Building Energy Assessment Research Articles

Multivariate stochastic generation of meteorological data for building simulation through interdependent meteorological processes

In recent years, the uncertainty of weather conditions and the impact of future climate change on building energy assessment has received increasing attention. As an important part of these studies, several types of methods for generating stochastic meteorological data have also been developed. Since solar radiation drops to zero at night, unlike the continuous 24-hour data for elements such as temperature and humidity, this has posed challenges for previous research to fully account for the simultaneity among multiple elements. Therefore, this study proposes a framework for meteorological data generation: First, perform multivariate time series modeling of meteorological data of air temperature, solar radiation and absolute humidity at 12:00 of each day of a typical year based on the S-vine copula method and simulating daily series data at 12:00 for 365 days. Then, based on the probability of change of each element evaluated from the historical meteorological observation data, the daily series data at 12:00 were expanded to 24 h, after which the yearly stochastic weather data were obtained. The analysis of 30 years of stochastic data generated by this method, compared with the original data, reveals that air temperature and solar radiation closely match the original distribution characteristics, except for a minor deviation in the absolute humidity’s kurtosis. Furthermore, the comparison of thermal load distributions for office buildings shows that the original data curve falls within the range of the generated data. This suggests that the generated data includes more information about uncertainty but still keeps the original data’s characteristics.

Incorporating multi-source uncertainties in fast building wall thermal resistance estimation through physics-based and multi-fidelity statistical learning models

Meta-modeling and Bayesian inversion technique are proposed for fast and accurate in-situ estimation of the total thermal resistance (RTot) of walls using non-intrusive wall instrumentation. Various sources of uncertainties are taken into account to provide an enhanced credible interval for the thermal resistance estimate. In the considered protocol, a small zone of the internal surface of the wall is excited by a prototype to get faster in situ estimation and to limit the influence of external weather conditions. To be independent of the heat transfer coefficients and of the misknown layer thicknesses, which are difficult to estimate, we use herein the measurements of internal and external surface temperatures as boundary conditions in a thermal direct problem reformulated. A meta-model of the thermal model is created based on a statistical multi-fidelity approach with two levels of fidelity, Resistance-Capacitance (RC) and 1D models, to achieve the Bayesian estimation of the total thermal resistance in a reasonable computation time. The RTot estimation method is applied to realistic internal insulated walls (IIW), from poorly to highly insulated, under different weather conditions. Several numerical tests are carried out and credible intervals are provided to study the importance of the wall initial condition, the excitation time and the instrumentation. Finally, an experimental application is conducted using real measurements on an internal insulated wall (IIW) in Nancy (France). The obtained experimental results show that, in a short excitation time (10h) with a reduced instrumentation, the proposed method accurately estimates the minimum wall thermal resistance. For a standard wall of about 4 m2.K/W, a relevant estimate of RTot with a relative deviation less than 10% can be achieved by using a polynomial initial temperature profile proposed in this study, sufficient measurements and an excitation time of 3 days. This study thus offers prospects for improved energy assessment of buildings before and after renovation.

Assessing Building Energy Savings and the Greenhouse Gas Mitigation Potential of Green Roofs in Shanghai Using a GIS-Based Approach

Climate change can significantly affect building energy use and associated greenhouse gas (GHG) emissions in urban areas, as fossil fuels remain a significant energy source. Green roofs can offer multiple benefits to the urban environment, but their effects on GHG mitigation have not been fully investigated, especially under climate change. This study assessed green roofs’ contribution to GHG mitigation by saving building energy and absorbing CO2 under the present (2017–2019) and future (2049–2051) climate scenarios (SSP2-45 and SSP5-85) in Shanghai, China, at the city and township scale. A Geographic Information System (GIS)-based spatial statistical method was developed based on climate change modeling and building energy simulation. The results suggested that installing green roofs can effectively save building energy regardless of building type, yet the amount of savings can vary depending on the weather conditions within the city. The contribution analysis indicated that most saved building energy was attributed to the Heating, Ventilation, and Cooling (HVAC) system, with more energy saved under warmer climate scenarios in the future, particularly during the summer months. More energy was saved from shopping malls on an annual and monthly scale, regardless of the climate scenarios and weather zones. Finally, a case study indicated installing green roofs on all five types of buildings (office, hotel, hospital, shopping mall, apartment) of less than 50 m in height can reduce 8.28% of the CO2 emitted during the building operation stage in the entire city under the present climate scenario. The annual CO2 reduction varied with the location of townships, ranging from 2.18% to 13.78%, depending on the composition of building types and local weather conditions in Shanghai. This study offered policymakers a reference on the environmental benefits and investment values of installing green roofs in large cities.

