Energy Consumption Factors Research Articles

Comparative Life Cycle Assessments of Laboratory and Pilot-Scale Mechanochemical Processes for Producing Carbonated Mineral Products as Cement Substitutes.

The use of carbonated mineral products in cement production reduces carbon emissions and enhances durability. This study evaluated the environmental sustainability of using mineral carbonated biomass fly ash (BFA) as a partial cement replacement in European cement production. Laboratory-scale and simulated large-scale scenarios were analysed. Incorporating 20% mineral carbonated BFA showed potential for a 33% reduction in the annual Global Warming Potential (GWP) of cement products. Energy consumption factors, such as ball milling and mineral carbonation processes, were evaluated using a machine learning model and comminution flow sheet model simulations. The machine learning model predicted CO2 absorption and energy requirements for mineral carbonation, showing greater efficiency in large-scale scenarios. Life cycle assessments consistently revealed GWP reductions for OPC-BFA mixtures, with additional emissions reductions when incorporating flow sheet modelling and machine learning data. However, the study's limitations include simplified CO2 flue gas treatment, use of the mean EU electricity mix, exclusion of transportation impacts, and reliance on simulation data. Additionally, the cement mix exhibited reduced compressive strength. This study highlights the potential of mineral carbonated BFA to reduce cement production's environmental impact while emphasising the need for balanced optimisation between sustainability and material performance.

An analysis of the rebound impact of energy consumption and the factors that influence it in China's agricultural productivity

With China's agricultural sector being a major contributor to both the national economy and energy consumption, the rebound effect—the phenomenon where energy efficiency improvements lead to increased energy use—poses significant challenges to sustainable energy use. The motivation for this study stems from the need to understand the extent of this rebound impact and its underlying drivers, particularly in the context of China's agricultural modernization efforts. This study examines the rebound effect of energy consumption in China's agricultural productivity and the factors influencing it over the period from 1990 to 2023. Using a Generalized Method of Moments (GMM) model, this research analyzes the relationship between energy consumption, agricultural productivity, and several influencing factors, including technological innovation, rural-urban migration, and financial development. The results reveal that (1) energy efficiency improvements have led to a significant rebound effect in China's agricultural sector, limiting potential energy savings, (2) technological advancements have mitigated the rebound effect to some extent, but the effect remains substantial, (3) rural-urban migration has contributed to labor shortages, increasing mechanization and energy demand, and (4) financial development has positively influenced both agricultural productivity and energy use, exacerbating the rebound effect. The study suggests that policymakers should focus on developing stricter energy efficiency standards and promoting technological innovations that reduce energy intensity in agriculture, while also addressing labor migration challenges to curb the rebound impact and achieve more sustainable agricultural growth.

Dynamic Simulation of Carbon Emission Peak in City-Scale Building Sector: A Life-Cycle Approach Based on LEAP-SD Model

Systematically predicting carbon emissions in the building sector is crucial for formulating effective policies and plans. However, the timing and potential peak emissions from urban buildings remain unclear. This research integrates socio-economic, urban planning, building technology, and energy consumption factors to develop a LEAP-SD model using Shenzhen as a case study. The model considers the interrelationship between socio-economic development and energy consumption, providing more realistic scenario simulations to predict changes in carbon emissions within the urban building sector. The study investigates potential emission peaks and peak times of buildings under different population and building area development scenarios. The results indicate that achieving carbon peaking by 2030 is challenging under a business as usual (BAU) scenario. However, a 10% greater reduction in energy intensity compared to BAU could result in peaking around 2030. The simulation analysis highlights the significant impact of factors such as population growth rate, per capita residential building area, and energy consumption per unit building area and the need for a comprehensive analysis. It provides more realistic scenario simulations that not only enhance theories and models for predicting carbon emissions but also offer valuable insights for policymakers in establishing effective reduction targets and strategies.

Energy-Saving Multi-Agent Deep Reinforcement Learning Algorithm for Drone Routing Problem.

