Effect of building energy efficiency standards on carbon emission efficiency in commercial buildings

The building sector plays a crucial role in the worldwide shift toward achieving net-zero emissions.Building energy efficiency standards(BEESs)are highly effective policies for reducing carbon emissions.Therefore,exploring the provincial variations in carbon emission efficiency(CEE)in the building sector and identifying the effect of BEESs on CEE is crucial.This study focuses on commercial buildings in China and applies a difference in differences model to evaluate the impact of BEESs on the CEE of commercial buildings.The slacks-based measure–data envelopment analysis model is employed to assess the CEE of commercial buildings in 30 Chinese provinces from 2000 to 2019.Furthermore,heterogeneous tests are used to explore how climate characteristics and economic conditions affect the efficiency of BEESs.The results indicate that BEESs positively influence the CEE of commercial buildings.Specifically,a 1%increase in the intensity of BEESs causes a 0.1484%increase in the CEE of commercial buildings.Moreover,the impact of BEESs is particularly pronounced in the southern and western provinces.This study provides valuable scientific evidence for governments to enhance BEESs implementation.

The Problematic Build-pollution and Its Architectural Impact beyond the Architectural Uprising in Jordan （Case Study： Housing ＆ Commercial Buildings of Amman City）

By the adoption of architecture as a means for communication and discourse between the architect and the recipient, yet current arguments took the conscious of local architect away from the uniqueness and the concerns of the society, as well. It also dominated its creative and educational capabilities through making him distracted in formal non-rational overestimated compositions without dealing with the concerns and needs of the society and sympathize with its affection that led to the appearance of an intellectual crisis resulted from the loss of design strategy in the current trends among some Jordanian architects. As such, not only substantial amount of the leading architectural intellect had deteriorated to formal practices but also it took another approach, becoming incomplete intellectual isolation practicing an overestimated architecture that satisfies free markets requirements which appeared along with the globalization economy. This study attempts to comparatively investigate the variation in some architectural practices as a methodology based on design readings of the previous issues which were characterized of architectural uniqueness and current issues that lack of the existing intellectual references.

Light Environment Analysis and Design Strategies of Large Commercial Buildings-A Case Study in Harbin

This paper presents a comprehensive survey for the situation of light environment in large commercial building through architecture design information,subjective and objective fieldwork and online survey for three typical commercial building forms( centralized form, covered mall shopping center, lifestyle center).Descriptive method is used for statistical analysis which includes the evaluation of light environmental quality,subjective assessment among daylight and artificial illumination,effect of physical characteristics on subjective assessment of light environment. Based on the results of statistical analysis,the improving strategies for light environment are proposed in terms of making use of daylight,artificial lighting design and improvement of the overall light environmental quality. The design strategies which based on the consumer needs is very important can provide help for further impact prediction on the quality of light environment.

On the Teaching of Interior Design for Commercial Buildings in the Age of Experience Economy

The coming of the experience economy time not only brings huge challenge to traditional commercial space design, but also makes new demand on university commercial building intenor design- course teaching. Expenencmg construction is considered, as an important content in the teaching process to obtain better teaching objectives by means of design thought of ＂theme setting-physical manifestation- emotional experience＂, and assist students to set up design approach adjust to the new market demand.

Impact of COVID-19 on the energy consumption of commercial buildings:A case study in Singapore

Numerous studies have demonstrated that commercial activities have significantly reduced during COVID-19,while there are few studies disclosing the consequent impacts on the energy consumption of commercial build-ings.This study explores the changes in energy consumption of different types of commercial buildings in Sin-gapore under the impact of the pandemic,using commercial building energy performance data from 2017 to 2020(n=540).The sampled buildings include 93 hotel buildings,303 office buildings,106 retail buildings,and 38 mixed developments.The analysis mainly used linear regression and paired sample t-test.The results showed that relative to 2019,the mean energy use intensity(EUI)of sampled commercial buildings decreased by 56.77 kWh/m^(2)in the pandemic year(2020),a plunge of 19.9%.The extent to which the EUI of each type of commercial building is affected by the pandemic is found as:mixed development>retail>office>hotel.The study also identi-fied the factors that significantly influenced the EUI of commercial buildings before and during the pandemic.The results of the study complement existing knowledge about the factors influencing energy consumption in com-mercial buildings by considering the impact of the pandemic and furthermore contribute to the improvement of energy management in commercial buildings by providing directions for building energy efficiency approaches.

