Comprehensive Energy Consumption Research Articles

Optimizing electric vehicle routing under traffic congestion: A comprehensive energy consumption model considering drivetrain losses

Energy consumption is a critical concern within the electric vehicle (EV) industry. Despite its substantial impact, drivetrain losses have been inadequately addressed in some studies. This paper introduces drivetrain losses as a nonlinear function of velocity, incorporating them into the comprehensive energy consumption model. Considering the influence of traffic congestion on vehicle speed, our proposed model integrates drivetrain losses, aerodynamic losses, tire losses, kinetic energy losses, and potential energy losses. The objective is to optimize routing with the aim of minimizing energy consumption. To tackle this problem, we present a dedicated adaptive large neighborhood search algorithm. Our numerical experiments reveal key findings: (1) Drivetrain losses represent a higher percentage of total energy consumption compared to aerodynamic and tire losses; (2) Drivetrain losses influenced by traffic congestion constitute a significant share, ranging between 63.82% and 72.08% of total energy consumption; (3) Energy consumption experiences notable increases under the impact of traffic congestion.

Research on Multi-Objective Optimization Design of University Student Center in China Based on Low Energy Consumption and Thermal Comfort

Ensuring optimal building performance is vital for enhancing student activity comfort and fostering energy-saving initiatives toward low-carbon objectives. This paper focuses on university student centers in China, aiming to diminish building energy consumption while enhancing indoor thermal comfort. Parametric modeling of typical cases is executed using the Grasshopper 1.0.0007 software package, and the simulation of building energy consumption and indoor thermal comfort relies on the Ladybug and Honeybee plug-in. Employing a multi-objective optimization design method and the Octopus multi-objective optimization algorithm, this study integrates numerical simulations and on-site surveys to analyze how factors like building form, orientation, envelope structure, and others impact the indoor and outdoor environment. A comprehensive optimization design approach is implemented for the building’s exterior components, including the walls, windows, roof, and shading system. After conducting a comparative analysis of the annual comprehensive energy consumption and indoor thermal comfort before and after the optimization plan, it is determined that implementing these measures reduces the annual comprehensive energy consumption of the building under study by 58.8% and extends the duration of indoor thermal comfort by 53.0%. This study presents a practical optimization design methodology for university student center architecture in China, aiding architects in decision making and advocating for energy-efficient building designs.

Architecture-level energy model for high-capacity STT-MRAM memory

ABSTRACT Nowadays, STT-MRAM has emerged as one of the leading contenders for next-generation memory technologies. However, STT-MRAM still exhibits high energy consumption due to its peripheral circuits, including the read/write and the sample amplifier (SA) circuits. Therefore, it is crucial to accurately evaluate the energy consumption of memory architectures during the early stages of the design process. Traditionally, energy evaluation can be conducted using the NVSim platform. However, the peripheral read/write circuitry of STT-MRAM undergoes iterative improvements, which cannot be promptly incorporated into NVSim. To addreGss this limitation and to enable comprehensive energy consumption evaluations spanning from device-level to architecture-level for STT-MRAM, an architecture-level energy consumption model is proposed in this paper. The model offers high accuracy and flexibility for the STT-MRAM memory array structure, abstracting the influences of read/write circuits for power evaluations. Comparative analysis is performed between the proposed model and the traditional approaches. When estimating a memory array with 128 × 64, the deviation using the proposed model is less than 2%, showing improved accuracy compared to that with analytical formulas. Notably, the proposed model offers up to 97% time savings in the whole circuit simulation, thus enhancing efficiency in the design and evaluation process.

An Improved Slack Based Measure Model for Evaluating Green Innovation Efficiency Based on Asymmetric Data

