Building Heating, Ventilation, And Air Conditioning Research Articles

Comparing the life cycle costs of a traditional and a smart HVAC control system for Australian office buildings

Many smart technologies have been introduced in buildings with the aim to reduce the energy and GHG emissions associated with their operation, particularly through improved control systems for regulating heating, ventilation and air conditioning (HVAC) equipment. Despite their energy saving potential, only a few studies have comprehensively assessed the costs associated with their practical implementation from a life cycle perspective. Accordingly, this study quantifies and compares the life cycle costs of a smart HVAC control system with that of a traditional control system, in the context of an Australian office building. For both systems, the required hardware are specified based on the characteristics of these systems and the layout of the serviced spaces in the reference building. The costs incurred over the period of assessment are quantified using the net present cost (NPC) approach. To evaluate the effects of these control systems on the operational energy costs of the building HVAC system, the control logics of both these systems are modelled through building energy simulations. The results show that, over the period of assessment, the smart control system incurred a higher total cost compared to the traditional control system. However, the findings from the simulations show that the HVAC energy cost savings achieved through the specification of the smart control system offset the additional cost incurred to deploy this system over the traditional control system. The smart control system resulted in HVAC operational cost savings between 9 % and 10 % compared to the traditional control system. Sensitivity analyses indicated that the total life cycle costs varied between −27 % and +50 %, with the discount rate and energy price increase rate being the most influential parameters.

Design Change and Operational Consideration of the HVAC System during Nuclear Power Plant Decommissioning

The heating, ventilation, and air conditioning (HVAC) system plays a crucial role in ensuring the safety of workers and preventing the release of gaseous radioactive materials into the environment during the decommissioning of a nuclear power plant (NPP). To establish an HVAC operation strategy, decommissioning phases were divided into four stages, and the HVAC systems were reclassified. In addition, assumptions have been made regarding design modifications and maintenance for the reactor containment building (RCB) HVAC, fuel handling building (FHB) HVAC, and main control room (MCR) HVAC. Based on these, for RCB HVAC operation, natural ventilation and RCB purge operation during the transition period are proposed. In the decommissioning stage, recirculation operation, entire ventilation operation consisting of continuous operation and purge operation, and finally partial ventilation operation to purify local space were proposed. Moreover, during the transition period, the FHB HVAC was proposed to operate as normal NPP, and the MCR HVAC was suggested to operate with safety-related equipment removed.

Modelling building HVAC control strategies using a deep reinforcement learning approach

Heating, ventilation, and air-conditioning (HVAC) systems are responsible for a considerable proportion of total building energy consumption but are also vital for improved indoor temperature comfort, indoor air quality and well-being of building occupants. Thus, developing control strategies for HVAC systems is critical for the total life cycle of any building projects. Particularly, HVAC and building operations are not stationary but are filled with fuelled by environmental dynamisms and unexpected disruptions such as users' activities, weather conditions, occupancy rate, and operation of machinery and systems. This research aims to develop and propose a strategic control learning framework for HVAC systems using the deep reinforcement learning (DRL) approach. The results show that the proposed Phasic Policy Gradient (PPG) based method is more adaptive to changes in real building's environments. Notably, PPG performs better and more reliable than the conventional method for HVAC control optimization with about 2-14% in energy consumption reduction and indoor temperature comfort enhancement, along with a 66% faster convergence rate. Overall, our findings demonstrate that our proposed DRL approach is less resource intensive and much easier than the conventional approach in deriving solutions for HVAC control optimization driven by energy efficiency and indoor temperature comfort.

Successful application of predictive information in deep reinforcement learning control: A case study based on an office building HVAC system

Reinforcement Learning (RL), a promising algorithm for the operational control of Heating, Ventilation, and Air Conditioning (HVAC) systems, has garnered considerable attention and applications. However, traditional RL algorithms typically do not incorporate predictive information for future scenarios, and only a limited number of studies have examined the enhancement and impact of predictive information on RL algorithms. To address the issue of coupling RL and predictive information in HVAC system operation optimization, we employed an open-source framework to examine the impact of various predictive information strategies on RL outcomes. We propose a joint gated recurrent unit (GRU)-RL algorithm to handle situations where a time-series exists in state space. The results from four classic test cases demonstrate that the proposed GRU-RL method can reduce operating costs by approximately 14.5% and increase comfort performance by 88.4% in indoor comfort control and cost-management tasks. Moreover, the GRU-RL method outperformed the conventional DRL method and was merely augmented with prediction information. In indoor temperature regulation, the GRU-RL algorithm improves control efficacy by 14.2% compared to models without predictive information and offers an approximately 5% improvement over traditional network models. Finally, all models were made open source for easy replication and further research.