Trends,benefits,risks,and challenges of IoT implementation in residential and commercial buildings

Residential and commercial buildings are undergoing a dramatic change,and the Internet of Things(IoT)tech-nologies are shaping the future of these buildings.Researchers have recently utilized IoT in a variety of applica-tions and settings to transfer conventional buildings into smart,efficient,and secure buildings.While functional IoT approaches have been developed,there is still a need for improvement in IoT applications and operations to understand the full potential of this technology.This improvement can properly be done by bridging the gaps of the current literature to provide a building block for future studies.The objective of this paper is to present a comprehensive review of research work on the existing technologies and applications of IoT in residential and commercial buildings.For residential buildings,studies are divided to three major categories of home automa-tion,intelligent energy management systems,and healthcare facilities.For commercial buildings,the current literature is divided to four categories of office buildings,healthcare facilities,educational buildings,and restau-rants and retail facilities.Based on the review of each category,the trends,current benefits and risks,and future challenges of the IoT implementation in the built environments are identified and discussed.In particular,in-tegration of various IoT technologies with different capabilities,data storage and processing,and privacy and security risks are identified as the main challenges for IoT implementation.In addition,our results show that the commercial building sector has received more attention compared to the residential building sector.By providing the directions for future research opportunities,this paper benefits IoT developers and researchers to properly identify their work boundaries and define their contributions.

A method to predict cooling load of large commercial buildings based on weather forecast and internal occupancy

Considering the fact that customers of large commercial buildings have the characteristics of the higher density and randomness, this paper presented an air- conditioning cooling load prediction method based on weather forecast and internal occupancy density. The multiple linear feedback regression model was applied to predict, with precision, the air conditioning cooling load. Case analysis showed that the largest mean relative error of hourly and the daily predicting cooling load maximum were 18.1% and 5.14%, respectively.

Investigation and analysis on indoor air quality of large commercial office building in summer

To study the indoor air qualities（IAQ）of large commercial office buildings in Hunan province of China and the corresponding improvement methods,the IAQ of a large commercial office building in Changsha in July,2008,is investigated.A questionnaire survey and field tests are used to collect data.According to the data of twelve rooms in this building,objective evaluation and the subjective evaluation of the IAQ are obtained.Almost all of the environmental parameters in these rooms basically meet the standards of the objective evaluation.But the average concentration of carbon dioxide in most rooms cannot reach the value of the cleanliness standards,1 255 mg/m^3.The average acceptability of the IAQ in these rooms is 71%,which is lower than the value of the ASHRAE 55—1992 standards,80%.The proper increase in the wind speed and the indoor fresh air supply can greatly improve the objective evaluation and the subjective evaluation of the IAQ.

Modern Hotel Buildings in Guangzhou

Hotel buildings as important components of modern commercial buildings in Guangzhou City were originated from foreign hotel buildings, and urged the transition of national hotels to western advanced hotel buildings by applying western architectural styles, architectural science and management. This paper sorted out the development history of modern hotel buildings in Guangzhou City, summarized the features to provide references for the development of modern hotel buildings and protection and research of antique hotel buildings in Guangzhou.

An Approach to Optimize Multi-family Residence HVAC Systems Using Digitalization

As mentioned by National Geographic(2017),70%of world’s population is expected to live in large apartment buildings by 2050.Today,buildings in cities generate 30%of world’s greenhouse gas emission or GHG(National Geographic,2017).Major urban centers are committed to reducing greenhouse gases by 80%by 2050(IEA,2021).However,achieving such goals in rental properties is not easy.Landlords are hesitant to use high-efficiency technologies because,typically,tenants pay the utilities bill.However,that situation is rapidly changing.For example,New York City like other US cities,is considering a carbon cap on all large buildings(Local Law 97,2019).That means landlords will pay a carbon penalty if the building’s carbon footprint exceeds certain threshold no matter who uses that carbon.The Pacific Northwest National Laboratory(PNNL)has received funds from DOE(US Department of Energy)with the collaboration of a commercial partner to address emerging energy efficiency market opportunity in multi-family or rental housing as discussed above.It has partnered with a large national real estate owner in order to test a novel energy optimization method at a rental property in Tempe,Arizona.By using a seamless-integrated method of acquiring building’s operating data,the optimization approach essentially resets setpoints of different energy consuming equipment such as chillers,boilers,pumps,and fans.Data-driven optimization approach is pragmatic and easily transferrable to other buildings.The authors shall share the problem background,technical approach,and preliminary results.

