Large-scale Commercial Buildings Research Articles

Risk assessment of fire safety in large-scale commercial and high-rise buildings based on intuitionistic fuzzy and social graph

Fire risk assessment is a useful method to identify potential fire risks and improve safety management, especially for those large-scale commercial and high-rise buildings. However, after the establishment of the fire risk indexing system, the weight assignment in the existing assessment methods is not scientific and objective enough to synthesize the opinions of multiple experts with different knowledge backgrounds and working experience. To solve this problem, a novel fire risk assessment for large-scale commercial and high-rise buildings is proposed with intuitionistic fuzzy and social graph. First, based on the national standards of fire safety management, the monthly maintenance and the annual fire assessment reports, the indexing system is established. Then, a scientific and objective weight assignment method called Social Graph based Intuitionistic Fuzzy Analytic Hierarchy Process (SGIFAHP) is proposed such that (1) by utilizing the intuitionistic fuzzy and social graph, it can synthesize the positive, negative and hesitant opinions from multiple experts by considering the importance of experts; (2) a novel mechanism is designed to automatically check and repair the consistency of the opinions of each expert when the expert compares the importance of risk factors. To validate the performance of the proposed method, eleven buildings are selected and their fire safety data are collected. A comparison analysis is carried out with the existing methods to compare the assigned weights. The comparison and score validation conclude that the proposed method can assign more reasonable, scientific and objective weights than the existing methods while the scores are comprehensive and strongly interpretable. In the end, a guide is offered to help safety owners and inspectors improve fire safety with the proposed method.

Business Perspectives of Distributed System Operators for Solar Rooftop-as-a-Service

Rising fossil energy prices and the significantly decreasing prices of energy technology have resulted in electricity consumers having the option to install solar PV rooftops to rely on the self-consumption of clean energy. However, the increase in this amount is affecting the revenue of electricity as a utility, which must adapt and develop its business model to accommodate the situation. If the utility cannot be adapted in time, it may lead to a loss of income from services and the sale of electricity from fossil energy. The utility in Thailand’s electricity market that acts as the distribution system operator (DSO) is known as the Provincial Electricity Authority (PEA), and the Metropolitan Electricity Authority (MEA) is responsible for managing distribution networks and customers. There are four types of solar rooftop-as-a-service (RaaS) business perspectives they could consider as opportunities through which to minimize revenue impact. The business services were designed for the DSO customer as follows: Consulting, Design, and Installation (CDI); Operation and Maintenance (O&M); Energy Service Company (ESCO); and Power Purchase Agreement (PPA). The model comprises four customer segments: residential buildings and small-, medium-, and large-scale commercial buildings. This paper applies SWOT, Five Forces, 4P marketing, and economic impact analyses to identify the possibilities when using the DSO business model. The SWOT analysis demonstrates that ESCO and PPA are strengths in the DSO’s performance characteristics and existing customer data. In the electricity industry, both models offer enormous customer bargaining power in terms of a Five Forces analysis. The main reason is that there is currently high competition in the installation service. In the 4P analysis result, the price per unit is found to be significantly lower than in residential scenarios. Therefore, there is a format for presenting promotions with an advantage over competitors. Deploying an after-sales service that brings convenience to all customer segments is needed. The economic analysis conducted using Cournot competition game theory shows a significant differential in the Medium (M) and Large (L) customer sectors’ competition due to lower technology prices. In conclusion, with the current regulatory framework and criteria, the ESCO and PPA show the best practical model from a utility business perspective. The recommendation for DSO is to create a strategic ecosystem and to link it with private companies as their partnership business.

Analysis and prediction of carbon emission in the large green commercial building: A case study in Dalian, China

Reducing carbon emissions from the construction industry has been vital to addressing the growing global environmental change challenge. Building energy consumption data is crucial to many applications, such as carbon emission auditing, energy efficiency improvement, etc. This study aims to mine the energy consumption patterns of commercial buildings and explore the applicability of a data-driven model in building carbon emissions prediction. This research selected Dalian's large-scale green commercial building as a case study. The electrical load data for the past four years were collected, and the indoor and outdoor environmental data were monitored under different seasons. Machine learning was used to develop building carbon emissions forecasting models. The average annual increase rate of building electricity consumption before the pandemic was 5.9%. Miscellaneous electric loads (MELs) is the largest electricity consumer in the target building. On typical days, indoor illuminance and CO2 are highly correlated under different seasons. A forecasting model based on ensemble learning is found to have certain advantages in building carbon emissions prediction.

