Building Energy Efficiency Research Articles

Comparative Analysis of Energy Efficiency in Conventional, Modular, and 3D-Printing Construction Using Building Information Modeling and Multi-Criteria Decision-Making

Energy efficiency has become a crucial focus with the growing attention on sustainable development and decreasing energy consumption in the built environment. Different construction methods are being applied worldwide, such as conventional, modular, and 3D-printing methods, to increase energy efficiency in buildings. This study aims to enhance the decision-making process by identifying optimal construction techniques, material selection, and ventilation window dimensions to promote sustainable energy use in buildings. A novel framework combining Building Information Modeling (BIM), computational analysis, and Multi-Criteria Decision-Making (MCDM) approaches is applied to assess the energy use intensity (EUI), annual electric energy consumption, and lifecycle energy cost across multiple sequences for each type of construction. Computational analysis in this research is combined in two main tools. Minitab is utilized for experimental design to determine the number and configurations of sequences analyzed. The Simple Additive Weighting (SAW) method, applied as an MCDM tool, is used to assess and rank the performance of sequences based on equally weighted criteria. Subsequently, 3D models of case study buildings are developed, and energy simulations are conducted using Autodesk Revit and Autodesk Green Building Studio, respectively, as BIM tools to compare the energy performance of various design alternatives. The results revealed that 3D printing surpassed other methods, where Sequence 7 achieved approximately 10.3% higher efficiency than modular methods and 40.5% better performance than conventional methods in the evaluated criteria. The findings underscore the higher energy efficiency of 3D printing, followed by modular construction as a competitive method, while conventional methods lagged significantly.

Development of a camera-based device for real-time indoor illuminance distribution assessment using a deep-learning model

Real-time monitoring and assessment of indoor illuminance are crucial for maintaining environmental quality, visual comfort, and building energy efficiency. Traditional systems struggle with complex layouts, but integrating real-time monitoring with smart building management enhances comfort and sustainability. This study presents an innovative method combining a CMOS camera, data processing, and deep learning algorithms. The camera captures continuous images and extracts texture features affected by lighting. A machine learning model then predicts the illuminance distribution, generating detailed light maps in real-time. This novel sensing system, validated through experiments at the University of Skikda, provides a promising solution to improve light management by optimizing daylight use, reducing energy consumption, and enhancing visual comfort. It offers architects and facility managers a tool to integrate real-time data into building management systems for more sustainable indoor environments.

Enhancing Energy Efficiency in Mediterranean Coastal Buildings Through PCM Integration

In this paper, the impact of phase change material (PCM) integration into building envelopes is evaluated, considering a coastal mediterranean climate. PCM integration represents an innovative technology to lower construction’s heating and cooling loads. A case study was conducted on PCM integration into a Lebanese structure’s envelope where the thermal performance and energy effectiveness were assessed. A significant reduction was observed for both cooling and heating loads. Simulations revealed that the optimal PCMs were those whose melting temperatures were between 21 °C and 24 °C under the Mediterranean weather conditions, showing the importance of selecting the appropriate PCM to enhance energy savings. The annual energy savings calculated were between 11% and 13.4%. The study also emphasized the impact of the specified heating and cooling temperatures within the comfort range on the effectiveness of PCM integration. The results show that optimal PCM selection is a function of the weather conditions and indoor temperature settings.

Calibrated models for effective clustering: Discriminating operation schedules in occupied buildings

AbstractEuropean directives advocate for end-users to be aware of their energy consumption. However, individual energy monitoring tools, such as energy meters or cost allocators, are not always affordable or technically feasible to install. Therefore, the development of virtual tools that enable the study of energy consumption in existing buildings is necessary. Virtual sensors, particularly based on white-box models, offer the opportunity to recreate these behaviours. When calibrated with measured data, white-box models, which incorporate detailed building physics, become increasingly valuable for designing energy-efficient buildings. This research explores a novel approach to identifying building’s load period directly from energy data generated by these calibrated models. The volume of data generated by white-box models can be overwhelming for visual analysis, but the hypothesis here is that analysing this data through clustering techniques can reveal patterns related to occupant behaviour and operational schedules. By feeding indoor temperature data into the calibrated model and analysing the resulting energy outputs, the research proposes a method to identify the heating, ventilation and air conditioning (HVAC) system operation schedule, free oscillation periods and non-recurrent events. Validation is achieved by comparing the identified periods with actual measured data. This methodology enables the development of a virtual sensor for cost allocation, which minimises the need for physical sensor deployment while complying with European Union directives. The research not only demonstrates high accuracy but also the potential to outperform measured schedule. This suggests the ability of the method to identify missing sensor data or other factors affecting temperature curves, enabling fault detection and diagnostics (FDD). Consequently, this opens doors for setting optimised operation schedules that balance energy efficiency with occupant comfort.

