Electrochromism-induced adaptive fresh air pre-handling system for building energy saving

Building fresh air supply needs to meet certain regulations and fit people’s ever-growing indoor air quality de-mand.However,fresh air handling requires huge energy consumption that goes against the goal of net-zero energy buildings.Thus,in this work,an adaptive fresh air pre-handling system is designed to reduce the cool-ing and heating loads of HVAC system.The sky-facing surface of the system uses electrochromic mechanism to manipulate the optical properties and thus make full use of solar energy(solar heating)and deep space cold source(radiative cooling)by switching between heating and cooling modes.In the cooling mode,the sky-facing surface shows a transmittance of down to zero,while the reflectance is high at 0.89 on average.In the heating mode,the electrochromic glass is highly transparent,allowing the sunlight to reach the solar heat absorber.To obtain the energy-saving potential under different climates,six cities were selected from various climate regions in China.Results show that the adaptive fresh air pre-handling system can be effective in up to 55.4%time of a year.The maximum energy-saving ratios for medium office,warehouse,and single-family house can reach up to 11.52%,26.62%,and 18.29%,respectively.In addition,the system shows multi-climate adaptability and broad application scenarios,making it a potential solution to building energy saving.

Dynamic thermal performance of multilayer hollow clay walls filled with insulation materials:Toward energy saving in hot climates

This study examines the benefits of incorporating passive techniques into multilayer hollow clay brick walls to improve their dynamic thermal performance.The finite element approach was used to solve the incompressible Navier-Stokes and energy equations to analyze the dynamic thermal response of walls exposed to real thermal excitations of the Marrakesh climate.The results show that increasing the emissivity from 0.1 to 0.9 significantly increases the total heat load over 24 h.Furthermore,filling 100% of the cavities with insulation materials delayed the temperature peak by about 2.3 h and lowered the decrement factor by roughly 43%,with a value smaller than 0.07.In addition,it is demonstrated that the total thermal load is reduced by approximately 28% for improved wall configurations(100% insulation filling cavities)compared to traditional wall configurations(100% air filling cavities),which aids in improving building energy efficiency.

The effects of cool materials,façade orientation,and morphological parameters on energy consumption at the residential neighborhood scale

Building surface cool materials are novel materials that can reduce urban heat island intensity and decrease building energy consumption.This study investigated the impact of radiative properties of materials,façade orientation,and morphological parameters on energy consumption in six typical residential neighborhoods in Nanjing,China.The neighborhood energy consumption of 16 application schemes considering the façade orientation factor is compared to determine the best energy-saving scheme.Seasonal and annual energy-saving rates,savings in electricity costs,and the price ceiling for materials per unit area are analyzed.The results show that for low-rise buildings,using cool materials only on the roof can reduce the annual energy consumption by 1%.When cool or super cool materials are also used on the building façade,the annual energy saving rate can be up to 3.4%and 4.3%,respectively.Using cool materials on the south façade of buildings is not recommended due to significant heat loss in winter.Considering savings in electricity costs and the price ceiling for materials per unit area,the price of cool and super cool materials should be less than 3.0 and 3.7 RMB/m2,respectively,assuming a lifespan of eight years in Nanjing.

Energy saving analysis of a transparent radiative cooling film for buildings with roof glazing

A transparent radiative cooling(T-RC)film with low transmittance in solar spectra and selectively high emissivity in the atmospheric window(8-13𝜇m)is applied on roof glazing for building energy saving.To evaluate the per-formance of the T-RC film,two identical model boxes(1.0 m×0.6 m×1.2 m,L×W×H)were constructed and the inside air temperatures were measured in August in Ningbo,China.Results show that the maximum temperature difference between the two model boxes with and without the T-RC film was 21.6℃during the experiment.A whole building model was built in EnergyPlus for the model box.With a good agreement achieved between the calculation results and the measured temperature data,the experimentally validated EnergyPlus model was then extended to an 815.1 m^(2)exhibition building with roof glazing to analyze the annual air conditioning(AC)energy consumption.The results show that by incorporating both the T-RC film’s cooling benefit in summer and heating penalty in winter,the annual AC energy consumption of the exhibition building can be reduced by 40.9-63.4%,varying with different climate conditions.