Research on systematic analysis and optimization method for chillers based on model predictive control: A case study

Chillers are key components of heating, ventilation, and air-conditioning (HVAC) systems and consume large amounts of energy in commercial buildings. This study conducted field tests on the operation performance of chillers in large-scale commercial buildings and analyzed the typical problems influencing their energy efficiency. Subsequently, based on the field test results, a systematic optimization method for chillers was proposed based on external factors, internal factors, and synergy control between them. For external factors, the cooling load prediction model comprising a linear model based on the time series was constructed using historical operation data; the proposed model could be applied to guide the control of chillers to match the building cooling demand accurately with high energy efficiency. For internal factors, the energy performance model of the chiller was constructed using historical operation data, and the operation characteristics of constant-speed and variable-speed centrifugal chillers were compared and summarized. Then, using the cooling load prediction model and energy performance model of the chillers, a new control strategy was proposed to improve the energy performance under different operating conditions throughout the year. Compared with previous operation modes in 2018 and 2019, the energy saving rates reached 19.21% and 5.22%, respectively, achieving considerable energy-saving and CO2 emission reduction effects.

A timeseries supervised learning framework for fault prediction in chiller systems

Chiller systems play a critical role in large-scale commercial buildings, providing air-conditioning to occupied spaces and possibly also providing cooling to other process equipment. When faults occur in these systems, significant energy can be wasted, and costly maintenance may be necessary to restore the equipment to working conditions. Meanwhile, occupant comfort or operational efficiency may be sacrificed due to the lost cooling capacity. To help avoid these issues, various fault detection and diagnosis (FDD) approaches have been proposed and developed to automatically detect and diagnose faulty conditions in chiller systems. Unfortunately, merely detecting faults may not be sufficient to fully mitigate the ill effects. If instead faults can be predicted ahead of time, then operators may be able to take corrective action to prevent the faults from occurring in the first place. Nevertheless, fault prediction has rarely been investigated, perhaps due to its inherent challenges compared to standard FDD. In this work, we develop a machine-learning-based modular framework to predict faults in chiller systems based on timeseries sensor data. Specifically, the overall model consists of autoencoder, classifier, and thresholder components, with multiple variants of each component type. These models undergo supervised training using labeled data containing both normal and faulty instances. To validate the proposed approach, we leverage various deep and shallow components (see the Terminology defined in Section 4) in the modular framework with data collected from multiple chillers over one or more years of operation. Sample results show the efficacy and applicability of the framework for fault prediction, thus illustrating the potential for real-world application.

Modeling and techno-economic optimization of overhead panels and reflectors in near-wall mounted PV systems

Compact installations of solar photovoltaic (PV) systems to maximize the use of land are a necessity in urban regions, where the available installation areas exposed to solar irradiance are limited. Large-scale commercial buildings are typically surrounded by open areas and have busy roofs with mechanical and air conditioning equipment. This study assesses three candidate installation schemes of dual-row near-wall ground-mounted PV systems. The baseline configuration (BC) comprises the classic parallel PV rows. The overhead reflector (OR) configuration adds reflectors above the panels of the second row, to be supported by the wall. The overhead panel (OP) configuration moves the panels of the second row to be above the panels of the first row. Mathematical models are developed to evaluate and optimize the techno-economic performance (in terms of total annual energy production “EPV” and levelized cost of electricity “LCOE”, respectively) based on the tilt angles of the two rows, as well as the length and tilt angle of the reflector in the OR configuration. The results show maximum energy production of 12.54, 12.77, and 12.53 MW for the three configurations, respectively. When the land cost is excluded, the minimum LCOE is 0.0696, 0.0698, and 0.0696 USD/kWh, which correspond to 0.1318, 0.1332, and 0.1182 USD/kWh, respectively, when the land cost is included. The EPV-LCOE Pareto fronts are dominated by different designs of the OR and the OR/OP configurations when excluding and including land cost, respectively, which demonstrates the favorableness of the two proposed schemes, compared to the baseline one.

Energy consumption and indoor environment evaluation of large irregular commercial green building in Dalian, China

Commercial buildings, especially large irregular commercial buildings with complex space and dense occupation, have high energy consumption and carbon emissions. Moreover, the actual operating performance differs significantly from its design, in terms of energy conservation and carbon emissions. This study selected an irregular large-scale green commercial building in Dalian, a coastal city in the cold zone of China. The running energy consumption and indoor physical environment were investigated and analyzed, and a satisfaction survey was conducted using the Post-occupancy evaluation (POE) methods. The promotion strategies of holistic design were proposed by comparing the operational building performance with the design target. The research shows that the energy consumption of the building was 68.02 kWh/m2, which failed to meet the standard requirements. The carbon emission was 74.76 kgCO2/m2. Less than 10 % of humidity in summer fell in the expected range, and the lowest value of PM2.5 concentration in winter was 28.3 %. The score of indoor environmental quality and service satisfaction was low. This paper identifies the problems and analyzes the differences between the operation stage and design phase. It provides recommendations and guidelines for optimizing the carbon emission and indoor environment based on the performance improvement of building operational conditions.

