Building Equipment Research Articles

Machine Learning-Based Automated Fault Detection and Diagnostics in Building Systems

Automated fault detection and diagnostics analysis in commercial building systems using machine learning (ML) can improve the building’s efficiency and conserve energy costs from inefficient equipment operation. However, ML can be challenging to implement in existing systems due to a lack of common data standards and because of a lack of building operators trained in ML techniques. Additionally, results from ML procedures can be complicated for untrained users to interpret. Boolean rule-based analysis is standard in current automated fault detection and diagnostics (AFDD) solutions but limits analysis to the rules defined and calibrated by energy engineers. Boolean rule-based analysis and ML can be combined to create an effective fault detection and diagnostics (FDD) tool. Three examples of ML’s advantages over rule-based analysis are explored by analyzing functional building equipment. ML can detect long-term faults in the system caused by a lack of system maintenance. It can also detect faults in system components with incomplete sets of sensors by modeling expected system operations and by making comparisons to actual system operations. An example of ML detecting a failure in a building is shown along with a demonstration of the soft decision boundaries of ML-based FDD compared to Boolean rule-based FDD analysis. The results from the three examples are used to demonstrate the strengths and weaknesses of using ML for AFDD analysis.

BIM-based method for post-earthquake medical functionality assessment of hospital departments considering functional coupling

Hospitals play an important role in post-earthquake emergency response and recovery, and the assessment of post-earthquake medical functionality is important for ensuring hospital functionality after earthquakes. Such assessments of hospital departments currently face the following challenges: (1) lack of unified modeling rules and integrated platforms to manage the associated data; (2) the complex functional coupling relationships between different components and medical equipment are not often considered; (3) lack of unified communication platforms, which hinders collaboration among researchers from different fields. Therefore, this paper proposes a BIM-based method for post-earthquake medical functionality assessment (PEMFA) of hospital departments considering functional coupling. Specifically, first, multi-level of detail (LOD) BIM modeling rules for structural components, nonstructural components, and medical equipment oriented towards PEMFA are suggested. Thereafter, a conversion method for the medical functionality assessment model and PEMFA method based on BIM are proposed. Subsequently, the PEMFA method for departments is proposed, emphasizing functional coupling. Finally, a typical medical building is selected as an example to demonstrate the proposed method. The results indicate the following: (1) the fragility data for various components and equipment of medical buildings can be integrated using the proposed multi-LOD modeling rules; (2) the mapping relationship between medical functional logic and BIM has been established, through which the functional coupling relationship between different components and equipment can be fully considered, thus providing an important method for the PEMFA of hospital departments and displaying results.

Designing a Resilient Building Maintenance Program: Integrating Preventive and Predictive Maintenance at the U.S. Navy

ABSTRACT Building equipment failure can have drastic effects on a company's operations and budget. This paper presents two types of maintenance approaches that if done effectively, can prevent or significantly reduce the failure of building equipment assets. The first is traditional time-based preventive maintenance (PM), which conducts prefailure inspections and tasks in a cyclic time-based approach. The second, is predictive maintenance (PdM), which conducts maintenance functions based on the condition of the equipment found through continuous or cyclic measurements and analysis during machine operation. The purpose of investigating these maintenance approaches is to determine whether we can improve on the U.S. Navy's (Navy) existing facility maintenance program, helping to reduce overall costs while improving sustainability, equipment resiliency, and efficiency. By presenting each maintenance program and leveraging today's technologies we show that these advancements in technology can directly improve the Navy's operational mission and warfighter readiness. Research was conducted through the following methods: interviews, books, third party reports, journal articles, industry websites and articles that focus on equipment maintenance. The Navy, the National Aeronautics and Space Administration (NASA), and the University of Washington provided case studies, existing facility/utility maintenance data, and budget information used in this research. The results show that PdM approaches using advanced analytics are more effective in diagnosing equipment, prescribing equipment problems, and predicting equipment failure. It will also show that when a PdM model is used, building tenants have less operational impacts as equipment operates longer with less downtime between maintenance events. By changing to a PdM program, facility managers and owners can improve asset efficiency and resilience, directly improving environmental sustainability and lowering overall longterm costs. It highlights the significant capital costs of a fully online PdM program and the benefits of using a hybrid model of PM and PdM. This research concludes with an overview of how building maintenance is currently being conducted on Navy bases by Naval Facilities Engineering Systems Command (NAVFAC), and how they are transitioning to a more sustainable maintenance program leveraging existing advanced metering infrastructure (AMI), building control systems (BCS), and utility control systems (UCS) with Smart Grid (SG), OSI Pi, and advanced analytics. In addition, major gaps in this transition are identified, and solutions are proposed to optimize these building system investments.

