, disturbs the visual and thermal comfort of the building's occupants. The present paper aims to present a new approach for multi-objective optimization of the architectural specifications and control parameters of a smart shadow curtain. The use of this proposed approach leads to a significant reduction in building energy consumption and improved thermal and visual comfort. Energy-related simulations are performed through EnergyPlus software. Afterward, 21 design variables including control strategies and setpoints, location and orientation of shading, slat dimensions and material specifications, as well as hourly slat angle are considered in JEPLUS software. These simulations are conducted in four geographical directions over seasonal and annual periods for the city of Tehran, Iran. JEPLUS + EA software is employed to perform sensitivity analysis on the outputs of JEPLUS software and data optimization. Morris sensitivity analysis is implemented to investigate the effects of input variables on the objective functions. The NSGA-II algorithm is then applied to optimize the data to extract the optimal points on a Pareto front. The optimization results indicate that the external blind performs better than the internal blind and also the smaller the angle of the blind slat is, the better the visual comfort is but increases the electricity consumption for lighting. A comparison of the results of multi-objective optimization of controlled blind characteristics with initial values demonstrates that based on geographical directions, the total energy consumption of the entire building decreases by a range of 40–50 % per year, while visual comfort and thermal comfort are improved by ranges of 70–100 % and 10–40 %, respectively. [ABSTRACT FROM AUTHOR]

Current indoor thermal environment is usually automatically controlled based on a temperature set-point given by room occupants or building managers. However, investigation shows that many temperature set-points are far from the comfortable temperature range recommended by design handbooks. Such unreasonable temperature set-points will result in not only uncomfortable indoor thermal environment, but also waste of cooling or heating energy. The authors have proposed a methodology of satisfaction based control to take place of the traditional set-point based control. This paper describes the experimental comparison results between satisfaction based indoor thermal environment control and set-point based control. Two test-beds were set up and a series of experiments was conducted to compare control performances, user acceptances, user work performances and system energy consumptions of the set-point based and satisfaction based control. The comparison results show that the satisfaction based control can achieve more stable thermal environment. Both of the two control methods can get relatively high scores regarding user acceptance and work performance. There are no statistically significant differences for user acceptance and work performance between two control methods. However, satisfaction based control consumed 15.3% and 11.9% less energy at two test-beds respectively than set-point based control due to more reasonable temperature settings given by the satisfaction based control. [ABSTRACT FROM AUTHOR]

• Synthetic thermal comfort profiles were generated from ASHRAE thermal comfort databases I and II used to evaluate occupant satisfaction and to calculate the HVAC setpoints. • Corrective Power of PCS was integrated into the Comfort Profiles. • PCS with maximum-satisfaction setpoints can improve occupant satisfaction by 18%. • ASHRAE requirement of 80% occupant satisfaction is met with little increase in energy consumption. Personal Comfort Systems (PCS) are energy-efficient devices that create a microenvironment around occupants and can be controlled individually to improve personal comfort. Most studies related to PCS either focus on improving occupants' thermal satisfaction or evaluate the energy-saving potential of PCS devices but do not consider the energy and comfort consequences simultaneously. This paper investigates the impact of PCS on improving occupants' thermal satisfaction and energy consumption across different climates in the U.S. using 16 representative cities. To account for the diversity in individual preferences, 400 comfort profiles for four climatic zones for summer and winter were generated utilizing comfort votes from ASHRAE global thermal comfort database I and II. PCS devices were used to improve occupant satisfaction by integrating the Corrective Power (CP) values into the comfort profiles. CP is defined as the temperature difference between two ambient temperatures that achieve the same thermal sensation one with PCS and another without PCS (reference condition). Then we compute the zone level setpoints that maximize the number of satisfied occupants and evaluate energy consumption of those zone level setpoints using EnergyPlus. Our study includes 3 cases; Case 1: without PCS (baseline), Case 2: only fans in summer and heaters in winter, and Case 3: fans and heaters in both summers and winters as required to improve satisfaction. In each of the three cases, we evaluated the comfort consequences using the comfort profiles, and the energy consumption implications. This accounts for the change in HVAC setpoints and the power requirements of PCS devices. The results show that incorporating PCS can increase occupants' satisfaction from 68% in Case 1 to 77.8% in Case 2 and 86.6% in Case 3. Moreover, energy consumption results in Case 2 are 0–1% higher, and 0–1.3% higher in Case 3 compared to without PCS, depending on the climate. Our results demonstrate that the adoption of PCS with maximum-satisfaction setpoints can improve occupant satisfaction by 18% and meet the ASHRAE requirement of 80% occupant satisfaction with little increase in energy consumption. The comfort profiles generated in this study are also released as a dataset to support future studies. [ABSTRACT FROM AUTHOR]