The Sub-Metered HVAC Implemented for Demand Response Dataset

Abstract Closed-loop control of commercial building heating, ventilating, and air conditioning (HVAC) for demand response requires measurements used as feedback to the controllers. Demand response effectiveness is usually measured as a power deviation from baseline, but the building automation system (BAS) does not usually collect power measurements, and whole-building electric meters typically measure power at intervals of 15 min, which may be too slow for some types of demand response. Demand response strategies are sometimes focused on components of building HVAC systems, e.g., the response of supply/return fans to temperature set-point changes, but these components are usually not submetered. Fan power can be estimated from physics-based models leveraging BAS data, e.g., airflow measurements; but our ability to effectively close the loop on these estimates is not clear. In this paper, we introduce a massive dataset that contains both submetered fan power data and BAS data for several building HVAC systems during typical operation and demand response events. Through a case study we show that models leveraging BAS data alone do not provide accurate estimates of fan power during event transients, making it unlikely that closed-loop control of commercial building HVAC components for demand response would be effective using BAS data alone. This demonstrates the value of submetering HVAC components. More broadly, our dataset will enable future research bridging the gap between building control and power systems research.

Life cycle energy and greenhouse gas emissions of a traditional and a smart HVAC control system for Australian office buildings

In recent years, many novel smart technologies have been proposed to reduce the energy consumption and greenhouse gas (GHG) emissions attributed to the building sector. One such application of these technologies is the reduction of the Heating, Ventilation and Air Conditioning (HVAC) energy needs and GHG emissions using smart control systems. The environmental benefits of smart control systems during building operation have been explored in many studies, however, their embodied effects, associated with the extraction of raw materials, manufacturing and replacement are often overlooked. Accordingly, this study quantifies the life cycle energy needs and GHG emissions of a smart HVAC control system and assesses its potential for reducing the HVAC operational energy and GHG emissions in an Australian office building. A comparative assessment is performed, considering a traditional HVAC control system and an equivalent smart HVAC control system. The components of both the traditional and smart control systems are specified based on the characteristics of these systems as well as the layout of the serviced spaces in the reference building. The embodied energy and GHG emissions of both the traditional and smart control systems are quantified through a hybrid life cycle inventory (LCI) approach. To evaluate the effects of these control systems on the building HVAC operational energy, a building energy simulation is performed by applying the control logics of both systems. The results show that energy and GHG emissions savings in the HVAC operational offset the additional energy needs and GHG emissions of deploying the smart HVAC control system.

MF^2: Model-free reinforcement learning for modeling-free building HVAC control with data-driven environment construction in a residential building

Reinforcement Learning (RL) has advanced energy-efficient control of building Heating, Ventilation and Air Conditioning (HVAC) systems. Constructing a suitable RL environment for buildings is a crucial challenge. Compared to widely-used simulation-based environments, data-driven approaches offer higher training efficiency but face convergence difficulties due to influential factors, limiting their current application.To explore data-driven construction of RL environments for building HVAC systems, this study proposes two strategies for controlling room temperature setpoints in a residential building. XGBoost and Long Short-Term Memory Network (LSTM) are trained for energy consumption and room temperature prediction. One strategy predicts parameters for on-off states, while the other for power-on states. The XGBoost models are integrated into an OpenAI Gym environment. The first strategy achieves 0.8634 R2 and 0.2423 Root Mean Squared Error (RMSE) for energy consumption prediction. The R2 of room air temperature models are approximately 0.99 and the RMSE are lower than 0.31. The second strategy achieves 0.9181 R2 and 0.1042 RMSE for energy consumption prediction and similar performance for room temperature prediction. Deep Q-learning (DQN) and Deep Deterministic Policy Gradient (DDPG) algorithms are separately trained using these environments. Results show that the first strategy fails to induce the correct training of RL models, while the second strategy successfully induces a useable DDPG model for controlling building HVAC systems but fails to induce a useable DQN model. We analyze the reasons behind these observations. Compared to the original room temperature setpoint method, the DDPG-based HVAC control logic achieves a 10.06% energy-saving effect while ensuring comfort.