Thermal and Mechanical Performances Optimization of Plaster–Polystyrene Bio-Composites for Building Applications

Polystyrene is renowned for its excellent thermal insulation due to its closed-cell structure that traps air and reduces heat conduction. This study aims to develop sustainable, energy-efficient building materials by enhancing the thermal and mechanical properties of plaster–polystyrene bio-composites. By incorporating varying amounts of polystyrene (5% to 25%) into plaster, our research investigates changes in thermal conductivity, thermal resistance, and mechanical properties such as Young’s modulus and maximum stress. Meticulous preparation of composite samples ensures consistency, with thermal and mechanical properties assessed using a thermal chamber and four-point bending and tensile tests. The results show that increasing the polystyrene content significantly improved thermal insulation and stiffness, though maximum stress decreased, indicating a trade-off between insulation and mechanical strength.

Impact of Building Orientation on Energy Performance of Residential Buildings in Various Cities Across Afghanistan

Building or solar orientation, a key architectural design parameter, significantly influences energy consumption in buildings. Optimizing building orientation to harness passive solar benefits is a fundamental and cost-effective measure in designing energy-efficient buildings. However, the optimal orientation varies based on geographical location, climatic conditions, and building type. Notably, Afghanistan’s building sector currently lacks tailored energy efficiency regulations. Therefore, this study investigates the impact of building orientation on the energy performance of residential buildings across nine cities in Afghanistan, each characterized by distinct climatic conditions and geographic locations, employing BEoptTM energy simulation software. The findings reveal diverse optimal orientations, dividing the country into three distinct climatic zones: subarctic (optimal orientation: south-southeast), continental (optimal orientation: south), and hot-arid (optimal orientation: north). The optimal orientations in these regions yield potential energy savings ranging from 25.6% to 48.9% compared to the least efficient orientations. These insights are critical for establishing location-specific building regulations in Afghanistan, promoting energy-efficient design, and addressing the country’s current trend of unsustainable development.

Energy-Efficient Buildings for Biosphere’s Sustainability

For decades now, the planet’s biosphere sustainability has been at stake due to human, social, economic and environmental factors that have negatively impacted our earth. With global warming and greenhouse gas emissions on the rise, there is every need to worry. Climate change today has a significant impact on almost every aspect of our environment, economies, societies and the planet’s biosphere which is under immense threat of extinction. The building sector is a key contributor to carbon dioxide emissions in the world today. Reducing the building sector's production of greenhouse gasses and other negative impacts to safer levels is a big challenge today and it must be met quickly and decisively. Luckily, there are many Information and Communications Technology (ICT) technologies that already exist to mitigate carbon dioxide emissions and adverse climatic change effects. The purpose of this research is to review the nexus of Internet of Things innovations to deliver Net Zero energy buildings (NZEBs) that can mitigate global warming for a sustainable biosphere. This will help achieve favourable energy efficiency for a sustainable world from the adverse climatic upheavals due to increased global warming. The specific objectives of the research are: i) To examine the gravity of emissions from non-energy efficient buildings and the extent to which they contribute to global warming; ii) To explore the components and capabilities of IoT technology and infrastructure that influence the design of internet of things; iii) To specify ways of integrating machine learning (ML) and artificial intelligence (AI) technology to reconstruct past climate events and improve future predictions. Data analysis was done at the National Construction Authority with a target population consisting of 350 technical and management staff. The research fronts a future of worldwide energy efficiency for a sustainable biosphere to be realized by mass implementation of the Internet of Things, M2M energy-efficient buildings technology

Efficiency multi-agent model assisted Moea/D algorithm for optimization design for building taking into account annual energy consumption and annual user discomfort hours