Deep Weighted Fusion Learning(DWFL)-based multi-sensor fusion model for accurate building occupancy detection

With the advancement of artificial intelligence,the dominance of deep learning(DL)models over ordinary machine learning(ML)algorithms has become a reality in recent years due to its capability of handling complex pattern recognition without manual feature pre-definition.With the growing demands for power savings,building energy loss reduction could benefit from DL techniques.For buildings/rooms with the varying number of occupants,heating,ventilation,and air conditioning(HVAC)systems are often found in operations without much necessity.To reduce the building’s energy loss,accurate occupancy detection/prediction(ODP)results could be used to control the proper operations of HVACs.However,ODP is a challenging issue due to multiple reasons,such as improper selection/deployment of sensors,inefficient learning algorithms for pattern recognition,varying room conditions,etc.To overcome the above challenges,we propose a DL-based framework,i.e.,Deep Weighted Fusion Learning(DWFL),to detect and predict occupancy counts with optimal multi-sensor fusion structure.DWFL fuses the extracted features from multiple types of sensors with the priority/weight assignment to each sensor.Such weight assignment considers different room conditions and the pros/cons of each type of sensor.To evaluate DWFL model in terms of occupancy prediction accuracy,we have set up an experimental testbed with low-cost cameras,carbon dioxide(CO_(2)),and passive infrared(PIR)sensors.Among the recently proposed occupancy detection models,DeepFusion utilized deep learning model on heterogeneous sensor data and achieved 88%accuracy in occupancy count estimation(Xue et al.,2019).Another deep learning-based model MI-PIR achieved 91%accuracy on raw analog data from PIR sensors(Andrews et al.,2020).Our research outcome is 94%.Therefore,the experiment results show that our DWFL scheme outperforms the state-of-the-art ODP methods by 3%.

Energy saving potential of a fresh air pre-cooling system using radiative sky cooling

To achieve required indoor air quality,fresh air supply in buildings should meet relevant standards and regulations.However,the handling of fresh air introduced a cooling load that takes up a large portion of building energy consumption,especially in tropical and subtropical areas.A proper way should be employed to reduce the cooling load of fresh air.Radiative sky cooling,which is the process that an object cools itself by emitting thermal radiation to outer space without any energy input,is a cost-effective and eco-friendly technology.In this work,a fresh air pre-cooling system using radiative sky cooling is proposed to reduce fresh air cooling load.The system,consisting of filters,a radiative air-cooling system,an air handling unit(AHU),fans,etc.,is installed on the rooftop of the modeled building.Six cities in low-latitude areas are selected and investigated.Results show that with the radiative air-cooling system installed,annual cooling energy consumption of the modeled building can be reduced by around 10%in most cities.For arid areas,e.g.,Abu Dhabi,the system has even better performance with 19.34%annual cooling energy saving.

Energy and comfort performance of occupant-centric air conditioning strategy in office buildings with personal comfort devices

The personal comfort system(PCS)aims to meet individual thermal comfort demands efficiently to achieve higher thermal comfort satisfaction while reducing air conditioning energy consumption.To date,many PCS devices have been developed and evaluated from the perspective of thermal comfort.It will be useful for future PCS development if an approach to quantify the thermal comfort and energy performance of certain PCS devices and their combinations with consideration of user behaviors can be established.This study attempted to fill this gap by integrating thermal comfort experiments,occupancy simulations,usage behavior modeling,and building energy simulation technologies.First,human subject experiments were conducted to quantify the thermal comfort effects of the PCS.Then,the Markov chain model and conditional probability model were employed to describe the room occupancy and PCS usage behaviors.Finally,the extended comfort temperature range and user behavior models were imported into the building energy simulation tool to analyze the energy-saving potential of the PCS.The results show that the use of PCS can significantly improve occupants,thermal comfort and satisfaction rate under both warm and cool conditions.Using a cooling cushion and desktop fan can lift the upper limit of the comfortable temperature to 29.5℃while the heated cushion can extend the lower limit to 15℃.By increasing the air conditioning temperature setpoint by 2℃in summer and reducing by 2.5℃the heating temperature setpoint in winter,PCS devices can reduce heating and air conditioning energy consumption by 25%-40%while maintaining occupants’thermal comfort.

Performance Comparison and Analysis of Heating and Ventilation of Two Kinds of Roof Solar Collectors

Two roof solar collectors (RSCs), conventional single pass RSC and new double pass RSC, were compared. The new roof solar collector, which is formed by integrating a double pass solar air collector with the roof of the building, can be operated more efficiently for space heating in winter, and for natural ventilation in other seasons. To evaluate the performances of the two RSCs for both space heating and natural ventilation, a single traditional Chinese style house, on which the two RSCs will be mounted, was developed. The efficiency of solar energy conversion for the new RSC is higher than that of the single pass one by 10% on average, and the ventilation rate contributed by natural ventilation for the new RSC can be improved to a great extent for most cases, indicating that the new RSC is superior to the single pass one from the viewpoint of both space heating and natural ventilation. The new RSC is more potential for improving indoor thermal environment and energy saving of buildings.