With the rapid advancement of drone technology, the efficient distribution of drones has garnered significant attention. Central to this discourse is the energy consumption of drones, a critical metric for assessing energy-efficient distribution strategies. Accordingly, this study delves into the energy consumption factors affecting drone distribution. A primary challenge in drone distribution lies in devising optimal, energy-efficient routes for drones. However, traditional routing algorithms, predominantly heuristic-based, exhibit certain limitations. These algorithms often rely on heuristic rules and expert knowledge, which can constrain their ability to escape local optima. Motivated by these shortcomings, we propose a novel multi-agent deep reinforcement learning algorithm that integrates a drone energy consumption model, namely EMADRL. The EMADRL algorithm first formulates the drone routing problem within a multi-agent reinforcement learning framework. It subsequently designs a strategy network model comprising multiple agent networks, tailored to address the node adjacency and masking complexities typical of multi-depot vehicle routing problem. Training utilizes strategy gradient algorithms and attention mechanisms. Furthermore, local and sampling search strategies are introduced to enhance solution quality. Extensive experimentation demonstrates that EMADRL consistently achieves high-quality solutions swiftly. A comparative analysis against contemporary algorithms reveals EMADRL's superior energy efficiency, with average energy savings of 5.96% and maximum savings reaching 12.45%. Thus, this approach offers a promising new avenue for optimizing energy consumption in last-mile distribution scenarios.

Energy and cost-efficient ionic liquids extractive distillation for producing electronic and polymer-grade propylene with emphasis on feedstock variability

The separation of propylene/propane is the main source of electronic or polymer-grade propylene. However, the current industrial method faces significant challenges due to high theoretical stages and energy consumption. In this work, an extractive distillation process using ionic liquids (ILs) 1-butyl-3-methylimidazolium trifluoroacetate ([BMIM][TFA]) is proposed to overcome these difficulties. Based on quantum chemical calculations, the mechanism of separation of propylene/propane is analyzed, and the mechanism behind the performance differences of various anions is explored. Optimal designs for extractive distillation processes are investigated for varying propylene/propane feed compositions, targeting electronic and polymer-grade propylene. A comparison of traditional distillation, heat pump distillation, and ILs extractive distillation evaluates both energy consumption and economic factors. The results suggest that both the cation and anion contribute to the separation of propylene/propane, with the cation having a greater effect than the anion. The separation is enhanced by the π-π stacking, C-H···π, and π···H···O interactions between [BMIM][TFA] and propylene, which strengthen the affinity for propylene over propane. Compared to traditional distillation, ILs extractive distillation reduces energy costs by 64.1% to 82.1% for polymer-grade propylene and by 69.5% to 84.0% for electronic-grade propylene, with theoretical stages dropping from 210 to 240 to 46–78 and 250–280 to 64–98, respectively. It also cuts total annual cost by 2.0% to 37.4% for polymer-grade and 7.4% to 39.2% for electronic-grade propylene compared to heat pump distillation. The extractive distillation using [BMIM][TFA] performs best, indicating substantial potential for separating propylene/propane.

Energy consumption analysis and operating characteristics research on small time scale of variable refrigerant flow air conditioning systems in public buildings

In order to analyze the energy consumption and operation characteristics of the variable refrigerant flow (VRF) air-conditioning system on small time scale and put forward effective energy-saving suggestions, the VRF system operation experiments are carried out under different indoor set temperatures, different indoor unit opening quantities and different indoor set air velocities. This experiment studies the main influencing factors of energy consumption and operation of the VRF system, such as indoor temperature difference, indoor air velocity, outdoor environment temperature and so on. The results show that the rated power of indoor unit is linearly related to the energy consumption of the VRF system. The total active power changes greatly when the VRF starts and stops, and the outdoor ambient temperature changes. The indoor set air velocities have a great influence on the energy consumption and operation of the VRF system. The performance parameters increase first and then decrease with the increase of compressor frequency and load rate. When the load rate is 40 %∼60 %, the performance parameters can reach 3.2 ∼ 4.2.

Advanced Machine Learning Techniques for Energy Consumption Analysis and Optimization at UBC Campus: Correlations with Meteorological Variables