Distributed MPC-ILC Thermal Control Design for Large-Scale Multi-Zone Building HVAC System

With building heating, ventilation, and air conditioning (HVAC) systems accounting for 50% the energy consumption in the building sector in the United States, there is a need to develop and implement optimal control strategies for building HVAC systems that reduce energy consumption while achieving thermal comfort for users. In the author's previous work, an integrated model predictive control (MPC) and iterative learning control (ILC) design approach was presented that took advantage of both controllers. It did not rely on model accuracy compared to conventional MPC and reduced the learning curve compared to conventional ILC. Albeit the previous numerical results showed fast convergence in most of the VAV subsystems, the gap between room air temperature and its set point remains noticeable in several zones. This paper further proposes an approach to extend the centralized MPC-ILC controller to take into account the distributed factor and the spatial distribution of the thermal zones of the VAV system. The improved control strategy allows all VAVs to interact with each other and contribute collectively to the overall convergence of the whole system. The proposed controller is implemented on a thirty-two zone VAV reheat system and compared with different controllers, including our previous MPC-ILC design. The outcomes show that the proposed controller results in faster and closer convergence among all zones even when the number of subsystems is large.

Two-layer management of HVAC-based Multi-energy buildings under proactive demand response of Fast/Slow-charging EVs

Building heating, ventilation, and air conditioning (HVAC) and associated energy consumption make up the more and more important part of the world, whose reduction provides a cost-effective path to the “dual carbon” goal. This paper proposes a two-layer management of HVAC-based multi-energy buildings under proactive demand response of fast/slow-charging electric vehicles (EVs). In this paper, the building HVAC is mathematically formulated via a R-C thermodynamic model, which coordinates with multi-energy converters and storages to form a 100% renewable building. The building management is a challenging optimization problem due to its severe constraints and strong spatio-temporal couplings. In the first layer, a day-ahead multi-energy dispatch is formulated to economically optimize the electrical, heat, gas energy carriers. In the second layer, alternating current (AC) slow charging and direct current (DC) fast charging types of EVs are considered and managed via a novel real-time supply–demand pricing mechanism. After acquiring the economical dispatch references in the first layer, the second layer implements a model predictive control (MPC)-based real-time scheduling to handle the multi-energy supply–demand fluctuations. The original two-layer optimization is further handled via mixed-integer linear program (MILP) reformulation for high-efficient solving. Comparisons have shown the advantageous performances of the proposed two-layer optimization over economics and practicability. Simulations results show that the overall system operating cost can be reduced by at most 3.01% with a higher operational flexibility in building management.

A Control Framework to Enable a Commercial Building HVAC System for Energy and Regulation Market Signal Tracking

Commercial buildings are great demand response resources in the energy and regulation markets. Many commercial building heating, ventilation and air conditioning (HVAC) systems are composed of a chiller producing chilled water, and multiple Air-Handling Units (AHUs) distributing cooled air to thermal zones. Demand response of such systems has the potential to closely track energy and regulation market signals. However, existing control methods are mostly focused on fan control, neglecting the impact of the air loop fan control on the power consumption of water loop pumps and chillers. This paper presents a complete modeling of a commercial building HVAC system consisting of chiller, water pump and multiple AHUs, and develops a two-level control to follow five-minute energy market signals and four-second frequency regulation signals. The first-level coarse control provides commands for both water and air loop variables, the second-level fine control adjusts the fan commands while maintaining the water loop inputs within the five-minute control period. As a result, the water-loop power serves as a basis to track energy market signals and the air-loop fan control can be adjusted flexibly for frequency regulation. A high-fidelity model in Dymola is built to validate the model and control. The validation is conducted through co-simulation between Matlab and Dymola model via the Building Controls Virtual Test Bed (BCVTB). The simulation results show significant tracking improvement with the proposed two-level control framework.