Design of Commercial Building Complex Based on 3D Landscape Interaction

The needs of visualization of multidimensional information of the earth, 3D virtual construction, and engineering design are driving research into some technologies of 3D landscape interactive design. The function and availability of the 3D landscape interactive system are largely determined by an efficient interactive algorithm, which is an essential part of the interactive design system of commercial architecture landscape. This paper investigates and evaluates the current state of 3D landscape intersection in landscape design, compares and evaluates existing 3D landscape modeling methods, and proposes some recommendations for the feasibility of a 3D commercial building performance scheme that will serve as a model for future large-scale urban commercial building landscape modeling. When the landscape is represented by objective substances like visual images, people are more likely to actively engage with nature and make automatic decisions based on physiological responses. The introduction of 3D technology has given people’s lives a new lease on life, and it is critical to optimize urban planning and management.

A Novel Fire Risk Assessment Approach for Large-Scale Commercial and High-Rise Buildings Based on Fuzzy Analytic Hierarchy Process (FAHP) and Coupling Revision.

In recent years, much more emphasis than before has been placed on fire safety regulations by the local and central authorities of China, which makes fire risk assessments more important. In this paper we propose a new fire risk assessment approach for large-scale commercial and high-rise buildings that aims to evaluate the performances of their fire safety systems; this should improve the fire risk management and public safety in those buildings. According to the features of large-scale commercial and high-rise buildings, a fire-risk indexing system was built, and based on it we established a scientific fire risk evaluation system. To this end, the fuzzy analytic hierarchy process (FAHP) was used to assign a reasonable weight to each fire risk factor in the evaluation system. In addition, we revised the original scores by analyzing the coupling relationships among the fire risk factors. To validate our system, we selected 11 buildings in Shandong province and collected their fire safety data. Then, we calculated the final scores for the fire safety management of those buildings, and the results show that: (1) our fire risk evaluation system can assign reasonable weights; (2) the proposed evaluation system is comprehensive and has strong interpretability, since it exploits the coupling relationships among the risk factors. The novelty of the proposed approach lies in that it integrates opinions from multiple experts and utilizes coupling relationships among the factors. Further, the feedback from the approach can find not only the weaknesses in fire risk management, but also the potential causes of fires. As a result, the feedback from our assessment can assist the safety chiefs and inspectors with improving fire risk management.

Building energy consumption prediction and energy control of large-scale shopping malls based on a noncentralized self-adaptive energy management control system

Central air conditioning is the main energy-consuming equipment in modern large-scale commercial buildings. Its energy consumption generally accounts for more than 60% of the electricity load of an entire building, and there is a rising trend. Focusing on reducing central air conditioning energy consumption is a first priority to achieve energy savings in modern large-scale commercial buildings. To study the main influencing factors of central air conditioning energy consumption in large shopping malls, in-depth collection and analysis of energy consumption data of Shenzhen Tian-hong shopping mall were considered, and the impact of factors such as the basic composition of central air conditioning, time, and Shenzhen weather on the energy consumption of shopping malls was considered. The most representative Buji Rainbow store of the Rainbow Group is used as the research object. The influencing factors of central air conditioning on its energy consumption are divided into air conditioning pumps, host 1–1, host 1–2, host 2–1, and host 2–2. The power consumption of the freezer and the eight impact indicators of time and weather in Shenzhen were constructed using Pearson correlation coefficients and a long short-term memory neural network method to construct a regression model of the energy consumption prediction of the mall building. The average relative deviation between the predicted energy consumption values and the measured energy consumption values is less than 10%, which indicates that the main influencing factors selected in this paper can better explain the energy consumption of the mall, and the obtained energy consumption prediction model has high accuracy.