• Implementation of a model predictive control scheme for HVAC systems is presented. • The hardware/software solution is generalisable to most building management systems. • Cloud solutions can offer low-cost, scalable control in commercial buildings. • Economic MPC reduced energy usage by 1.7% compared to baseline control. • Building thermal performance strongly affects viability of MPC schemes. Many businesses are looking for ways to improve the energy and carbon usage of their buildings, particularly through enhanced data collection and control schemes. In this context, this paper presents a case study of a food-retail building in the UK, detailing the design, installation and cost of a generalisable model predictive control (MPC) framework for its Heating, Ventilation and Air Conditioning (HVAC) system. The hardware/software solution to collect relevant data, as well as the formulation of the MPC scheme, is presented. By utilising cloud-based microservices, this approach can be applied to all modern building management systems with little upfront capital, and an ongoing monthly cost as low as $6.39/month. The MPC scheme calculates the optimal temperature setpoint required for each Air-Handling Unit (AHU) to minimise its overall cost or carbon usage, while ensuring thermal comfort of occupants. Its performance is then compared to the existing legacy controller using a simulation of the building's thermal behaviour. When simulated across two months the MPC approach performed better and achieved the same thermal comfort for a lower overall cost. The economic optimisation resulted in an energy saving of 650 kWh, with an associated cost savings of $240 (an improvement of 1.7% compared to the baseline), while the carbon optimisation gave negligible CO 2 savings due to the inability of the building to shift heating to low-carbon periods. Findings from this study indicate the potential for improving building performance via MPC strategies, however the level of impact will depend on specific building attributes. [ABSTRACT FROM AUTHOR]

• Review identified 29 unintended impacts post residential energy retrofits. • Moisture and dampness are common issues reported in post retrofitted housing. • The indoor environment is significantly affected by airtightness and over-insulation. • Pre retrofit assessment is essential to avoid hygrothermal failure. Improving energy efficiency of existing buildings is currently among the most diverse and extensive mitigation opportunities to reduce energy consumption and CO 2 emissions worldwide. However, the implementation of energy-saving measures has caused unintended impacts, often correlated with dampness and mold growth connected to poor hygrothermal behavior in residential buildings. The focus of this paper is research on the impacts of energy efficiency measures (EEMs) in regard to the hygrothermal behavior resulting from the interaction of building's envelope, indoor environment, and occupants. The results show that dampness and mold growth are by no means exclusive to neglected houses, since the occurrence of these pathologies actually depends upon a complex set of conditions, including indoor and outdoor conditions, occupancy, maintenance, ventilation, mechanical systems, and quality of the envelope. We found that building envelope post-retrofit may suffer from higher levels of moisture and dampness, higher condensations risks, and a faster structural degradation caused by higher humidity levels. We also found that measuring hygrothermal behavior may play a role in more accurately predicting both overall energy consumption and occupant comfort. While hygrothermal behavior may be problematic, we found evidence that retrofits may moderately improve thermal comfort. [ABSTRACT FROM AUTHOR]

Several conflicting criteria exist in building design optimization, especially energy consumption and indoor environment thermal performance. This paper presents a novel multi-objective optimization model that can assist designers in green building design. The Pareto solution was used to obtain a set of optimal solutions for building design optimization, and uses an improved multi-objective genetic algorithm (NSGA-II) as a theoretical basis for building design multi-objective optimization model. Based on the simulation data on energy consumption and indoor thermal comfort, the study also used a simulation-based improved back-propagation (BP) network which is optimized by a genetic algorithm (GA) to characterize building behavior, and then establishes a GA–BP network model for rapidly predicting the energy consumption and indoor thermal comfort status of residential buildings; Third, the building design multi-objective optimization model was established by using the GA–BP network as a fitness function of the multi-objective Genetic Algorithm (NSGA-II); Finally, a case study is presented with the aid of the multi-objective approach in which dozens of potential designs are revealed for a typical building design in China, with a wide range of trade-offs between thermal comfort and energy consumption. [ABSTRACT FROM AUTHOR]