Nowadays, one of the main challenges facing green innovation management is how to enhance the performance of innovation processes by utilizing asymmetric input and output data. Therefore, this paper develops an improved SBM model analysis framework for evaluating the green innovation efficiency of asymmetric input and output data. The framework is applied to assess the technical (TE), managerial (PTE), and scale (SE) efficiencies of new energy companies under three input variables (R&D personnel input, R&D capital input, and comprehensive energy consumption input), two desirable output variables (green technology output and economic output), and one undesirable output variable (greenhouse gas emissions). Then, environmental factors and random factors are eliminated from the obtained input slack variables based on the SFA model, placing decision-making units in a homogeneous environment. The results demonstrate that TE, PTE, and SE are improved after eliminating environmental factors and random factors. Subsequently, based on the entropy method, this paper classifies companies’ green innovation patterns into four categories and provides targeted solutions. The purpose of this paper is to provide an evaluation method for new energy companies to understand green innovation efficiency and assist decision makers in identifying the most optimal resource allocation approach. The proposed improved SBM model contributes to the literature and to industry practice by (1) providing a reliable evaluation of green innovation efficiency under asymmetric input and output data; (2) determining effective improvement actions based on a slack analysis of environmental variables and random variables that lead to improved process performance; and (3) making fuzzy innovation performance efficient to facilitate understanding and managing innovation resource allocation quality.

Low Carbon and High Efficient Utilization of Zinc-bearing Dust in Iron & Steel Industry by Rotary Kiln Process

More than 50 million tons per year of zinc-bearing dust with large amounts of zinc and iron resources is produced by iron and steel smelting in China, which is classified as hazardous solid waste. The zinc-bearing dust is an important secondary resource, in which the zinc and iron can be recycled as raw materials for the iron and zinc industries after separating them. Rotary kiln reduction is one of the main methods for separating zinc and iron from zinc-bearing dust. In this study, the 5 mm composite pellets containing zinc-bearing dust is prepared by forced disturbed pelleting as reduction charge. The rotary kiln system with controllable temperature field and atmosphere field by multi-point quantitative air suppling is used as the main equipment of reduction. This technology can realize the treatment of raw materials with high iron and zinc content in rotary kiln, but also can effectively improve the production efficiency and the quality of zinc oxide powder, reduce the kiln-ringing. Compared with the conventional rotary kiln method, the processing capacity of zinc-bearing dust increase by 30%. The zinc removal ratio reaches 95% and the ZnO content of zinc oxide powder is 53.93 wt.%. The TFe content and ZnO content of reduction product are 60.56 wt.% and 0.25%, respectively. The annual operation ratio of zinc-iron separation by rotary kiln with zinc-bearing dust is 92%. The metallization rate of reduction product is controllable. The reduction product with metallization rate of 65.97% can be used as a sintering raw material, and the comprehensive energy consumption is 187kgce/t. The reduction product with metallization rate of 88% can 1 be used as a converter steelmaking raw material, and the comprehensive energy consumption is 253.1kgce/t.

Coupled optical-thermal-electrical modelling of translucent photovoltaic curtain wall considering indoor lighting

The thermal, optical and electrical properties of PV curtain walls are coupled, and the results obtained from a single calculation model are biased. Therefore, the development of a coupled thermal-optical-electrical performance model for crystalline silicon PV curtain walls is essential for their thermal-optical-electrical performance analysis. In this paper, light harvesting calculation models, heat transfer calculation models and power generation calculation models are developed based on the structural characteristics of translucent crystalline silicon photovoltaic curtain walls, and the coupled calculation models are developed using the linkage between some input and output parameters and the parameters of the calculation process. An experimental platform for translucent crystalline silicon photovoltaic curtain walls was built and the performance parameters of light, heat transfer and power generation of photovoltaic curtain walls and ordinary curtain walls were calculated according to the experimental conditions. The coupled model is then used to analyse the thermal, optical and electrical performance of buildings with translucent PV curtain walls with different PV module distribution methods and comprehensive energy consumption under the five thermal zones, and the best solution is given for the PV module distribution methods of translucent PV curtain walls in each of the five thermal zones based on the results of the light and comprehensive energy consumption studies.

Sensitivity study of integrated carbon capture and methanation process using dual function materials