Smart Filter Performance Monitoring System

Air filters are widely used in residential and industrial applications. It is designed to remove particulate pollutants in the air to supply cleaner air to either occupants or industrial equipment. Without air filters, the occupants might suffer from polluted air, and expensive industrial equipment could be damaged by contaminants. However, air filters are installed and operated with a limited performance monitoring system, and the efficacy of air filters is unknown after replacement. Here, we propose and prototype a smart filter performance monitoring system that costs less than 200 US dollars and can report filtration efficiency, differential pressure, temperature, and RH (relative humidity) in real time. Three case studies are presented: an air purifier, a teaching building HVAC (heating, ventilation, and air conditioning) system, and a large-scale air cleaning system. Applications of the proposed system cover from household to industrial, which not only verified the concept of the system but also proved its feasibility and advantages of the system. With the proposed system, residential customers can rest assured with their air purifiers or HVAC furnace filter; industrial customers can monitor the filtered air cleanness that will enter their internal combustion engines or gas turbines. Moreover, filter monitoring data can establish a database for researchers to validate the filter models or train a machine learning model for filter performance prediction. Filter or air-cleaning device manufacturers can improve their products or recommend suitable products to their customers based on big data contributed by this filter monitoring system.

Discrete-Time-Distributed Adaptive ILC With Nonrepetitive Uncertainties and Applications to Building HVAC Systems

Aiming to addressing the nonrepetitive uncertainties of multiagent systems, this work proposes a discrete-time-distributed adaptive iterative learning control (DDAILC) scheme for an output consensus problem, where two fundamental requirements in the traditional distributed iterative learning control (ILC) methods, i.e., the identical initial states and the repetitive desired trajectories, are removed. Furthermore, the algorithm design and analysis are directly aimed at discrete-time nonlinear multiagent systems, rather than continuous-time ones, to meet the needs of practical implementations. The iteration-varying trajectory of the virtual leader is included in the learning control protocol for a compensation. The adaptive parameter-updating law works along the iteration dimension by using a general consensus error that contains the output data of adjacent agents. To ensure the estimation of the control gain to be nonzero, a semisaturator is utilized in the parameter-updating law. The convergence of the output consensus is shown rigorously. Both numerical and practical examples are used to test the theoretical results. Moreover, the DDAILC efficiently improves performance of the building heating, ventilation, and air conditioning (HVAC) system by utilizing both the distributed topology and the repetitive dynamic characteristic.

Sensitivity of modeled residential fine particulate matter exposure to select building and source characteristics: A case study using public data in Boston, MA

Many techniques for estimating exposure to airborne contaminants do not account for building characteristics that can magnify contaminant contributions from indoor and outdoor sources. Building characteristics that influence exposure can be challenging to obtain at scale, but some may be incorporated into exposure assessments using public datasets. We present a methodology for using public datasets to generate housing models for a test cohort, and examined sensitivity of predicted fine particulate matter (PM2.5) exposures to selected building and source characteristics. We used addresses of a cohort of children with asthma and public tax assessor's data to guide selection of floorplans of US residences from a public database. This in turn guided generation of coupled multi-zone models (CONTAM and EnergyPlus) that estimated indoor PM2.5 exposure profiles. To examine sensitivity to model parameters, we varied building floors and floorplan, heating, ventilating and air-conditioning (HVAC) type, room or floor-level model resolution, and indoor source strength and schedule (for hypothesized gas stove cooking and tobacco smoking). Occupant time-activity and ambient pollutant levels were held constant. Our address matching methodology identified two multi-family house templates and one single-family house template that had similar characteristics to 60 % of test addresses. Exposure to infiltrated ambient PM2.5 was similar across selected building characteristics, HVAC types, and model resolutions (holding all else equal). By comparison, exposures to indoor-sourced PM2.5 were higher in the two multi-family residences than the single family residence (e.g., for cooking PM2.5 exposure, by 26 % and 47 % respectively) and were sensitive to HVAC type and model resolution. We derived the influence of building characteristics and HVAC type on PM2.5 exposure indoors using public data sources and coupled multi-zone models. With the important inclusion of individualized resident behavior data, similar housing modeling can be used to incorporate exposure variability in health studies of the indoor residential environment.

Building‐to‐grid optimal control of integrated MicroCSP and building HVAC system for optimal demand response services

AbstractThe world is shifting toward cleaner and more sustainable power generation to face the challenges of climate change. Renewable energy sources such as solar, wind, hydraulic are now the go‐to technologies for the new power generation system. However, these sources are highly intermittent and introduce uncertainty to the power grid which affects its frequency and voltage and could jeopardize its stable operations. The integration of micro‐scale concentrated solar power (MicroCSP) and thermal energy storage with the heating, ventilation, and air conditioning (HVAC) system gives the building greater leeway to control its loads which can allow it to support the power grid by providing demand response (DR) services. Indeed, the optimal control of the power flowing between the MicroCSP, the HVAC system, and the thermal zones can bring additional degrees of freedom to the building which can be relegated to the power grid based on the objective function and the incentives provided by the latter. This article presents an in‐depth investigation of the MicroCSP potential to provide ancillary services to the power grid. It focuses on evaluating the effect of incentives provided by the power grid on the building participation to the load following programs. It also demonstrates how the MicroCSP can help the building deal with constraints related to load peak shaving and ramp‐rate reduction set by the power grid as part of long‐term DR contracts. A sensitivity analysis is carried out to confront the results to prediction uncertainties of the energy prices and the weather conditions.