Underfloor air distribution (UFAD) system, which is a relative new method of providing conditioned air and ventilation to the commercial buildings and data centers, is focused by researchers and designers for its potential advantages of better indoor thermal comfort (ITC) and indoor air quality (IAQ), layout flexibility, reduced life cycle costs, and energy saving. However, the application of UFAD system are still obstructed by the gap in the fundamental understanding of some new elements, such as the cooling load calculation, control of the thermal stratification, and evaluation of energy consumption. This paper reviews the current situation of UFAD technology, and the existing problems in recent researches are summarized. To improve the performance of UFAD system, several possible directions for the further studies are suggested. [ABSTRACT FROM AUTHOR]

[Display omitted] • Most of the reviewed studies (62%) focused on group-based comfort models. • ML models, especially PCMs outperformed conventional models (PMV and adaptive). • ML-based controls can improve IAQ while reducing energy consumption and CO 2 levels. • The most frequently used algorithms were SVM, ANN and Ensemble Learning. This paper provides a systematic review on the application of Machine Learning (ML) in thermal comfort studies to highlight the latest methods and findings and provide an agenda for future studies. Reviewed studies were investigated to highlight ML applications, parameters, methods, performance and challenges. The results show that 62% of reviewed studies focused on developing group-based comfort models, while 35% focused on personal comfort models (PCMs) which account for individual differences and present high prediction accuracy. ML models could outperform PMV and adaptive models with up to 35.9% and 31% higher accuracy and PCMs could outperform PMV models with up to 74% higher accuracy. Applying ML-based control schemas reduced thermal comfort-related energy consumption in buildings up to 58.5%, while improving indoor quality up to 90% and reducing CO 2 levels up to 24%. Using physiological parameters improved the prediction accuracy of PCMs up to 97%. Future studies are recommended to further investigate PCMs, determine the optimum sample size and consider both fitting and error metrics for model evaluation. This study introduces data collection, thermal comfort indices, time scale, sample size, feature selection, model selection, and real world application as the remaining challenges in the application of ML in thermal comfort studies. [ABSTRACT FROM AUTHOR]

• Characterizes the urban heat island of Leeds and quantifies the intensity at an hourly resolution. • Demonstrates the importance of site topography and altitude when using air temperature data to quantify urban heat island intensity. • Creation of urban building simulation weather files for comparison with those used for regulatory compliance from a rural site. • Identifies the link between building occupancy patterns and the diurnal nature of urban heat islands. Urban heat islands are evident throughout the world and may become more problematic in temperate climates as global warming continues. This paper characterizes the urban heat island of Leeds, a city in the temperate maritime climate of the UK. Measured weather data from rural and urban sites have been used to quantify the heat island and to create building simulation weather files for the summer of 2013. These weather files have been used to model the impact on energy consumption and thermal comfort in notional non-domestic and domestic buildings, including a hotel, office, house and apartment. The heat island intensity is found to be greatest at night, with the peak in this data set reaching 5.9 °C at 20:00 on 2nd August 2013, with an average peak value of 2.3 °C occurring at the same hour for the case study period. Daytime temperatures were however similar at urban and rural sites. In the notional hotel building, air conditioning costs were increased by up to 40% due to night-time loads whereas consumption in the notional office was similar in all locations. Air temperature, and especially night-time temperature, increased in the domestic examples by an average of approximately 95 additional hours above 25 °C. This work expands knowledge by characterizing the Leeds urban heat island, demonstrating the importance of rural reference site selection for city regions that span a range of altitude, quantifying the gap in building performance when using regional or city centre weather files, and linking building occupancy patterns to the diurnal profile of the urban heat island. [ABSTRACT FROM AUTHOR]

• The method is based on the bidimensional temperature matrix. • Temperatures were captured using infrared thermography. • Firstly, a distortion comparison due to the measurement surface was done. • The temperature measurements were done using a non-continuous surface. • The measuring error was reduced a 42% by using the non-continuous surface. The energy consumption in buildings is still very high despite its reduction in recent years. Buildings' conditioning is the main cause of this energy consumption and, in this sense, air infiltration has a great impact. The control of air infiltration is essential to achieve thermal comfort and energy efficiency. The evaluation of the global airtightness of the building envelope based on pressurization tests has been broadly used to determine the airtightness of buildings, but it cannot define the shape of air leakages. The use of thermography in combination with pressurization tools could be very useful. This paper presents the laboratory experimental measurements to obtain a three-dimensional definition of the air temperature when passing through a controlled envelope gap. The method is based on the bidimensional temperature matrix, captured by infrared (IR) thermography, that the air leakeage produces close to the gap, and depending on the configuration of the air inlet and airflow. The temperature measurement was done using a non-continuous rendering surface in order to avoid the Coanda effect that opaque surfaces cause, achieving in this way a 42% increase in the measuring accuracy. [ABSTRACT FROM AUTHOR]