Recently, with the continuous consumption of energy, building energy conservation has been popular in the energy field. In response to the high computational cost, slow convergence speed, and low accuracy of existing optimization design methods for building energy efficiency, this study first built a multi-objective optimization model for building energy efficiency on the ground of the annual energy consumption of buildings and the quantity of uncomfortable hours for users. Then it introduces a multi-agent model auxiliary mechanism to improve the decomposition based multi-objective evolutionary optimization algorithm, and then solves the multi-objective optimization model for building energy efficiency. In order to select the optimal decision variable of the algorithm, the decision parameters were analyzed and found that the performance was optimal when the number of samples, aggregation number and base model were set to 25.3 and 20. The improved multi-objective evolutionary optimization algorithm on the ground of decomposition has average supervolume and running time values of 32416.13 and 1774.58 seconds under office buildings, and 7899.13 and 3616.96 seconds under residential buildings, respectively. In addition, the annual user discomfort time of office buildings is 555.28h, which is lower than other comparison algorithms. In summary, the optimal performance of the algorithm when the decision variable is set to 25.3 and 20. The algorithm proposed by the research institute has superior performance and has certain application value in selecting the optimal solution for building energy-saving design.

Passive energy saving through thermal retrofitting of existing government office building envelopes - Case study for office-research building in Cairo, Egypt

Egypt used several million kilowatts per hour of electrical power in 2023. According to 2022 data, public utilities and agencies used 15% of Egypt's power. Most of Cairo’s government buildings built in the 50th and the 60th of the last century are composed of reinforced concrete structure skeletons, flat concrete roofs, and burnt brick exterior and interior walls. At that time, building energy efficiency was not a priority. Thus, uninsulated building envelopes were built. Nowadays, renovating buildings requires increasing energy efficiency. Previous research showed that lack of thermal insulation in roofs, walls, window solar heat gain, and window frames thermal bridging increases heating and cooling costs. The current research aims to enhance existing buildings energy saving by improving thermal insulation of its envelope components. The existing research building in Cairo was assessed for environmental factors to accomplish the research goal. The research used a three-dimensional (3D) terrestrial laser scanner to assess building facades for creating an environmental study model. The research used the building office top floor construction as a comparative environmental simulation using (Design builder® version6 software) as a base case for yearly energy consumption due to heating and cooling loads, followed by improving the thermal insulation of each building element individually in the base environmental model and comparing its effect on annual energy saving percentage, then applying all of the best annual energy saving building envelope element thermal insulation retrofitting techniques to the improved building model. The research results showed that improving roof thermal insulation saves 33.5% of the yearly top floor total electrical energy, 12% for exterior walls, 0.75% for window glazing, and 1.56 % by retrofitting window frames. Applying all of the best energy-saving thermal insulation retrofit techniques to the whole structure saved 31% of annual energy consumption.

Towards effective implementation of building energy efficiency codes in Tripoli, Lebanon: Key actions for enforcement

Energy is vital for human life. However, the increasing global demand for and reliance on fossil fuels are significantly contributing to rising CO2 emissions. The Building Energy Efficiency Codes (BEECs) are a government initiative aimed at regulating CO2 emissions. BEECs support both national environmental goals and global climate change initiatives in Lebanon. The study aims to close the gap between the effectiveness of Lebanon's building energy codes and the country's environmental goals. It seeks to identify the key enforcement actions taken by the Trpl-M towards the successful implementation of the Building Energy Efficiency Code (BEEC). Tripoli was selected for its administrative independence and unique authority in enforcing building energy codes, including a dedicated technical department. This autonomy provides a valuable setting to explore compliance strategies and address regional challenges. This is achieved through an approach involving a literature review, case studies, and semi-structured interviews with employees from the Trpl-M Permit Office. The data is analyzed using a thematic analysis approach, providing a nuanced understanding of the implementation challenges within the employees' working environment. Findings suggest considering proactive design integration, third-party inspections, random compliance checks, penalties, and incentives to achieve high standards of energy efficiency and contribute to sustainable urban development and energy conservation in the region.