Impact of occupancy prediction models on building HVAC control system performance: Application of machine learning techniques

This paper analyzes the impact of using different types of occupancy prediction models on the performance of occupancy-based heating, ventilation, and air conditioning (HVAC) control systems in residential buildings. The future occupancy prediction problem is approached from two different viewpoints: 1) as a regression task for arrival-time prediction and 2) as a classification task for occupancy-state prediction. Four machine learning techniques, namely decision trees, k-nearest neighbors (kNN), multi-layer perceptron, and gated recurrent units, are implemented to predict future occupancy from each perspective. The performance of the occupancy models is evaluated in terms of mean absolute error (MAE), accuracy, and the ability to provide occupants’ thermal comfort and to save energy. An overall performance score is proposed by making a trade-off between the energy and thermal comfort objectives using the technique for order of preference by similarity to the ideal solution method. The results demonstrate that selecting the optimal viewpoint has a higher impact on the control performance than selecting the machine learning techniques, with occupancy-state prediction models providing superior performance in most cases. It is also shown that the machine learning evaluation metrics (i.e., MAE and accuracy) provide a weak to moderate correlation with the overall performance score. Consequently, relying solely on the MAE and accuracy might fail to provide a reliable evaluation of the occupancy model performance for use in HVAC control systems.

Knowledge Discovery by Analyzing the State of the Art of Data-Driven Fault Detection and Diagnostics of Building HVAC

The automated fault detection and diagnostics (AFDD) of heating, ventilation, and air conditioning (HVAC) using data mining and machine learning models have recently received substantial attention from researchers and practitioners. Various models have been developed over the years for AFDD of complete HVAC or its sub-systems. However, HVAC complexities, which partly have roots in its close coupling nature and interrelated dependencies, mean that understanding the relationship between faults and the suitability of the techniques remains an unanswered question. The literature analysis and interactive visualization of the data collected from the past implementation of AFDD models can provide useful insight to further explore this question by applying artificial intelligence (AI). Association rule mining (ARM) is deployed by this paper, using the frequent pattern (FP) growth algorithm to generate frequent fault sets for most common HVAC faults from the body of AFDD models developed in the literature to represent the status quo. A new model is developed for common HVAC faults and the techniques most frequently used to detect and diagnose them. A recommender system is developed using the ARM model to extract knowledge from the body of knowledge of HVAC data-driven AFDD in the form of rule-sets that reflect the associations. Findings of this review paper can significantly help civil and building engineers, as well as facility managers, in better management of building HVAC systems.

Graph-based algorithmic design and decision-making framework for district heating and cooling plant positioning and network planning

Although buildings are the single largest users of energy in cities, many individual building HVAC (heating, ventilation, and air conditioning) systems are energy inefficient. Sustainable district heating and cooling systems have been developed to address this inefficiency, however, the implementation of district heating and cooling systems is a complex, capital intensive multivariate problem. One critical engineering problem, the location of energy production plant and pipe network topology, has a major influence on the system performance as well as the life cycle cost, and therefore should be comprehensively studied and chosen. This paper proposes a novel algorithmic design and decision-making framework that uses multiple simultaneous criteria to generate and evaluate design alternatives of site selection and pipe network layout. The framework consists of three components: site location and topology generation, network sizing and evaluation, and multi-objective decision-making assistance. The proposed framework has been validated in a real-world district cooling greenfield project in China's metropolitan area. Results were compared against the engineers’ best practices. It has shown that the proposed framework could find equally good or better designs in engineering and financial performance with CAPEX reduction of up to 15%.