Pre-Feasibility Evaluation of the Eco-Cooler Model Made of Waste for Hot Humid Climates

Based on the results of a survey conducted by the Ministry of Energy and Mineral Resources in 2018 on commercial buildings, the largest energy-using equipment of each commercial building is air conditioning equipment with an average energy use of over 62%. Therefore, energy-saving efforts related to space cooling will have a significant impact on energy-saving efforts in the world. One way is to use the wind catcher method, namely Eco-Cooler. On the other hand, based on data from the National Waste Management Information System in 2021, waste generation in Indonesia reached 30,881,803.15 tons per year. So this research aims to find the lightest eco-cooler material that can withstand the weather using composite materials from waste. This research uses an experimental method by making an eco-cooler prototype using processed waste materials such as wood powder, corn cobs, plastic, and sand. Then 2 types of sizes, namely the outlet diameter (D1A) of 40 cm, and the inlet diameter (D2A) of 20 cm, while the second size is the outlet diameter (D1B) is 30 cm, and the inlet diameter (D2B) is 15 cm. After molding, the eco-cooler was then weighed and observed for a month to see its resistance to weather where the weather at that time was quite fluctuating. The parameters tested in this study were material weight and weather strength in the humid tropical climate. From the results of this study, it is found that the eco-cooler with sawdust material has the lightest weight of 16 kg for a diameter of 40 cm and 15 kg for a diameter of 30 cm. Meanwhile, the heaviest material is sand with a weight of 29 kg for a diameter of 40 cm and 23 kg for a diameter of 30 cm. In terms of durability, sawdust and sand are the most resistant to weather. Therefore, it can be concluded that the lightest and most weather-resistant eco-cooler material for hot humid climates is eco-cooler from wood powder material.

Labelled dataset for Ultra-Low Temperature Freezer to aid dynamic modelling & fault detection and diagnostics

Ultra-low temperature (ULT) freezers are used to store perishable biological contents and are among the most energy-intensive equipment in laboratory buildings, biobanks, and similar settings. To ensure reliable and efficient operation, it is essential to implement data-driven fault detection and diagnostic algorithms, along with energy optimization techniques. This study presents labelled and long-term ULT-freezer performance dataset, the first of its kind, derived from 53 ULT freezers featuring two different control strategies. The dataset comprises high-resolution historical operation data spanning up to 10 years. More than 10 attributes are recorded from the freezing chamber and critical locations in the refrigeration systems. The dataset is labelled with regular events, such as door openings, as well as fault events obtained from 46 service reports. A scalable data pipeline, consisting of extraction, transformation, and loading processes, is developed to convert the raw data into a format ready for analysis. The dataset can be utilized to support the development of data-driven models and algorithms that advance the intelligent digital operation of ULT freezers.

Современное состояние и перспективы развития систем предиктивной аналитики в области строительства

Introduction. The roots of modern predictive analytics go back to the 1940s, when governments began using the first computational models: Monte Carlo, neural network computational models, and linear programming. In the 1960s, corporations and research institutions began the era of computer-assisted commercialization of analytics. Then in the 1970s - 1990s it became more widespread in organizations. Tech startups have made Prescriptive Analytics and real-time analytics a reality.
 Materials and methods. Data from open sources was used. The subject of the study is the history, current state of predictive analytics systems and the prospects of the developed methodology for analyzing big data in order to predict changes in the stages of the life cycle of elements of engineering equipment of buildings and structures. The preparation and visualization of information was carried out using Microsoft Office Excel.
 Results. The terms, history of appearance, development and current state of predictive analytics systems are studied. The perspectives developed in the dissertation work, the methodology for analyzing big data in order to predict changes in the stages of the life cycle of elements of engineering equipment of buildings and structures, have been studied. The use of “Shewhart Control Charts”, methods of cluster and qualimetric analysis in scenarios unusual for them in the dissertation work allows us to count on the positive prospects of the developed methodology.
 Conclusions. Predictive analytics in the construction field is one of the most promising areas of big data analysis. The big data analysis technique developed in the dissertation in order to predict changes in the stages of the life cycle of elements of engineering equipment of buildings and structures is based on the use of modern algorithms.
 The scientific novelty lies in the approach to analysis, which uses a combined scheme in which cluster and qualimetric methods of analysis are used to search for equipment elements close to changing the stage of the life cycle.