Integrated Carbon Capture and Methanation (ICCM) is a promising technology to produce Synthetic Natural Gas (SNG) via captured CO2 and renewable hydrogen. This work performed a sensitivity study on how operation pressure, the types of DFM and point source, and the catalytic metal loading in DFMs affect the performance of ICCM process. As Na-Ru/Al2O3 (Na-DFM), (Li-Na-K)NO3-MgO-Ni/CeO2-phy (Mg-DFM), and Ni5-CeLi (Li-DFM) were adopted as DFMs, the performances of ICCM process were investigated in the range of 0.1 Mpa-5.0 Mpa, showing elevated pressure is favorable to improving capture efficiency and the quality of SNG products, and reducing hydrogen consumption. Under the optimized operation conditions, Na-DFM based ICCM system shows the better overall performance than ICCM system using Mg-DFM or Li-DFM, which energy conversion efficiency, comprehensive energy consumption, and net CO2 emission are 82.74 %, –14.61 MJ/kg CO2, and –1.18 kg CO2/kg CO2, respectively. Circulation rate is the amount of DFMs necessary to be transferred between reactors to ensure the desired CO2 capture efficiency or SNG productivity; it heavily depends on the type of adsorbent component, capture/hydronation reactions, and the fraction of active adsorbent component in DFMs for CO2 transport. Na-DFM based ICCM systems using different point sources (Power Plant, Steel Plan, and Cement Plant) possess the approximate performances in terms of energy conversion efficiency, comprehensive energy consumption, net CO2 emission, and SNG composition; but when Steel Plan is the point source, Na-DFM based ICCM process features of low electricity and hydrogen consumptions. Cyclic redox reactions of catalytic metal and the cycled capture/hydrogenation proceed in parallel in ICCM process. The increased hydrogen consumption and strong exothermic effects arising from the redox reactions of catalytic metal leads to profound performance changes of ICCM system. Overall, the present work is providing comprehensive information and necessary data to guide the design and operation of an ICCM plant.

Cloud-edge collaborative computing offloading and resource allocation optimization in LEO satellite network

Satellite edge computing can provide communication and computing services to areas lacking ground network coverage, enhancing the computational capabilities of terrestrial users. However, due to the relatively limited computational resources available on satellites, cloud-edge collaborative computing in Low Earth Orbit (LEO) satellite communication scenarios emerges as a more optimal solution. In this paper, a cloud-edge collaborative offloading algorithm based on Deep Deterministic Policy Gradient (DDPG) algorithm is proposed to solve the offloading decision and resource allocation problems. The optimization problem is modeled as Markov decision process, and the comprehensive delay and energy consumption cost of computing tasks completed by ground users are optimized. Simulation results validate that the proposed algorithm has good convergence properties and lower comprehensive cost than processing all tasks locally and offloading all tasks to LEO satellites.

Research on decentralized resource operation optimization of virtual power plant with 5G base station

Abstract The extensive construction and promotion of 5G base stations (5GBSs) have led to a surge in communication energy consumption, as 5G energy consumption is about three to five times that of 4G. To reduce the energy consumption of 5GBS, this article incorporates 5GBS into power demand side management and proposes a flexible resource collaborative optimization method that integrates 5GBS with virtual power plants (VPPs). Firstly, starting from the basic structure of 5GBS, the adjustable potential of 5GBS and the paths for participating in VPP optimization were analyzed. Secondly, with the minimum operating cost as the optimization objective and considering the constraints of 5GBS and various decentralized resources, a demand side decentralized resource collaborative optimization model for VPP integrating 5GBS was established. This model can be solved using the Non-dominated sorting genetic algorithm II (NSGA-II) algorithm. The results indicate that the participation of 5GBS in power demand side management can reduce their comprehensive energy consumption and operating costs.

Electricity–gas multi-agent planning method considering users’ comprehensive energy consumption behavior

With the advent of the energy Internet and the swift growth of unified energy systems, the comprehensive energy demand of users has gradually become a problem that cannot be ignored for the planning of integrated energy systems. Aiming at this problem, this paper suggests a multi-agent planning approach for electricity and gas, considering users’ holistic energy consumption behavior. First, utilizing a combined subjective and objective weighting method, this study establishes a utility model for users’ energy consumption characteristics. The analysis of comprehensive energy consumption behavior is conducted through an evolutionary game. On this basis, the planning revenue model for electricity grid and gas network investors is formulated, and the game mechanism of different investors is analyzed. A dynamic game model of electricity–gas multi-agent planning considering comprehensive energy consumption behavior is proposed. Ultimately, the model is resolved using an iterative exploration approach. The validity and efficacy of the proposed method are confirmed through a simulation example.