Energy consumption analysis has often faced challenges such as limited model accuracy and inadequate consideration of the complex interactions between energy usage and meteorological data. This study is presented as a solution to these challenges through a detailed analysis of energy consumption across UBC Campus buildings using a variety of machine learning models, including Neural Networks, Decision Trees, Random Forests, Gradient Boosting, AdaBoost, Linear Regression, Ridge Regression, Lasso Regression, Support Vector Regression, and K-Neighbors. The primary objective is to uncover the complex relationships between energy usage and meteorological data, addressing gaps in understanding how these variables impact consumption patterns in different campus buildings by considering factors such as seasons, hours of the day, and weather conditions. Significant interdependencies among electricity usage, hot water power, gas, and steam volume are revealed, highlighting the need for integrated energy management strategies. Strong negative correlations between Vancouver’s temperature and energy consumption metrics are identified, suggesting opportunities for energy savings through temperature-responsive strategies, especially during warmer periods. Among the regression models evaluated, deep neural networks are found to excel in capturing complex patterns and achieve high predictive accuracy. Valuable insights for improving energy efficiency and sustainability practices are offered, aiding informed decision-making for energy resource management in educational campuses and similar urban environments. Applying advanced machine learning techniques underscores the potential of data-driven energy optimization strategies. Future research could investigate causal relationships between energy consumption and external factors, assess the impact of specific operational interventions, and explore integrating renewable energy sources into the campus energy mix. UBC can advance sustainable energy management through these efforts and can serve as a model for other institutions that aim to reduce their environmental impact.

Short-term building cooling load prediction based on AKNN and RNN models

Cooling load is the main energy consumption factor for commercial buildings during summer. This paper proposes a new approach to realize short-term building cooling load prediction by combining adaptive feature extraction and deep learning models. An autonomous kernel-based neural network (AKNN) is proposed to extract intrinsic patterns from sensor data by clustering kernels in a bottom-up way. Furthermore, a recurrent neural network-based sequence-to-sequence structure is proposed to make multi-step-ahead forecasting. Tested on an industrial park, the results indicate that the proposed model achieves the lowest RMSE of 1.79 MW for 1-hour-ahead prediction. For 24-hour-ahead predictions, the proposed model maintains superior accuracy with an RMSE of 2.85 MW, while ARIMA has an RMSE of 7.27 MW. Additionally, using a public dataset, the proposed model shows an 18.02% lower RMSE than ARIMA for 24-hour prediction, verifying its accurate and stable performance.

Thermal energy resource utilization and sample image restoration technology based on Machine vision simulation in interior design

With the rise of sustainable building design, the traditional design methods are often unable to fully consider the optimal allocation and application of thermal energy resources. This study aims to explore the application of heat resource utilization and sample image restoration technology based on machine vision simulation technology in interior design, and provide practical solutions for optimizing indoor environment. In this paper, machine vision technology is used to monitor and model indoor space in real time, and heat energy flow and distribution under different design schemes are simulated. At the same time, the influence of different designs on thermal energy utilization efficiency is analyzed by using sample image restoration technology. In the study, a set of comprehensive evaluation indexes was established, which comprehensively considered the factors of heat efficiency, indoor comfort and energy consumption. The experimental results show that the simulation technology based on machine vision can accurately predict the indoor heat distribution and identify the best design scheme. At the same time, the sample image restoration technology significantly improves the evaluation accuracy of heat utilization efficiency. Through these methods, the optimized design scheme has higher thermal energy utilization than the traditional design, and significantly improves the indoor comfort. This study shows that the combination of machine vision simulation and sample image restoration technology can effectively improve the utilization efficiency of thermal energy resources in interior design, and provide new ideas and methods for sustainable building design.

A Self-Adaptive Neighborhood Search Differential Evolution Algorithm for Planning Sustainable Sequential Cyber–Physical Production Systems

Although Cyber–Physical Systems (CPSs) provide a flexible architecture for enterprises to deal with changing demand, an effective method to organize and allocate resources while considering sustainability factors is required to meet customers’ order requirements and mitigate negative impacts on the environment. The planning of processes to achieve sustainable CPSs becomes an important issue to meet demand timely in a dynamic environment. The problem with planning processes in sustainable CPSs is the determination of the configuration of workflows/resources to compose processes with desirable properties, taking into account time and energy consumption factors. The planning problem in sustainable CPSs can be formulated as an integer programming problem with constraints, and this poses a challenge due to computational complexity. Furthermore, the ever-shrinking life cycle of technologies leads to frequent changes in processes and makes the planning of processes a challenging task. To plan processes in a changing environment, an effective planning method must be developed to automate the planning task. To tackle computational complexity, evolutionary computation approaches such as bio-inspired computing and metaheuristics have been adopted extensively in solving complex optimization problems. This paper aims to propose a solution methodology and an effective evolutionary algorithm with a local search mechanism to support the planning of processes in sustainable CPSs based on an auction mechanism. To achieve this goal, we focus on developing a self-adaptive neighborhood search-based Differential Evolution method. An effective planning method should be robust in terms of performance with respect to algorithmic parameters. We assess the performance and robustness of this approach by performing experiments for several cases. By comparing the results of these experiments, it shows that the proposed method outperforms several other algorithms in the literature. To illustrate the robustness of the proposed self-adaptive algorithm, experiments with different settings of algorithmic parameters were conducted. The results show that the proposed self-adaptive algorithm is robust with respect to algorithmic parameters.