A study on how to apply improvements to standards for measurement elevator noise in high-rise residential buildings

Elevators are essential equipment in high-rise buildings. In addition to residential buildings, elevator noise that can occur in high-rise buildings such as hotels, hospitals, and offices affects residants as noise. ISO 16032 is the current international standard for elevator noise measurement methods. Through many previous studies, the elevator noise measurement method was continuously studied. In particular, problems that are difficult to apply to high-rise buildings, in the case of high-rise apartments in Korea, have been verified. In order to solve these problems, previous studies have set items that need improvement. In addition, improvement plans were proposed through various experiments in apartment houses in Korea. In this study, I try to determine whether the improvement plan can be appropriately applied to the improvement of the current standard, and to find out what needs to be discussed in future research.

Microvibration Control of Hybrid Platform for High-Tech Equipment in Buildings Using Brain Emotional Learning-Based Intelligent Controller

In the semiconductor industry, the vulnerability of high-tech facilities installed on platforms to ground excitations induced by nearby traffic is significantly pronounced, primarily due to their small-scale dimensions. Consequently, it is imperative to design a smart control technique by effectively utilizing model-free controllers. Recently, adaptive intelligent control algorithms have emerged as a viable alternative to conventional model-based control algorithms. To address this issue, this study meticulously designed a hybrid platform using the adaptive intelligent controller known as the brain emotional learning-based intelligent controller, along with a Sugano fuzzy inference system to effectively optimize the controller’s learning parameters. These learning and intelligent-based algorithms offer notable advantages, including the ability to handle nonlinearity, uncertainty, and training capabilities within the control systems. To assess the effectiveness of the proposed controller in mitigating vibrations induced by traffic on high-tech facilities, a three degree-of-freedom structure is employed along with the hybrid platform. Finally, the performance of the hybrid platform in terms of microvibration control levels is meticulously validated using the Bolt Beranek Newman vibration criteria. Simulation results unequivocally demonstrate that the proposed controller outperforms both an uncontrolled system and a traditional linear quadratic regulator controller in terms of reducing the traffic-induced response of the hybrid platform and second floor, respectively. Through the integration of learning and intelligent-based controllers, the velocity levels of both the hybrid platform and the second floor are reduced to approximately 49.02% and remain well within the acceptable standard criteria curves.

Capacity determination of renewable energy systems, electricity storage, and heat storage in grid-interactive buildings

If renewable energy systems, electricity storage equipment, and heat storage equipment do not have a reasonable capacity, then a large amount of energy will be wasted even if the supply and demand sides have rational energy scheduling, affecting the safe and stable operation of a power grid. The rational allocation of energy storage equipment and renewable energy systems can significantly improve the power flexibility potential of buildings, save equipment costs, and prevent the occurrence of light and electricity abandonment. In this study, the flexible allocation strategy model proposed in previous studies is modified to determine the reasonable capacity of renewable energy systems, electricity storage equipment, and heat storage equipment in grid-interactive buildings. The modified capacity determination model can also be used to determine the operation strategy of each flexible source under reasonable capacity. Under extreme weather conditions, the upper and lower limits of the power stability coefficients of various cases in this study are 110 % and 94 %, respectively, and the frequency of the extremum is relatively low. The power and thermal flexibility coefficients during the peak period of electricity consumption are both 1, indicating that the flexible potential and utilization are reasonable.

Don State Technical University Experience of Enhancing Vocational Training of the Specialists in Organisation of Construction

Introduction. Professional requirements set for the specialists in organisation of construction (job foremasters and foremen) grow from year to year along with the emergence of new building technologies, materials, machines and equipment. After implementation of the new professional standards in our country, such specialists are urged to acquire the additional knowledge, abilities and skills necessary for fulfilling their job functions and activities in compliance with their proficiency level. Therefore, searching the ways of enhancing the vocational training of the specialists in organisation of construction has become the objective of the authors’ research.Materials and Methods. To achieve this objective, the authors have developed a series of additional activities for teaching the specialists in organisation of construction, based on improving the competencies required in their profession. This series of activities includes the initial, intermediate and final stages of vocational training. The initial stage is implemented during the first three years of bachelor and long first degree (specialist) programmes within direction of study “Construction” and the remaining two stages – during the last years of university study or after graduation.Results. The article presents the Don State Technical University experience of enhancing vocational training of the specialists in organisation of construction. The article contains the information on integration into the educational process of the photo and video tests, construction machines simulators, business games (including the ones on erecting the building structures out of the crane operator’s field-of-view), thematic references on difficult (challenging) issues of construction production, glossary of construction slang, as well as the information on organisation of the student scientific circle “Student construction control”.Discussion and Conclusion. The experience of enhancing vocational training of the specialists in organisation of construction accumulated by the authors is successfully implemented at the Continuing Education Department and the Production Technology in Construction Chair of Don State Technical University. It can be recommended for implementation in other universities where students of the direction of study “Construction” are educated.