Energy Consumption Modeling and Optimization of a Hobbing Machine Tool Considering Multi-Axis Coupling

Energy consumption of machine tools has always been a hot issue in the manufacturing sector due to the concern about climate change. To decrease the energy consumption of machine tools, this paper investigates the multi-axis coupling mechanism of a hobbing machine tool during machining from the design stage, and develops an energy consumption model under multi-axis coupling of the whole machine. Firstly, the multi-axis coupling mechanism in the actual machining process is studied. The hob speed and the feed speed of Z axis moving component will affect the hobbing force, and the component of hobbing force in each direction will affect the load and energy consumption of each axis. Meanwhile, the tracking error of each axis is reduced by a sliding mode controller. Next, a comprehensive energy consumption optimization model of a hobbing machine tool under multi-axis coupling is constructed. Furthermore, three optimization algorithms are implemented to the energy consumption model. Compared with the initial scheme, the simulation experiment results of the case-study indicate that the hobbing machine tool energy consumption is reduced by 1.19%, the steady-state error of each axis is reduced by 35.27%, 21.70%, and 36.34%, respectively, and the maximum deformation of the tool holder is reduced by 10.91%. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This work is dedicated to dealing with the issue of a hobbing machine tool energy consumption. Based on our previous work, the multi-axis coupling mechanism of the gear hobbing machine tool is investigated, and a comprehensive energy consumption model of the gear hobbing machine tool is proposed. The design variables of the energy consumption optimization model include the structural parameters of the Z axis moving component and the sliding mode controller parameters of the three axes (B, C, and Z axis). Three optimization algorithms are used to drive the optimization model. The simulation results verify the feasibility of the method. The control performance of the system is improved, and the mass of moving component is reduced.

Modeling method for optimal scheduling of Park comprehensive energy source load storage and consumption considering customer portrait

Aiming at the problems of energy consumption optimization of park integrated energy source load storage system in power grid dispatching, a modeling method of park integrated energy consumption optimization dispatching considering user portrait is proposed. Firstly, an improved k-means portrait method is proposed for park users; Secondly, on the premise of considering the constraints of equipment operation in the park, cluster users in the park, and establish an energy consumption optimization scheduling model with the objective function of user satisfaction, net income of wind power consumption and equipment operation cost; Finally, SAPSO algorithm and fuzzy theory are used for decision-making. The example simulation shows that the proposed model is correct and effective, which is more precise and objective than the traditional model.

The impact of green finance on total factor carbon emission reduction efficiency in China

Based on an analysis of the relationship between green finance and total factor carbon emission reduction efficiency, this article measures the levels of green finance and total factor carbon emission reduction efficiency in 30 provinces and cities in China. It also establishes a spatial Durbin model to quantitatively explore the impact of green finance on China’s total factor carbon emission reduction efficiency. The results indicate that currently, green finance and total factor carbon emission reduction efficiency in China follow a distribution pattern of high in the east, medium in the central region, and low in the west. The impact of green finance on total factor carbon emission reduction efficiency demonstrates a U-shaped relationship, and the spatial spillover effect between the two displays a similar U-shaped trend. The mechanism analysis demonstrates that green finance exerts a U-shaped influence on the efficiency of reducing total factor carbon emissions through the interplay of technological progress and technical efficiency enhancements. Other variables, such as research and development investment, comprehensive energy consumption, human capital, infrastructure construction, and government regulation, also have an impact on total factor carbon emission reduction efficiency. Therefore, it is recommended that regions strengthen their green finance initiatives, support efforts to carbon emission reduction, and contribute towards achieving the “dual-carbon” goal.

The Spatial Distribution and Influencing Factors of Heavy Metals in Soil in Xinjiang, China

Heavy metal pollution has been a problem of concern in soil ecology in recent decades. This study investigated the spatial distribution of heavy metals and their pollution levels in the soil of Xinjiang, based on the data of heavy metals published in the literature in the past five years, by using a geostatistical method, pollution index method, and geographic information system (GIS)-based spatial analysis. Additionally, the effects of five economic development indicators, such as population and industrial activities on the accumulation of heavy metals in soil, were explored by correlation analysis. The results showed that the average contents of Cd, Cr, Cu, Ni, Pb, and Zn in the soils were 2.858, 1.062, 1.194, 1.159, 1.192, and 1.086 times higher than the background values in Xinjiang, respectively. The semi-variance functions indicated that the Cd and Pb block gold coefficients of soils were greater than 25% and less than 50%, with an obvious spatial correlation. The spatial patterns showed that the high values of Cd, Cr, Cu, Ni, Pb, and Zn were mainly distributed in Karamay, Changji, Tacheng, and Kashi areas, with an overall decreasing trend from north to south, and the pollution index showed that the pollution of heavy metal Cd in soil was the most serious. Furthermore, Karamay, Changji, and Kashi areas were at heavy pollution levels. Correlation analysis showed that heavy metal Pb in the soil was significantly positively correlated with the agricultural GDP in Xinjiang, while Cd was correlated significantly and positively with comprehensive energy consumption and more significantly with industrial GDP. Thus, this study could provide a scientific basis for local evaluation of soil environmental quality and prevention and control of soil heavy metal pollution, which is of great significance for understanding the impact of human activities.