Economic optimization and potential analysis of fuel cell vehicle-to-grid (FCV2G) system with large-scale buildings

The popularization of fuel cell electric vehicles (FCV) is an important measure for solving the global carbon emission reduction problem. However, the overall cost of FCV and the production cost of fuel hydrogen are relatively high, so FCV promotion is slow. Considering that FCV has the characteristics of near-zero carbon emission and high endurance, which is suitable for the vehicle-to-grid (V2G) system, this paper hoped to provide a boost for the promotion of FCV by analyzing the economic potential of the FCV2G system. Firstly, a large-scale commercial building in Japan was selected as the research target and the agent of vehicles to provide a V2G service for the power grid. Then the Monte Carlo simulation method was used to simulate the visiting time and condition of the vehicles. Secondly, the discharge model was established. Combined with the carbon emission price and self-elasticity coefficient of the discharge price, the overall economic optimization model was established. Then, the genetic algorithm was used to optimize the model. With continuous importing of FCVs, the overall economic benefit was improved. Finally, a sensitivity analysis was carried out on the six parameters of daily electricity price, battery cost, fuel cell cost, carbon emission price, power grid carbon emission and hydrogen cost. It was concluded that FCV2G has good economic benefits and high development potential, and the economic benefits of FCV2G will continue to increase with the passage of time.

Developing a methodology for the ex-post assessment of Building Energy Efficiency Special Planning in Beijing during the 12th Five-Year Plan” period

In order to carry out an ex-post assessment of the completed Building Energy Efficiency Special Planning, a comprehensive ex-post assessment methodology is established. The methodology sets out the ex-post assessment criteria from three dimensions and 27 indexes, and apply the Fuzzy Synthetic Evaluation Method to build the ex-post assessment model. Applying the methodology, this research assesses the effectiveness of the Building Energy Efficiency Special Planning in Beijing during the “12th Five-Year Plan” period. The results show that it is generally effective in terms of realizing the main planning targets, reaching the desired development level of Building Energy Efficiency and ensuring the facilitating mechanisms to be in place. To be specific, substantial increase of the proportion of energy efficient buildings and considerable effects of energy saving and carbon emission reduction have been achieved, whilst the electricity consumption per unit area in small and median-sized commercial buildings being increased. The research further identifies the main barriers to the implementation of building energy efficiency program includes high electricity consumption of large-scale commercial buildings, insufficient data information, slow uptake of modern information technology, lack of market participation and lack of wider social recognition. Based on the assessment result and subsequent analysis, commensurate policy recommendations were proposed. This research was the first attempt examining the implementation effects of BEE Special Planning in Beijing.

Forecasting performance comparison of two hybrid machine learning models for cooling load of a large-scale commercial building

The hourly cooling load forecasting of a commercial building is very hard to be guaranteed with high accuracy. Due to the high cost of purchase, installation and maintenance for data acquisition devices in some cases, the forecasting method for univariate time series with nonlinear, random, large fluctuation is investigated in this paper and two hybrid machine learning modelling methods – Chaos–support vector regression (Chaos-SVR) and wavelet decomposition (WD) – SVR are presented. The optimization methods of the lag time and embedding dimension during reconstruction of the phase space are described in detail for Chaos–SVR while the selection processes for the wavelet-based function are also presented for wavelet decomposition –SVR. The prediction accuracy of these two hybrid forecasting methods are compared with backward propagation and SVR according to various evaluation metrics such as the expected error percentage(EEP), mean bias error(MBR), coefficient of variation of root mean square deviation(CV-RMSE), mean absolute percentage error (MAPE) and R2 determination coefficient. The forecasting results show that the Chaos–SVR modelling method outperforms the WD-SVR while EEP of Chaos-SVR is 7.4% better than WD-SVR– because of the chaotic characteristics of the cooling load for commercial buildings, and both of the hybrid algorithms outperform the single prediction algorithms, BP and SVR whose EEPs are at least 60% better than that of the other single ones. Although hybrid machine learning algorithms involve more complex modelling procedures than single prediction algorithms, their prediction times are still less than the single ones. The hybrid forecasting methods proposed in this work could also be used in other forecasting fields.

Hybrid environmental-media facades: rationale and feasibility

ABSTRACTIt has been demonstrated that kinetic sunscreens linked to intelligent building systems have the potential to improve the energy performance of large-scale public and commercial buildings. However, there has been minimal uptake, primarily due to the capital and maintenance costs. This study proposes that costs can be offset through adding value by repurposing the sunscreening system as a low-resolution media screen. To evaluate the feasibility of this hybrid environmental-media facade concept, physical prototypes has developed and calibrated to real-time simulation and control software; and two case studies were undertaken to assess the physical and economic feasibility. This article introduces the rationale for a hybrid façade, documents the prototypes and case studies, and outlines further work on control systems for a truly smart building façade.