Design Optimization of Energy-Efficient Residential Buildings in Morocco

In this paper, an optimization-based analysis approach is presented to cost-effectively improve the energy efficiency of residential buildings in Morocco. This study introduces a unique focus on the Moroccan context, where a comprehensive application of energy efficiency optimization has not yet been undertaken. This analysis considers the interactive effects among various energy efficiency measures to determine optimal combinations for designing high-energy performance, as well as net-zero energy, residential buildings for six climate zones in Morocco. In particular, the design analysis approach combines a whole-building simulation with the sequential search technique, providing a novel, integrated cost–benefit analysis that minimizes lifecycle costs (LCC) while maximizing energy savings for each climate zone. This study also includes an unprecedented comparison of optimized designs, reference designs, and current Moroccan building regulations (RTCM), highlighting potential improvements to the existing regulatory framework. While the sequential search method has been applied elsewhere, its specific application to achieve net-zero energy homes in the Moroccan context with comparable LCC is a new contribution. The analysis results show that houses in Morocco can be cost-effectively designed to achieve annual energy savings of 51% for Zone 1, 53% for Zone 2, 60% for Zone 3, 67% for Zone 4, 54% for Zone 5, and 56% for Zone 6 compared to the current construction practices considered as reference designs. Moreover, the results indicate that houses can reach net-zero energy building designs with almost the same LCC as the reference design cases for all the climate zones in Morocco.

Improving Energy Efficiency of School Buildings: A Case Study of Thermal Insulation and Window Replacement Using Cost-Benefit Analysis and Energy Simulations

This study demonstrates the benefits of comprehensive school building (SB) energy efficiency (EE) improvements through building envelope renovations, lighting upgrades, and changes to cleaner heat sources. The parametric study in the building energy simulation software was used to check the application of various interventions on the energy consumption of existing SBs while reducing CO2 emissions with the most profitable return on investment (ROI). The energy savings from window replacements did not correspond with expectations. However, other measures such as the wall, roof insulation, and lighting modernization improved EE by up to 152 kWh/m2 and 41 kg/m2 CO2/m2 annually. The study also points to a significant trade-off between district heating (which reduces CO2 but has a slower ROI) and other heating solutions. The results suggest that climate-specific insulation thickness and glazing type needs are required, and optimal insulation strategies are shown to improve EE by 48–56% and CO2 reductions of 45–56%. Lighting replacement and biogas boiler use were both impactful. The findings support the importance of sustainable practices, which should stimulate educational awareness and environmental responsibility. This research presents actionable insights for EE and sustainable development from within educational facilities.

A regenerative architectural solution for decarbonizing the construction sector in Algeria: lightweight and low-carbon insulating mortar

The building sector is responsible for a significant portion of greenhouse gas (GHG) emissions, thereby exacerbating climate change. To meet the growing need to minimize carbon footprints and improve construction energy efficiency, the development of eco-friendly building materials is crucial. This research concentrates on the development and assessment of lightweight insulating low-carbon mortar. This material is regarded as a prospective solution for regenerative architecture in Algeria due to its advantageous properties. This research aims to explore the characteristics of this material within the context of regenerative architecture, assessing its potential to enhance thermal insulation and energy efficiency of buildings and reduce CO2 emissions. This approach aligns with the principles of the "Materials" petal of the Living Building Challenge (LBC) certification, promoting architecture that goes beyond sustainability to become regenerative. To this end, we proceeded to make samples, conduct testing and laboratory studies on several specimens of this material, and perform digital simulations using RETA software. This mortar, is composed of low-CO₂ cement and lightweight aggregates, providing notable advantages in thermal insulation, energy efficiency, and GHG emissions reduction. In this way, it is observed that the thermal performance of buildings is improved, with a potential reduction in thermal energy consumption of up to 20% and a decrease in GHG emissions of up to 40% compared to conventional mortars. These, allows us to conclude that this ecological mortar contributes in initiatives that promote energy efficiency and sustainable development, promoting a transition towards more environmentally respectful regenerative architecture in Algeria and globally.

Sustainable lightweight concrete with EPS beads and cork additives: a study on physico-mechanical properties of eco-friendly composites

This study investigates the properties of lightweight concrete composites incorporating expanded polystyrene (EPS) and cork as aggregates, focusing on their effects on density, thermal performance, and mechanical strength. The results show that using EPS and cork significantly reduces the density of the composites, making them suitable for applications where weight reduction is critical, such as in energy-efficient building materials. Additionally, the thermal conductivity of these composites decreases substantially, enhancing their insulating properties. However, the reduction in density is coupled with a significant decrease in compressive strength, with values up to 94% lower than conventional concrete. This limits the use of these lightweight composite materials to non-structural applications or situations where low load-bearing capacity is acceptable. The study also reveals an increase in porosity and capillary water absorption in the composites, which may compromise their durability, particularly in environments with high moisture exposure. In conclusion, lightweight concrete composites incorporating EPS and cork offer considerable advantages in terms of reduced weight and improved thermal insulation. However, for structural applications or where higher mechanical performance is required, adjustments such as the inclusion of reinforcing materials or reducing the lightweight aggregate content may be needed to ensure optimal performance and durability.