Conceptual design and assessment of Integrated capture and methanation of CO2 from flue gas using chemical-looping scheme of dual function materials

Integrated Carbon Capture and Methanation (ICCM) with renewable hydrogen is considered a promising Power-to-Gas technology. Although ICCM is expected to reduced CO2 emission, the potentials of net CO2 reduction and economic feasibility are still unrevealed by careful assessments. This study developed three ICCM processes at industrial scale, and performed an assessment in terms of energy conversion efficiency, comprehensive energy consumption, net CO2 emission, and levelized cost of CO2 conversion. Special attentions were paid to investigate the effects from electricity and hydrogen sources, and plant configuration on the technical, environmental, and economic feasibilities. This work also identified the opportunities and challenges as operation pressure, SNG price, and carbon tax are consideredthe primary factors. A comparison was made between ICCM processes and the reference process that combines CO2 capture with aqueous solutions and CO2 catalytic hydrogenation for CH4 fuels. 89.0 % of energy conversion efficiency could be achieved with ICCM processes, much higher than that of reference process. When ICCM processes are driven by renewable energy, the best comprehensive energy consumption is estimated to be –20.68 MJ/kg CO2; negative CO2 emissions could be easily achieved for ICCM processes, and the best net CO2 emissions is –1.57 kg CO2/kg CO2 when powered by wind energy, demonstrating the technical and environmental feasibility as ICCM process is coupled with renewable energy. The levelized costs of CO2 conversion are in the range of 0.98–2.0 US$/kg CO2. The economic feasibility of ICCM process is primarily limited by H2 production cost. But the levelized costs of CO2 conversion decreases rapidly with H2 production cost, and the ICCM processes could become profitable if the cost of H2 production from renewable energy drops to the same level of coal-based hydrogen (<1.4 US$/kg H2). It is possible for European Union to deploy ICCM plants to bring economic revenues because of its high SNG price and carbon tax.

Research on joint planning method of supply and demand considering users’ comprehensive energy consumption behavior and policy orientation

Given the recent escalation of the energy and environmental crises, it is crucial to consider the evolution of various energy systems from a macro perspective in order to increase the overall efficiency of energy use and improve the environment. In order to tackle this issue, a wide-area integrated energy system planning method that takes into account consumers' comprehensive energy consumption behavior (CECB) and policy recommendations is provided in this study. First of all, according to the analysis of users’ CECB, the decision-making model of regional users' energy supply transformation is established. A game model is constructed to explore cooperative planning of an integrated electricity-natural gas energy system based on the profit models of various market participants. The government is considered as the main body of planning and decision-making, and the evaluation model of government’s pollution emissions from both supply and demand sides is established. A two-level dynamic game planning model for the wide-area IES is then created as a result of the examination of the game mechanism between users, the government, the integrated energy system (IES), and other market players. Finally, the model is solved using an iterative search approach. Results from simulations are used to validate the suggested method’s accuracy and efficacy.

Comprehensive Characterization of Energy Saving and Environmental Benefits of Campus Photovoltaic Buildings

The development of campus photovoltaic buildings is a promising way to solve the problem of high energy consumption in colleges and universities. However, comprehensive study on their energy saving and environmental benefits is still insufficient. In this study, a theoretical model of a photovoltaic building roof system was preliminarily built, and the main factors affecting the power generation of campus photovoltaic buildings were analyzed. Furthermore, an experimental test platform for the campus photovoltaic building system was built, and a dynamic grid-connected strategy of “spontaneous self-use, surplus electricity connected to the grid” was creatively proposed, which points out that the grid connection rate in winter and summer vacations are about 15% and over 40%, respectively, and the annual grid connection rate is 25%. The result shows that the electricity input of the campus photovoltaic building can bear nearly 30% of the school’s annual electricity supply, which reduces the comprehensive energy consumption per unit area and per capita comprehensive energy consumption of the campus by more than 20%. The economic and environmental benefits of the 130,000 square meter campus photovoltaic building in the article are 38.8 million CNY and 20.12 million CNY, respectively, and the static investment payback period is about 7 years. The results show considerable reference value to the upgrading of campus photovoltaic buildings.