Effectiveness of portable air filtration on reducing indoor aerosol transmission: preclinical observational trials

BackgroundWhile the range of possible transmission pathways of severe acute respiratory syndrome coronavirus-2 in various settings has been investigated thoroughly, most authorities have recently acknowledged the role of aerosol spread in its transmission, especially in indoor environments where ventilation is poor. Engineering controls are needed to mitigate aerosol transmission in high-risk settings including hospital wards, classrooms and offices.AimTo assess the effectiveness of aerosol filtration by portable air cleaning devices with high-efficiency particulate air filters used in addition to a standard building heating ventilation and air conditioning (HVAC) system.MethodsTest rooms, including a single-bed hospital room, were filled with test aerosol to simulate aerosol movement. Aerosol counts were measured over time with various portable air cleaning devices and room ventilation systems to quantify the overall aerosol clearance rate.FindingsPortable air cleaning devices were very effective for removal of aerosols. The aerosols were cleared five times faster in a small control room with portable air cleaning devices than in the room with HVAC alone. The single-bed hospital room had an excellent ventilation rate (∼14 air changes per hour) and cleared the aerosols in 20 min. However, with the addition of two air cleaning devices, the clearance time was three times faster.ConclusionsInexpensive portable air cleaning devices should be considered for small and enclosed spaces in healthcare settings, such as inpatient rooms and personal protective equipment donning/doffing stations. Portable air cleaning devices are particularly important where there is limited ability to reduce aerosol transmission with building HVAC ventilation.

Genetic and Swarm Algorithms for Optimizing the Control of Building HVAC Systems Using Real Data: A Comparative Study

Buildings consume a considerable amount of electrical energy, the Heating, Ventilation, and Air Conditioning (HVAC) system being the most demanding. Saving energy and maintaining comfort still challenge scientists as they conflict. The control of HVAC systems can be improved by modeling their behavior, which is nonlinear, complex, and dynamic and works in uncertain contexts. Scientific literature shows that Soft Computing techniques require fewer computing resources but at the expense of some controlled accuracy loss. Metaheuristics-search-based algorithms show positive results, although further research will be necessary to resolve new challenging multi-objective optimization problems. This article compares the performance of selected genetic and swarm-intelligence-based algorithms with the aim of discerning their capabilities in the field of smart buildings. MOGA, NSGA-II/III, OMOPSO, SMPSO, and Random Search, as benchmarking, are compared in hypervolume, generational distance, ε-indicator, and execution time. Real data from the Building Management System of Teatro Real de Madrid have been used to train a data model used for the multiple objective calculations. The novelty brought by the analysis of the different proposed dynamic optimization algorithms in the transient time of an HVAC system also includes the addition, to the conventional optimization objectives of comfort and energy efficiency, of the coefficient of performance, and of the rate of change in ambient temperature, aiming to extend the equipment lifecycle and minimize the overshooting effect when passing to the steady state. The optimization works impressively well in energy savings, although the results must be balanced with other real considerations, such as realistic constraints on chillers’ operational capacity. The intuitive visualization of the performance of the two families of algorithms in a real multi-HVAC system increases the novelty of this proposal.

BIM-based green building evaluation and optimization: A case study

Green building has drawn worldwide attention due to the adverse impact of construction on the environment. This research presents a Building Information Modeling (BIM) based evaluation system for the performance assessment of green buildings. Combining BIM technology with green building analysis, the proposed approach can fully utilize the advantages of the BIM model and quickly conduct green building evaluation. A green building evaluation framework is constructed, which evaluates the green building performance from five aspects, i.e., the main building, the building envelope, the heating, ventilation and air conditioning (HVAC), the lighting and equipment, and extra points. A case study is performed to test the applicability and effectiveness of the proposed approach. Important findings are: (1) The performance of the target green building is determined as 0.87 (i.e., a fairly poor level), but it could be upgraded to 1.32 (i.e., a good level) with the consideration of extra points for the green building evaluation (2) Building renovation measures (i.e., improving the building envelop, HVAC, and lighting and equipment); could improve the performance of the green building by 31.5% on average. A synergetic impact exists among these renovation measures, where the impact of renovating all three aspects is greater than simply the sum of taking three individual measures; (3) Optimization of the green building performance could not only reduce the energy consumption but also create a more comfortable environment for the occupants, where severe conditions could be greatly reduced. The novelty of this research lies in (a) presenting a BIM-based model that is able to effectively perform the green building evaluation and optimization in a wide range and also incorporate the regional features as needed; (b) proposing a novel grading rule that makes influential factors under different standards measurable in a more intuitive manner.