A Local‐Dissociation Solid‐State Polymer Electrolyte with Enhanced Li+ Transport for High‐Performance Dual‐Band Electrochromic Smart Windows

AbstractWindows through heat exchange play a vital role in energy saving for realizing a net‐zero carbon emission society. Dual‐band electrochromic (EC) smart windows by dynamically regulate visible (VIS) and near‐infrared (NIR) light are necessary for improving both building energy efficiency and habitant comfort. However, the rational design of high‐performance electrochromic devices (ECDs) suffers sluggish EC response due to the slow ion transport. In this work, a locally dissociated Li+ concept is proposed to construct a solid‐state polymer electrolyte (SPE) with ultrafast Li+ transport. The succinonitrile (SN) is employed to loosen the Li+‐anion pair and the crystallographic C─O chain in the poly(ethylene glycol) methyl ether methacrylate (PEGMA) electrolyte by its strong solvation capability. The as‐prepared SPE shows a high ionic conductivity of 6.48 mS cm−1 at 30 °C and a high transmittance of >90%. The SPE‐based EC smart windows exhibited a fast switching speed (3.0/3.2 s for coloration/bleaching), a high coloration efficiency (CE) of 373.8 cm2 C−1, and a high optical modulation in the full solar spectrum (85%, 70%, 43% at 673, 1200, and 1600 nm, respectively). Finally, the SPE‐based EC smart windows shows three working modes with a temperature regulation range of 19.1 °C, exhibiting great potential in practical application.

Analysis of the indoor wind environment in buildings on the Qinghai-Tibet plateau of China: A case study of the Dege Scripture Printing House

The Dege Scripture Printing House (DSPH), built in 1729, is located on the southeastern edge of the Qinghai-Tibet plateau and serves as a significant center for Tibetan Buddhism culture. Compared to typical Tibetan architecture, DSPH features unique characteristics, such as its substantial size, diverse structures, and adaptability to the local climate. The surrounding topography of DSPH primarily comprises plateaus and hilly plains, and the region experiences a continental plateau monsoon climate. This climate is characterized by cold temperatures due to high altitude, low precipitation, long sunshine hours, dry air, long winters, short summers, and intense solar radiation. To thoroughly assess the ventilation status of DSPH, a 3D scan of the building, physical environment tests, and indoor wind environment simulations were carried out. The field test results show that the unique architectural design of DSPH helps mitigate the impact of the harsh climate on indoor activities. By analyzing and evaluating the wind environment of DSPH, locally adapted climate resilience practices are summarized, and the physical properties of buildings characteristic of the Tibetan plateau are clarified. These findings provide a reference for promoting the development of local climate-adapted buildings, preserving traditional architectural culture, and promoting the integration of modern architecture with traditional culture. Furthermore, this study represents an important step toward enhancing energy efficiency and green building practices in ethnic minority settlements, contributing to sustainable development in the region.

Design and thermal performance analysis of self-insulation concrete compound blocks

More than 60% of energy losses occur through the building envelope. Exterior wall insulation technology is widely used for wall insulation, but it is prone to cracking, falling off, and causing fires. Self-insulation concrete compound blocks (SIB) have attracted considerable attention in recent years for meeting building energy efficiency standards without the need for external insulation treatment. In this study, the shale ceramsite concrete (SCC) was prepared as the base material for the blocks through the orthogonal test and range analysis. In accordance with the insulation requirements of residential building walls, 12 types of self-insulation concrete compound blocks (SIB) were designed. The heat transfer process of these blocks was simulated and analyzed using Ansys Workbench, enabling a comparison of the thermal conductivity effects resulting from different hole distribution schemes in the insulation blocks. The simulated values were compared with the theoretical calculations, and the simulated results were in good agreement with the theoretical calculations. The results showed that TZ-12 exhibited the optimal hole configuration with a heat transfer coefficient of 0.5 W/(m2·K), which was 38.3% lower than that of the external insulation block TZ-9. Additionally, TZ-12 demonstrated the average compressive strength of 8.28 MPa and the minimum compressive strength of 7.45 MPa, meeting the requirements for MU7.5 strength grade and also satisfying the requirement of not less than MU5.0 when self-insulation blocks were used for external walls. The simulated heat flux rate of the self-insulation concrete compound block wall (SIBW) was 15.4 W, and its heat transfer coefficient was 0.56 W/(m2·K), which was 29.1% lower than that of the external thermal insulation wall (ETIW), meeting the design standard for achieving the 65% energy saving in residential buildings situated in regions with hot summers and cold winters.