Cluster analysis of energy consumption mix in the Japanese residential sector

Sector integration is one of the major components considered when designing future smart energy systems. Due to the lack of data, current partitioning of consumers into residential, commercial, industrial, and transportation sectors is based on assumptions on their respective energy demand. In reality though, there is a diversity in preferences and behaviors so that consumers from the same sector may have different demand and consumers from different sectors may have similar demand. With the increasing amount of individual data, it is becoming possible to study this diversity and more accurately partition consumers using advanced analysis techniques such as clustering. However, while this approach does allow for an accurate grouping, the complex mechanisms at the roots of identified clusters are still unclear. Indeed, energy demand depends on multiple factors such as economic, political, cultural, social, or historical besides environmental conditions. The present study uses households' data provided by the Japanese Ministry of the Environment with the objective of finding patterns associated with residential energy consumption profiles. It is found that the probability distribution of households' energy consumption seems to be log-normal so that clusters are revealed by first applying a logarithmic nonlinear transformation. Furthermore, k-means clustering, which is commonly used in energy systems study, fails here to correctly identify the clusters when compared with density-based clustering. After identifying clusters, we look for statistically significant specificities in the corresponding households' data such as their geographical location, number of residents, income, buildings' construction year, equipment and vehicles and suggest interpretations for each.

Identifying and prioritizing marketing strategies for the building energy management systems using a hybrid fuzzy MCDM technique

AbstractPreventing energy waste in residential and office buildings has emerged as a critical issue in both developed and developing countries over recent decades. The growing demand for oil and energy reserves has amplified the urgency of this concern. The deployment of building energy management systems (BEMSs) can lead to timely responses to changes in environmental conditions, the prevention of energy wastage, a reduction in CO2 emissions, and an increase in the longevity of building equipment. Despite the undeniable benefits of BEMSs, their market size remains small, creating challenges for providers in reaching potential customers. This research seeks to identify and prioritize the marketing strategies for BEMSs. A case study was conducted, employing the “Strengths, Weaknesses, Opportunities, and Threats” analysis as a tool for identifying marketing strategies related to BEMSs. This method resulted in the identification of 18 distinct marketing strategies. These strategies were subsequently prioritized using a novel fuzzy multicriteria decision‐making technique, VIkor‐topSIS, considering six specific criteria. The findings of the study suggested a hierarchical influence of six criteria on the BEMS market, arranged in the following order of significance: effectiveness, cost, attainability, complexity, timing, and popularity. Furthermore, the top three marketing strategies for BEMSs were found to be internet advertising strategies, discounts to consumers, and online sales. The analysis of the results has also offered valuable insights into the strengths and weaknesses of the studied BEMS provider, as well as the opportunities and threats present within the BEMS market.

Design of Health Healing Lighting in a Medical Center Based on Intelligent Lighting Control System

The intelligent lighting control system will unify the management of lighting equipment in outpatient buildings, inpatient buildings, and other buildings of the hospital, thereby improving the service quality of the hospital, providing a comfortable and relaxed working environment for medical staff, and providing a warm and comfortable treatment environment for patients. This project aims to develop a health rehabilitation environment lighting automation control system based on combining Zigbee Wide Ad Hoc Network and RFID (Radio Frequency Identification) technology. It can be customized according to the comfort needs of patients and can achieve lighting adjustments before the patient arrives. A PSO-based intelligent lighting control method has been proposed. To overcome the shortcomings of PSO such as being prone to local minima and premature convergence, a new PSO optimization method is proposed based on the inertia weight PSO and combined with genetic optimization theory. This method can not only learn experience from individuals in the group but also avoid the possibility of parent particles falling into local extremum. Compared with the original particle swarm optimization algorithm, the new particle swarm optimization algorithm can quickly find the optimal combination of lighting devices, improve computational efficiency by 6.208%, and also reduce the energy consumption of the calculated lighting devices, indicating the advantages of the new particle swarm optimization algorithm.