Optimizing the Integration of Building Materials, Energy Consumption, and Economic Factors in Rural Houses of Cold Regions: A Pathway

Limited material options and economic conditions significantly restrict the potential for energy efficiency improvements in rural houses in China’s cold regions. It is worth exploring how to propose suitable energy-saving renovation plans for rural houses in cold regions under practical constraints. By using Grasshopper within Rhinoceros 8 software, an algorithm integrates material selection, energy consumption calculations, and economic analysis. The method efficiently generates thermal optimization schemes, providing insights into energy use, costs, and payback periods. In a case study of a typical rural house in Daqing City, the optimized scheme achieved over 70% energy savings compared to traditional homes, with renovation costs amounting to less than 40% of residents’ annual income and a 2-year payback period. This significant improvement highlights the potential of the proposed method in enhancing the energy efficiency and economic viability of rural house renovations.

The Impact of Renewable Energy on Low Energy Consumption in Civil Engineering Construction

As the main representative of new energy, photovoltaic energy is widely used in civil engineering construction, its purpose is to reduce the energy consumption in the project, there is a deviation in the integration of new energy and civil engineering, and it cannot effectively play its own advantages. Continuously analyze the changes in the energy consumption values of civil engineering. Then, the influencing factors of building energy consumption were introduced, and the covariance matrix was constructed, and the engineering construction benefit was taken as the ultimate goal. When calculating energy consumption, the dual benefits of building benefits are fully considered, and environmental protection and engineering energy consumption are estimated to reduce the error of energy consumption calculation. The results show that new energy can reduce the high energy consumption value in the project, improve the light transmittance, enhance the conversion rate of sunlight, and provide support for the transportation, lighting and thermal insulation of the building, with an increase rate of 10~25%, and the new energy can reduce the cost of civil engineering, with a reduction rate of 19~26%, and improve the economic and social benefits, with an increase of more than 10%. Therefore, new energy can reduce the energy and energy consumption of civil engineering, and expand the scope and application depth of new energy development.

Effect of lubricants during the rolling process on the mixed mode fracture behavior of the as-rolled material

Friction is one of the major factors in energy consumption and metal depreciation during various mechanical processes. Lubrication is often used during the process of forming metals to cope with friction and its consequences. Mineral lubricants are employed in the rolling process to decrease the rolling force and enhance the quality of the sheets. This research aims to compare the performance of mineral lubricants such as crude oil, gas oil, and CM405A to enhance the production efficiency of the aluminum forming process, especially in the rolling process. The ring compression test was utilized to evaluate the influence of lubrication on the friction coefficient which was extracted through calibration curves. Then, the effect of friction coefficient was experimentally examined during the forming process through rolling. Al 1050 was rolled by the mentioned lubricants. The pre-cracked rolled samples were then tested by fracture test at different loading angles of 0 (pure mode-I), 45(mixed-mode of I and II), and 90(pure mode-II). J-R curves were extracted using elastic–plastic fracture theory based on material characteristics. The fracture tests indicated maximum resistance to crack growth occurred in the sample prepared with CM405A lubricant. In contrast, the lowest crack growth resistance was recorded in the case where crude oil was employed as a lubricant. In the end, the SEM images of the fractured surfaces were explored for a deeper understanding of the fracture behavior. Although a combination of brittle and ductile fracture mechanisms can be observed on the fractured surfaces, cleavage was the dominant phenomenon and fracture mechanisms.