Thermal comfort evaluation using different glazing technologies in an energy model of rural housing in a cold climate

Building energy efficiency, consumption and comfort are aspects to be evaluated in the energy simulation of buildings (BES). However, their results deviate from reality leading to the need to improve their accuracy through calibration. In this regard, a rural house located in a cold climate at more than 3000 meters above sea level, is energetically modeled with its subsequent calibration and evaluation of thermal comfort. The study is developed in four stages, starting with the monitoring of climatological variables and followed by the house energy modeling. Subsequently, manual and iterative calibration is conducted by means of statistical comparison and scatter plots. In this way, a model with more than 80% accuracy between monitored and simulated data is achieved. Based on the previously calibrated model, the thermal comfort is evaluated using different glazing technologies and then, the number of hours for two levels of thermal sensation are evaluated (comfortable and not comfortable). For this purpose, statistical methods such as predicted mean vote (PMV) and predicted percentage of dissatisfied (PPD) are used, which are included in the Design Builder software. This study identified that single bronze glazing technology increased thermal comfort hours by 14.2% with respect to the case study, allowing the development of a methodology that improves thermal comfort through passive heating in rural dwellings in cold climates.

Building electrical load forecasting with occupancy data based on wireless sensing

Building electrical load forecasting, as a necessary foundation for building energy management, is of great significance for building energy efficiency and sustainable urban development. However, the accuracy of forecasting can hardly be guaranteed due to the stochastic nature of occupant behavior. To overcome this challenge, this paper proposes a data fusion-based building electrical load forecasting method with occupancy data obtained by wireless sensing technology. Firstly, a wireless sensing scheme is developed, which utilizes pre-existing wireless devices within the building energy management system (BEMS), offering a cost-effective means of obtaining occupancy information without violating occupant privacy. Moreover, to estimate the pattern of occupant behavior in the entire building, an improved stacked sparse auto-encoder (ISSAE) model is developed, which involves unsupervised feature fusion from information sources of varying significance. Finally, to cope with the time-varying and strongly fluctuating building load, a multi-source data fusion forecasting model based on the ensemble deep random vector functional link (edRVFL) is proposed. This model integrates the contributions of the latest accuracy and diversity through the ranking-based dynamic integration strategy. The effectiveness of the proposed method is validated in a commercial building. The experimental results demonstrate that, compared with the load forecasting scheme without occupancy information, the proposed method can improve the forecasting accuracy on RMSE and MAPE by 13.21% and 14.97%, respectively, while cost-effectiveness and privacy are ensured.

Building energy efficiency evaluation based on fusion weight method and grey clustering method

The renovation and evaluation of building energy-saving projects can provide important support for building an energy-saving society. The study proposes using the contract energy management model to analyze building energy-saving projects and construct an evaluation index system. We also innovatively integrated the Analytic Hierarchy Process and Entropy Weight Method to calculate the weights of indicators, in order to leverage the effective influence of subjective and objective factors. Finally, we used Grey Cluster Analysis to obtain the evaluation effect of building energy-saving projects. Through weight calculation and evaluation analysis, it was found that the energy-saving rates of year-end electricity consumption and air conditioning electricity consumption in buildings after energy-saving renovation were 59.80% and 54.95%, respectively. The overall effectiveness of energy-saving buildings was above 50%, indicating a significant energy-saving effect. In the indicator evaluation system, the weight results of energy-saving service company indicators were relatively high, with values of 0.52, 0.48, and 0.51, respectively. The transformation effect was relatively good. The building energy-saving cost and economic benefits obtained from a 65% energy-saving rate were 3 million yuan and 530,000 yuan, respectively, which were significantly better than the simulation results of other energy-saving rates. The contract energy management model based on the fusion weight method and grey clustering method has superiority, which is effective for evaluating building energy-saving projects. It also provides technical reference and scientific suggestions for building energy-saving renovation.