Preliminary Research on Water Supply, Drainage, and HVAC Equipment in Shanghai Historic Buildings During the Northern Warlord Period

During the Northern Warlord Period (1912–1928), the construction industry in Shanghai underwent robust development. As an integral element of buildings, equipment served both functional purposes and stood witness to the evolution of the economy and society, thus earning its place as part of the architectural heritage. However, due to various reasons, there are many loopholes in the protection of these building equipment. This paper examines the development of building equipment in Shanghai during the Northern Warlord Period, using water supply, drainage, and heating, ventilation, and air conditioning (HVAC) systems as examples. Through historical context analysis, it summarizes this development from a social-spatial perspective, infers the reasons behind it, and analyzes the importance of preserving such equipment, considering both past and present viewpoints. In this research, the importance of protecting historical building equipment is emphasized, which aims to give people a deeper understanding of their cultural value, and suggests that scholars conduct more practical research on their protection.

Where and how do people search for medical emergency equipment in public buildings?

Exsanguinating trauma is a common cause of death. Placing bleeding control kits in public areas has been suggested as a countermeasure. Similarly, automatic external defibrillators (AEDs) are placed in public areas in case of cardiac arrests. Both severe bleeding and cardiac arrests require rapid care and people must be able to quickly find relevant emergency equipment. This study explores where and how people search for such equipment. Twenty participants wearing eye-tracking glasses searched for bleeding control kits and AEDs in a public building. The participants visually searched features such as signs with maps, written information, and other emergency equipment. The participants expressed elevators and staircases, open areas, entrances, and the reception to be places where medical emergency equipment would likely be placed. The results suggest that these features and places may be suitable for medical emergency equipment or directions.

Sustainable energy transition and decision-making for enhancing the performance of building equipment in diverse climatic conditions

The utilization of renewable resources for energy generation holds paramount significance in ensuring substantial and enduring growth for every nation. Both developing and developed countries heavily rely on conventional energy sources like coal, petroleum, and natural gas in thermal power plants, resulting in significant environmental impact due to their emissions. This research culminates in the successful evaluation of alternative approaches to implement sustainable green structures using an innovative scientific technique. This research has effectively identified ten key challenges impeding operational efficiency in the construction industry. The application of the groundbreaking Analytical hierarchy process (AHP) technology facilitated the simultaneous ranking of the most optimal solutions. The precise assessment of numerous Energy Management equipment options for sustainable green buildings was achieved, highlighting the tool’s impartiality and neutrality in addressing Multiple Criteria Decision Making (MCDM) challenges. The ‘Roof-mounted Solar Photovoltaic (SPV)’ configuration consistently ranked first across all MCDM solution techniques examined in this study. This robust consensus underscores the significance of solar panels followed by biodiesel nanoparticles energy generation as the preferred choice. In conclusion, the impartiality of the tools used to address multi-criteria challenges in “greening building” projects empowers decision-makers within institutions to make informed investment choices. By considering the rankings generated by these tools, funding can be directed towards the most appropriate projects. Following prioritization, the alternatives are ranked as follows: Solar thermal setup > Installing sunshade devices > Light switches > Free cooling > Controlling the cooling & heating of spaces > Utilizing the chiller’s waste heat > Energy-efficient light bulbs > Electrical appliance energy efficiency ratings > Effective active space cooling solutions.

Experimental and numerical study on a precast partially connected buckling-restrained arc-opening steel plate shear wall

With the goal to decrease the detrimental bending moment effect of the steel plate shear wall (SPSW) on the frame, the frame's material waste and section requirements, and facilitate the arrangement of building equipment and pipelines, three one-third scale novel SPSW were designed and tested, including two four-sided partially connected buckling-restrained SPSW (PPBSW, PPBASW), and one non-buckling-restrained SPSW (PPASW). The damage pattern, hysteresis curve, stiffness degradation, energy dissipation capacity, and ductility were explored under consideration of arc openings and equipped with buckling-restrained concrete slabs. The outcomes shown that: The buckling-restrained concrete slab efficiently inhibited the low-order buckling of SPSW and enhanced the seismic performance of the steel shear wall. Compared to specimen PPASW, an increment in the bearing capacity for specimens PPBASW and PPBSW, was 55.4% and 61.3%, respectively. The corresponding initial stiffness increased by 41% and 43% due to the existence of the covered concrete slabs. There was a harmless difference in energy dissipation, secant stiffness, and bearing capacity for specimens PPBSW and PPBASW, indicating that the material's utilization and contribution had been improved. Moreover, finite element models (FEM) were established based on ABAQUS, showing a good fit between the test curves and simulation results, an error within 6.5%. Furthermore, a series of design parameters analysis for specimen PPBASW were carried out based on the established FEM. Following that, based on the test and simulation results, theoretical models for calculating the shear-bearing capacities of specimens PPASW, PPBAW, and PPBASW were developed, with the yield-bearing capacity and ultimate bearing capacities of PPBASW having maximum calculation errors of no more than 7.5% and 3.6%, respectively.