Greenhouse gas emission in the whole process of forest fire including rescue: a case of forest fire in Beibei District of Chongqing.

In the context of global high temperature, the harm of greenhouse gases (GHG) emissions caused by frequent forest fires to the environment cannot be ignored. Existing research only calculates the GHG generated by the burning of forest vegetation, ignoring the GHG generated by the fire-driven social rescue activities. Taking the forest fire in Beibei District, Chongqing City, China, as an example, this paper studies and establishes the GHG emission accounting method for the whole process of forest fire from ignition to fire extinguishing through three processes: vegetation burning, rescue transportation, and on-site fire extinguishing. It covers three GHG calculation types: biomass burning, traffic activity level comprehensive energy consumption, and machine energy consumption. Among them, the CO2 produced by the burning of coniferous forest, the support transportation of rescue teams in Yunnan province, and the motorcycle transportation at the fire extinguishing site accounted for a relatively high proportion in the corresponding processes, reaching 12,761.445 t, 118.750 t, and 1056.980 t, respectively. Finally, through data analysis, suggestions on GHG emission reduction related to forest tree regulation and optimization of rescue and fire extinguishing management are put forward, which provides a direction for future research on carbon reduction in the whole process of forest fire events.

Energy-efficient motion planning of an autonomous forklift using deep neural networks and kinetic model

Autonomous Forklifts (AFs) play a vital role in smart factories, particularly in the transportation of heavy loads. However, their energy consumption poses a significant challenge as they need to operate for extended periods on a single battery charge. Therefore, energy-efficient motion is necessary to raise their availability. The AF’s movement is dynamically determined by a motion planning algorithm within its navigation system. In light of this, this article introduces a strategy to improve energy efficiency during the motion planning phase. This strategy involves a cooperative approach, utilizing the Deep Neural Networks (DNNs) and AF’s kinetic model to achieve this energy-saving goal. Unlike traditional methods that rely solely on the vehicle’s kinematic model, our approach considers an additional factor, incorporating the influence of the vehicle’s kinetic model for a more comprehensive and accurate energy consumption analysis. First, the kinetic model of an AF is developed by considering the effect of the front-powered wheel. Second, the kinetic model is employed within a time-energy optimization technique, aiming to find the AF’s ideal acceleration. This optimization process generates a dataset that covers a range of AF maneuvers and dynamic parameters. Third, a DNNs model is trained using this dataset to predict the optimal acceleration for the AF. Finally, the trained model is integrated into a motion planning algorithm to determine the optimal and acceptable limits for both linear and angular acceleration. Experiments illustrate that the suggested motion planning method can generate trajectories that are both feasible and optimized for energy consumption. This differs significantly from the typical algorithms which generally results in higher energy use by the AF, occasionally leading to the generation of infeasible trajectories.

Analysis of faculty of electrical engineering and informatics building energy use intensity in pardubice, czech

The investigation focused on analyzing the energy usage patterns of the Faculty of Electrical Engineering and Informatics in Pardubice. The recorded energy consumption data were examined and discussed. The average heating energy consumption in the Faculty of Electrical Engineering and Informatics building in 2020, 2021, and 2022 is 686.45 MWh or 57% of the total energy equivalent value. Meanwhile, the average electricity consumption in the building during the same period is 518.97 MWh or 43% of the total energy equivalent value. The average fluctuation in heating energy consumption used in the Faculty of Electrical Engineering and Informatics building tends to increase by 2.9% per year, while the average fluctuation in electricity consumption tends to increase by 3.3% per year. According to the findings, the average comprehensive energy consumption per unit area in the building was 146,03kWh/m2. It was the sum of the energy use intensity for heating and the energy use intensity for electricity. The average energy use intensity value for heating in the building was 83.16 kWh/m2, while the average energy use intensity value for electricity was 62.78 kWh/m2.