Applications of Low-Emissivity Hybrid Insulation in South Korea

Low-Emissivity Hybrid Insulation has been developed in South Korea. It combines both resistive and reflective insulating principle. It is composed with many layers of reflective aluminum foils and honeycomb-structure air cells with polyethylene foam. This paper introduces recent South Korean building insulation code and the applications of Low-Emissivity Hybrid Insulation to meet the code.

Deep learning for estimating energy savings of early-stage facade design decisions

The selection of high-performance building facade systems is essential to promote building energy efficiency.However,this selection is highly dependent on early-stage design decisions,which are extremely challenging considering numerous design parameters with early-stage uncertainties.This paper aims to evaluate the appli-cability of deep learning networks in estimating the energy savings of different facade alternatives in the early-stage design of buildings.The energy performance of two competing façade systems(i.e.,Ultra-High-Performance Fiber-Reinforced-Concrete and conventional panels)was estimated for different scenarios through building en-ergy simulations using EnergyPlusTM.Three deep learning networks were trained using the collected data from the simulation of fourteen buildings in fourteen different locations to estimate the heating,cooling,and total site energy savings.The accuracy of trained deep networks was compared with the accuracy of three common data-driven prediction models including,Gradient Boosting Machines,Random Forest,and Generalized Linear Regression.The results showed that the deep learning network trained to predict building total site energy savings had the highest accuracy among other models with a mean absolute error of 1.59 and a root mean square error of 3.48,followed by Gradient Boosting Machines,Random Forest,and last Generalized Linear Regression.Similarly,deep networks trained to predict building cooling and heating energy savings had the lowest mean average error of 0.20 and 1.17,respectively,compared to other predictive models.It is expected the decision support system developed based on this methodology helps architects and designers to quantify the energy savings of different facade systems in early stages of design decisions.

Heating load reduction characteristics of passive solar buildings in Tibet,China

China’s Tibet autonomous region has abundant solar energy resources,cold winters,and cool summers.These are ideal conditions for the application of passive solar heating methods.However,differences in climatic conditions and building types can significantly affect passive solar technology’s feasibility,which makes it challenging to promote passive solar buildings in Tibet.In this study,the suitability zone for passive solar technology is categorized based on the sub-zoning indicators for Tibet.By modeling between direct gain windows,Trombe walls,and attached sunspaces,the effect of indoor thermal environments and the capacity for heating load reduction is compared for different passive solar technologies.The climate-difference impact analysis shows that the I-B-1 zone is better suited for passive solar technology than other climate zones.More specifically,this zone has an average energy-saving rate difference of up to 28.61%compared to the II-A-1 zone.The analysis of the impact of building type differences indicates that residential buildings have higher Trombe wall-to-wall ratio limits and more significant potential for energy savings than office buildings.The study also clarifies the implications of Tibet’s climate conditions and building type differences on the effectiveness of passive solar technology.Moreover,it recommends appropriate passive solar technology adoption methods for every climate zone.This study can be used as a reference and engineering guide to improving the indoor thermal environment of Tibetan buildings,tailored to the highly variable local conditions.

Optical and thermal performance analysis of aerogel glazing technology in a commercial building of Hong Kong

Improving the thermal insulation of glazing units is a common strategy of reducing building energy use for spacing cooling.This paper newly examined the application of aerogel glazing technology in Hong Kong by the means of laboratory testing and simulation.Nine prototypes of granular aerogel glazing were selected to examine their optical properties,and the measured optical properties of aerogel were used to calculate the total window thermal performance indices.A typical 40-story commercial office building was chosen for energy simulation to compare the thermal performance of aerogel glazing with different glazing technologies in Hong Kong.The results showed that aerogel glazing could achieve the promising reduction of window heat gain up to 57%and cooling energy up to 8.5%compared with double glazing.The heat insulation performance of aerogel glazing is even better than the double glazing with low-E coating in Hong Kong.Therefore,aerogel glazing can be a good alternative of glazing to comply with the existing local Overall Thermal Transfer Value(OTTV)requirement and to reduce the building energy use for space cooling in Hong Kong and other regions.