A green extraction method for agar with improved thermal stability and water holding capacity

The conventional agar extraction method has drawbacks such as high energy consumption, low yield, poor quality, and possible residual harmful factors, which greatly limit its application in high-end fields such as biomedicine and high-end materials. This work explored a new freezing-thawing-high-temperature coupling technique for agar extraction. It increased the yield and the strength of agar by 10.6 % and 13.7 %, respectively, as compared to direct high-temperature extraction of agar (HA). The greater molecular weight and lower sulfate content of agar obtained from freeze-thaw cycles combined with high temperature extraction (FA) may be attributed to the desulfurization effect caused by freeze-thaw cycles and the preservation of the molecular chain structure. The reduction in sulfate content decreases the steric hindrance resistance of the polysaccharide chains, enhances their interactions, and promotes the regularity and density of the agar structure, while also improving its water retention and thermal stability. In conclusion, this research can offer a theoretical basis and guidance for the eco-friendly extraction of agar with improved agar characteristics and expended its applications.

Thermo- economic feasibility of solar-assisted regeneration in post-combustion carbon capture: A diglycolamine-based case

The solvent regeneration in the post-combustion carbon capture process usually relies on steam from the power plant steam cycle. This heat duty is one of the challenges of energy consumption in PCC (Post-combustion Carbon Capture). However, this practice results in a significant energy penalty, leading to a substantial reduction in the capacity of the Power Plant, estimated to be between 19.5 and 40 %. This paper investigate the techno-economic feasibility of a solar-assisted regeneration process for the PCC industrial scale with diglycolamine solvent. The study aims to assess the impact of system configuration modifications, such as LVC (Lean Vapor Compression), SPCC (Solar Post-combustion Carbon Capture), and combinations of trough or compound solar collectors with LVC, on energy efficiency and overall plant performance. With 3E analysis for SPCC configuration results show that this configuration. However, reducing energy consumption and energy penalty factor, exhibits a decrease in exergy and exergoeconomic efficiency compared to the other configurations in terms of exergy and exergoeconomic aspects. However, the LVC + SCSS (Solar Combined Separator-Stripper) configuration demonstrates the best performance across the 3E aspects, resulting in a reduction energy penalty to 12.2 % and improvements of 38 % and 4.2 % in exergy and exergoeconomic factors, respectively.

Novel STAttention GraphWaveNet model for residential household appliance prediction and energy structure optimization

The multifaceted challenge of carbon dioxide (CO2) emissions during building operations, construction, and material production perpetuates energy shortages and climate adversities. Urgent imperatives underscore the need for effective energy reduction and carbon mitigation, driving the building sector toward a paradigm shift in clean, low-carbon evolution. However, the complex multidimensional and nonlinear characteristics of influencing factors of building energy consumption make it difficult in precisely understanding the spatial and temporal relationships between the features in the predictive modeling of buildings. Therefore, a novel Graph Wavenet (GWN) based on the spatio-temporal attention fusion mechanism (STA-GWN) is proposed for spatio-temporal graph prediction modeling of structures, which can capture hidden spatial relationships using an adaptive dependency matrix. To overcome the drawbacks of standard predefined adjacency matrices and their inability to capture faraway dependencies, a spatio-temporal attention fusion mechanism is introduced to extract latent spatio-temporal features dynamically. By combining extracted features, building energy consumption can be predicted more precisely. Moreover, the proposed model becomes more interpretable, making it easier to reveal how spatio-temporal features relate to building energy consumption. Lastly, the proposed method is applied to energy conservation and emission reduction in the building industry. A predictive model for electrical energy consumption during two public datasets with an operational phase of buildings is established to analyze and optimize energy utilization in residential structures that rely on electricity. The experimental results demonstrate that the STA-GWN model surpasses other deep learning methods in terms of prediction accuracy and robustness. Moreover, the proposed model can offer optimal solutions for building energy processes, reducing approximately 201.44 kg (Kg) and 64.08 Kg of CO2 emissions in the appliances energy prediction dataset and the individual-household-electric-power-consumption (IHEPC) dataset, respectively.