Benchmarking energy performance for cooling in large commercial buildings

Urban development rapidly increases the total area and the energy consumption of large commercial buildings. Accordingly, the total energy consumption is the focus of numerous energy saving studies. An optimal benchmarking system provides a recommended energy consumption level for each building as well as identifying and prioritizing challenges to energy saving in each building. Based on detailed sub-metering system data and building operational data, this paper presents a simplified energy consumption benchmarking method for air-conditioning systems that cool large commercial buildings. Firstly, a multi-level benchmarking index system is established. Next, the energy performance data of eight large shopping centers in China validates the simplified benchmarking method and finally, the energy performance data of the participating shopping centers is analyzed under the present method. Our data focus on the energy saving potential, as well as informing improvement strategies for building energy performance, that can be used for efficient energy benchmarking process for cooling in large-scale commercial buildings.

Emission of combustion gases during fires of large-scale commercial buildings and its impact on environment

The analysis of the large-scale commercial buildings fires was conducted. The study presents summary of large-scale fires in Mazovian Voivodship in 2015-2016 and its impact on environment. Location and dates of large-scale fires were compared with historical data of air monitoring available on State Inspectorate for Environmental Protection database. Impact of wildfires on environment was already described widely in literature, this approach is focused on commercial buildings fires. The substantial peaks were noticed at Inspectorate for Environmental Protection research stations when large-scale fires occurred, especially regarding fires with long duration. Large fires with short duration were not reflected significantly in air monitoring database.

Influence Factors on Outdoor Visual Landscape Satisfaction of Large-Scale Commercial Buildings in Winter City

The quality of outdoor visual landscape of large-scale commercial buildings in winter cities needs to be improved urgently, and residents’ evaluation opinions are the important basis for countermeasures. In this paper, visual landscape images of large-scale commercial buildings were collected and were analyzed by Segnet decoder to obtain the proportion of each image. A questionnaire survey was also conducted on the satisfaction of residents' visual landscape based on greening, neatness, building

Fire risk assessment for large-scale commercial buildings based on structure entropy weight method

Based on the fire characteristic and the maintenance of fire equipment in the large-scale commercial buildings, this paper proposed a fire risk assessment system for large-scale commercial buildings. The assessment system focus on evaluating the safety performance of the fire protection system in the buildings. Firstly, the index system of the assessment system was determined by on-site investigation and experts' suggestions. Secondly, the weight of each index was determined by the structure entropy weight method. Thirdly, the score rules were determined according to the regulations from the relevant laws and design codes. Finally, the score and the corresponding fire risk level could be obtained. Moreover, four large-scale commercial buildings in Chongqing, China have been taken as examples to calculate the values of the fire risk level by the proposed assessment system. In addition, the fire safety performance of the four buildings were analyzed.

Developing a performance evaluation scheme for engineering facilities in commercial buildings: state-of-the-art review

Various post-occupancy evaluation schemes have been introduced for assessing building performance but one tailored for large-scale commercial buildings remains to be seen. Intended to develop a scheme for evaluating the performance of engineering facilities in existing commercial buildings, a multi-stage study was carried out in a dense-built metropolis – Hong Kong. Reported here is the part of work based on an extensive literature review. Considering the characteristics of relevant evaluation schemes, the requirements for useful performance evaluation and the criteria for selecting key performance indicators (KPIs), an integrated process-hierarchy model was formed for identifying applicable indicators for the intended scheme. A total of 71 indicators, classified into five categories: (i) physical, (ii) financial, (iii) task and equipment related, (iv) environmental, and (v) health, safety and legal, were identified. Their representations and their empirical applications, which are helpful for the strategic management of existing buildings, are also described.

Research on the Simulation and Operation Control Strategy of Hybrid Ground Source Heat Pump System

Large-scale commercial buildings served by ground source heat pump (GSHP) systems generally require a large land area for ground heat exchanger (GHE) installation. However it is difficult to provide such large area around the buildings, especially in urban area. Additionally, a large-scale GHE may consume more energy consumption compared with a medium or small GHE system because of the significantly large transmission energy consumption in the closed GHE loop. This study takes a commercial building located in northern China as a case study and proposes three different design schemes: chillers plus municipal central heating system, a single GHSP system and a hybrid GSHP system with chiller and municipal central heating as supplemental heat source and sink. The simulation models of the three systems have been established in the platform of TRNSYS. An optimal control strategy has been found with the aim of lowest annual operating cost. The results show that the municipal heating system can be put in operation when the outlet water temperature from GHE is less than 4℃ in winter; the cooling tower would be open to assist heat dissipation when the outlet water temperature from GHE is above 33℃ in summer. In this case the GSHP can provide 42% of the cooling and 83.5% of the heating loads, respectively.