Optimal Simulation of Distributed Generation System for CO_2-Reduction in Supermarket and Restaurant

After the great east Japan earthquake in 2011, Japanese energy system has been expected to prioritize safety and trustworthiness. Now, distributed power systems are considered as one solution, but utilizing exhaust heat is an important task to be solved. The purpose of this study is to build a simulation model to harness waste heat of commercial buildings. We obtained two types of data： distributed power system in 1/15 scale model of supermarket, restaurant and real world energy consumption of the two buildings. Results showed cold cabinets, whose electricity was affected by temperatures outside and inside, consumed most in supermarket. While air conditioning, affected by air enthalpy of outside and inside, consumed most in restaurant. According to our simulation with gas engine, PV （photovoltaic） panel, PCM （phase change material）, thermal storage, FCU （fan coil unit） and refrigerated cabinets in scale model, we could reduce 27% of CO_2 emission and 25% of running cost by selecting optimal size.

Characteristics of electricity consumption of different industry types considering atmospheric condition

Reducing greenhouse gases (RHG) is going on actively in the international movement. In the field of architecture, RHG is an inevitable work. To establish a plan for RHG, firstly we need to reduce energy consumption. Greenhouse gas generated by energy consumption is the main cause of global warming. For this we should know that how much electricity consumption we use. The research targets of this study are commercial buildings with various businesses. Their electricity consumption was analyzed by business units rather than buildings. Each business was divided into 13 sectors according to industrial classification and electricity consumption was analyzed for each industry. For commercial buildings, the electricity consumption is done by the private sector and construction management is an autonomy system in private instead of an integrated management system. In this study, we classified and analyzed the electricity consumption characteristics according to collected data, analyzed the relationship between the electricity consumption with atmospheric temperature through SPSS, and developed an electricity prediction model.

Option to Expand a Vacant Land Option

Pricing options(calls and puts)in general is a complex issue,given the assumptions that one has to make and sometime markets are asymmetric.McKean-Samuelson(1965)is the first model that prices a perpetual American option on a vacant land.Years later,Black and Scholes(1973)create a formula to price a call option on corporate liabilities.In the context of real estate discipline,it seems that there is no formula that prices a call on commercial building,specifically office building on a vacant land.The contribution of this study is exactly responding to the latter statement.The results illustrate that the formula of a commercial building on a vacant land is simply two call options—similar to option straddle.

Comparative study of univariate and multivariate strategy for short-term forecasting of heat demand density:Exploring single and hybrid deep learning models

Accurate short-term forecasting of heating energy demand is needed for achieving optimal building energy management,cost savings,environmental sustainability,and responsible energy consumption.Furthermore,short-term heating energy prediction contributes to zero-energy building performance in cold climates.Given the critical importance of short-term forecasting in heating energy management,this study evaluated six prevalent deep-learning algorithms to predict energy load,including single and hybrid models.The overall best-performing predictors were hybrid models using Convolutional Neural Networks,regardless of whether they were multivariate or univariate.Nevertheless,while the multivariate models performed better in the first hour,the univariate models often were more accurate in the final 24 h.Thus,the best-performing predictor of the first timestep was a multivariate hybrid Convolutional Neural Network–Recurrent Neural Network model with a coefficient of determination(R^(2))of 0.98 and the lowest mean absolute error.Yet,the best-performing predictor of the final timestep was the univariate hybrid model Convolutional Neural Network–Long Short-Term Memory with an R^(2)of 0.80.Also,the prediction accuracy of the best-performing multivariate hybrid models reduced faster per hour compared to the univariate models.These findings suggest that multivariate models may be better suited for early timestep predictions,while univariate models may be better suited for later time steps.Hence,combining the models can enhance accuracy at various timesteps for achieving high fidelity in forecasting and offering a comprehensive tool for energy management.

Diagnosis of building energy consumption in the 2012 CBECS data using heterogeneous effect of energy variables:A recursive partitioning approach

Numerous previous literature has attempted to apply machine learning techniques to analyze relationships between energy variables in energy consumption.However,most machine learning methods are primarily used for prediction through complicated learning processes at the expense of interpretability.Those methods have difficulties in evaluating the effect of energy variables on energy consumption and especially capturing their heterogeneous relationship.Therefore,to identify the energy consumption of the heterogeneous relationships in actual buildings,this study applies the MOdel-Based recursive partitioning(MOB)algorithm to the 2012 CBECS survey data,which would offer representative information about actual commercial building characteristics and energy consumption.With resultant tree-structured subgroups,the MOB tree reveals the heterogeneous effect of energy variables and mutual influences on building energy consumptions.The results of this study would provide insights for architects and engineers to develop energy conservative design and retrofit in U.S.office buildings.