Study on carbon emission characteristics and its influencing factors of energy consumption in Sichuan Province, China

Clarifying the key sectors and influencing factors of carbon emissions from energy consumption is an important prerequisite for achieving the “carbon peaking and carbon neutrality” goals. This study calculated the carbon emissions of fuel combustion in 7 major departments and regional electricity trading of Sichuan Province from 2000 to 2021, and empirically analyzed the impact of energy structure effect, energy intensity effect, industrial structure effect, economic development level effect, and population size effect on the carbon emissions of energy consumption based on the LMDI model. The main research conclusions are as follows: (1) LMDI model has the advantages of no residual and high interpretation. By refining the multi-departments of energy consumption and different types of fuel, it is helpful to improve the accuracy of empirical analysis results. (2) The carbon emissions of energy consumption mainly come from the fuel combustion process. Specifically, the industry sector composed of steel, building materials, chemicals and machinery is the key emission sector, and transportation and residential life are also vital. Regional electricity trading can indirectly reduce the intensity of carbon emissions while ensuring the safety of energy supply. (3) From 2000 to 2021, the energy intensity effect and the economic development level effect were key factors in slowing down and promoting the carbon emission growth of energy consumption in Sichuan Province, respectively. The population scale effect mainly played a positive role in driving carbon emissions, but the impact is small and almost negligible. Before 2012, the energy structure effect and the industrial structure effect were mainly positive driving effects, and after 2012, they all turned into negative inhibitory effects. This was mainly due to the low-carbon transformation of energy structure and the optimization of industrial structure.

Optimization of electrocoagulation process parameters for the treatment of oil industry drill site wastewater.

The effluent from the oil drilling site is a complex mixture of hazardous chemicals that causes environmental impacts on its disposal. The treatment of oil drill-site wastewater has not been explored much, and understanding its characteristics and optimizing the treatment process are required. In the present study, we have optimized the electrocoagulation process with aluminum electrodes for drill-site wastewater treatment. A multi-level factorial center composite design using response surface methodology is applied to optimize the effect of current density (CD), pH, and inter-electrode distance (IED) on chemical oxygen demand (COD) removal. The increasing current density shows a significant increase in COD removal, and a similar trend was observed with a decreased pH. It was found that with current density and inter-electrode distance, the maximum COD removal achieved was 70% at the CD of 19.04mAcm-2 and IED 2.6cm. By varying pH and current density, the COD removal reached up to 90% at pH 6 and CD 19.04mAcm-2. The study shows that the current density is the dominant factor for the process's energy consumption and operating cost, followed by pH. This study's findings could be effectively used to develop large-scale treatment processes through electrocoagulation.

Design, analysis, and operation optimisation of a shipborne absorption refrigeration–ice thermal storage system based on waste-heat utilization

The integration of waste-driven absorption refrigeration technology into marine applications is recognized as a promising low-carbon solution. Nonetheless, its practical application does not inherently outperform electric compression technology, primarily due to the absorption system’s inherent thermal inertia and limited adjustability. This characteristic renders the system less responsive to the dynamic refrigeration requirements typically encountered on board vessels. In this study, a shipborne ammonia absorption refrigeration–ice storage system is proposed to balance the cooling load between supply and demand. To evaluate the efficiency of the integrated system for ice storage and cooling during ship sailing, key operational variables including refrigeration temperature, cooling water temperature, and generation temperature are analyzed to assess the thermal cycle performance of the system. A typical shipping route is considered for cooling operation optimisation of the absorption refrigeration–ice storage system. This paper explores four typical cooling-supply strategies and determines the operation modes for the ice storage period and the cooling period based on considerations of energy consumption, energy utilization, and economic factors. The final optimisation results indicate that the combined system has significant advantages based on energy consumption and operation cost. In comparison to compression and absorption refrigeration systems, the proposed system exhibits 52.4% and 8.31% reductions in energy consumption, respectively. Additionally, the operation costs are decreased by 12.27% and 27.41%, respectively, and the life cycle costs are lowered by 11.51% and 32.35%, respectively. The findings of this combined system can provide a technical reference for the design and operation of a ship waste-heat refrigeration system.

Energy Consumption Calculation of Civil Buildings in Regional Integrated Energy Systems: A Review of Characteristics, Methods and Application Prospects

Civil buildings play a critical role in urban energy consumption. The energy consumption of civil buildings significantly affects energy allocation and conservation management within regional integrated energy systems (RIESs). This paper first analyzes the influencing factors of civil building energy consumption, as well as the energy consumption characteristics of different types of buildings such as office buildings, shopping malls, hospitals, hotels, and residential buildings. Subsequently, it reviews methodologies for calculating operational energy consumption, offering valuable insights for the optimization and strategic adjustments of an RIES. Finally, the paper assesses the application potential of these calculation methods within an RIES and discusses the future development trend of calculating civil building energy consumption.