A method for sizing air source heat pump and electric boiler considering the peak and valley electricity prices

In a combined air source heat pump and electric boiler heating system, the capacity an oversized heat pump increases investment costs but decreases operation costs, and vice versa. Most current equipment selection methods are complex and ignore the impact of the peak valley price policy on system costs. This often leads to the selection of oversized equipment for the actual project, resulting in an increase in the total cost of the system. Therefore, we propose a new calculation method. The optimal number of non-guaranteed days for the air source heat pump (OGD) was used as the reference value for the calculation. A new optimization module was developed, and a system simulation model was built using Trnsys. The variation patterns of the OGD for 12 groups of buildings in Changchun, China, were analyzed. Finally, OGD recommendations were provided for 35 important cities in China. The results showed that OGD was not affected by changes in the building type or equipment installation costs. The average error of the annual cost calculation for different building types was 0.074%, and the maximum error was 1.7% when the equipment investment cost was changed. In addition, the OGD value was affected by a combination of outdoor temperature and local electricity prices. This value gradually decreased as the climate warmed. For regions with unfavorable price policies, such as Kashgar, China, the recommended local OGD value increased by 75.8%. The proposed method can be used as a methodological complement to the calculations of such systems during the design phase.

Energy-efficient Optimization of Life-cycle Costs Based on BIM

This article deals with a methodology for the economic development of energy-efficient buildings from an early planning or development phase based on building information modelling (BIM). In this context, both geometrically and energetically relevant parameters of a building are derived from a digital building model, already in the early phase of a project. The subsequent definition of building components for the building envelope and the performance of an energy demand calculation provides the basis for the selection of reference buildings suitable for the respective application. This enables the determination of practical costs, which include both annuity costs and total costs arising in the life cycle of the building for the cost groups of the building structures and the technical building equipment. By taking a holistic view of all costs and focusing specifically on energy efficiency, the methodology presented in this article can be used to identify both ecological and economic advantages for planning in the early stages of a project. By incorporating energy efficiency and economic efficiency, a sustainable and successful project can be achieved.

Optimization of Cost–Carbon Reduction–Technology Solution for Existing Office Parks Based on Genetic Algorithm

With limited investment costs, how to fully utilize the carbon-reduction capacity of a campus in terms of buildings, equipment, and energy is an important issue when realizing the low-carbon retrofit of office parks. To this end, this paper establishes a mathematical optimization model for the decarbonization-based retrofit of existing office parks, based on the genetic algorithm, taking into account the relationship between cost, energy-consumption, and carbon-emissions, and taking the maximum carbon reduction of the park over its whole life as the optimization goal. The validity of the model was verified in conjunction with a case study of an office park in Nanchang, China. The case study shows that, compared with current typical parks, the carbon reduction through an office park’s decarbonization retrofit has a non-linear correlation with the investment cost, and when the total investment cost of the park is above CNY 60 million, the increase in carbon reduction with the increase in the investment cost is gradually weakened, and the park achieves the maximum carbon reduction of 236,087 t when the investment cost reaches CNY 103 million. Under the current technical and economic conditions, the investment-cost–carbon-reduction benefits of different carbon-reduction technologies are different, the carbon-reduction benefit of increasing renewable energy utilization is the best, and the carbon-reduction benefit of upgrading the energy efficiency of the park’s supply-and-use system is lower than that of renewable energy utilization, but better than that of upgrading the performance of the building envelope system. In addition, the configuration of the parameters of the same low-carbon technology in different forms of buildings varies significantly, due to differences in the building form and daily use. The model established in this paper is able to give a comprehensive optimized building–equipment–energy configuration plan for existing office parks, when maximizing carbon reduction under different investment costs, which guides the park’s decarbonization retrofit.