Assessing indoor air temperature settings in residential buildings during the COVID-19 era

While comfort models provide reliable evidence for designing building thermal environments, they may not be adequate for assessing building energy consumption due to factors beyond comfort. These factors influence occupants' actual control of their thermal environment. This study assessed how residential building occupants control indoor air temperature to maintain their comfort during the COVID-19 era. A dataset established from the ‘Donate Your Data’ program administered by Ecobee Inc. was utilised. The dataset was collected from 86,658 thermostats belonging to 65,535 users in the United States over multiple years, along with metadata that includes characteristics of the users and their homes. Statistical analysis was performed to suggest an indoor temperature range at which 90 % of collected data falls when the prevailing mean outdoor air temperature is between −10 °C and 37 °C, which effectively covers a wide variety of climate conditions. The estimated range of indoor air temperatures was 6.3 K before the pandemic, but it decreased by 1 K–5.3 K during the pandemic, implying that occupants used more energy to maintain a comfortable indoor air temperature during the pandemic. Our model can be useful in designing indoor temperature settings in residential buildings based on outdoor temperature.

Uncertainty in the embodied energy assessment of buildings: a case of EWS type low-cost house in India

Studies on embodied energy of buildings often present results as a single-point estimate without considering the variability in input parameters and the uncertainty it poses on results. Uncertainty analysis is useful to improve the reliability of results. This study examines uncertainty in the embodied energy of a single-storey EWS type residential building due to variability in the embodied energy intensity of materials and material wastage factors. Data quality indicator (DQI) based semi-qualitative method is used for modelling the variability in input parameters. Monte Carlo Simulation is used for uncertainty propagation. The building embodied energy is found to vary from 4 to 9 GJ/m2 based on variation in input data. The mean building embodied energy is found to be 6.3 GJ/m2. The embodied energy intensity of materials is found to cause more variation in building embodied energy in comparison with mass of materials. 'Brick’ is identified as the significant key parameter based on the relative contribution to uncertainty and embodied energy at the building level. Cement and rebar are identified as key parameters. The DQI based methodology presented in this study is useful for Life Cycle Assessment practitioners to evaluate uncertainty in the embodied energy assessment of residential buildings.

A Simplified Framework to Integrate Databases with Building Information Modeling for Building Energy Assessment in Multi-Climate Zones

BIM models are seldom used for the energy certification of buildings. This paper discusses the advantages of linking two important fields: building information modeling (BIM) and building environmental assessment methods (BEAM), presented as a rating system and a proposal for the Chilean context. The state of the art in both fields around the world is discussed, with an in-depth examination of current BIM software and related applications, followed by a discussion about previous research on integrating them. A lack of interoperability and data losses between BIM and BEM were found. A new tool is presented that addresses these challenges to ensure accurate rating system data, and this new framework is based on database exchange and takes crucial information from BIM to BEAM platforms. The development of the method includes BIM programming (API), database links, and spreadsheets for a Chilean building energy certification through a new tool, also applicable to multiclimactic zones. This new semi-automatic tool allows architects to model their design in a BIM platform and use this information as input for the energy certification process. The potential and risks of this method are discussed. Several improvements and enhancements of the energy certification process were found when incorporating this new framework in comparison to current methodologies.

The synergistic effect of multiple design factors on building energy consumption of office blocks: A case study of Wuhan, China

Design factors such as block morphology, building form and building envelope performance are key parameters affecting building energy consumption of urban blocks. However, the synergistic effect of multi-design factors on building energy consumption has not been studied in detail. This paper aims to investigate the synergistic effect of multi-design factors on building energy consumption of office blocks in Wuhan, China. This study employs field survey, satellite map and open street map to extract the typical building forms and construct parametric models of office blocks. Then, 81 representative scenarios considering the synergy of multiple design factors in office blocks are built. Then, based on the Rhino & Grasshopper parametric platform, a building energy assessment tool that considers the synergistic effect of multiple design factors is developed to simulate and record the building energy consumption of each scenarios. Finally, the synergistic effect of multi-design parameters on building energy consumption of office blocks is quantified using correlation and multiple linear regression methods. The findings reveal that the synergistic effect of multi-design factors on building energy consumption is 18.53 %. Building energy consumption for high-rise pavilion typology is saved by 9.64 % than multi-storey courtyard. The key design parameter combinations affecting building energy consumption in office block are building shape factor, solar heat gain coefficient, wall K-value, building width and window-wall ratio of west and east. This study contributes to a deeper understanding of the synergistic effects of multiple design factors on building energy consumption in office blocks and presents a scalable model that can be applied globally to promote sustainable urban development.

Enhancing low-carbon building operations leveraging demand response driven by renewable energy consumption contributions

In the pursuit of sustainable low-carbon energy practices, demand response (DR) has emerged as a pivotal strategy, particularly in the management of building energy consumption. However, current demand management methods often lack a thorough evaluation of building carbon reduction benefits and fall short in promoting grid decarbonization. This paper proposes a low-carbon operation optimization method for buildings, with a novel DR strategy aimed at facilitating local renewable energy consumption and system-level renewables integration. Considering local and systemic renewable shares, an evaluation framework is first introduced for quantitative building energy assessment in each period. The building loads with controllable flexibilities are then grouped and aggregated based on their elasticity characteristics and probability distribution. Finally, a low-carbon optimization model is established, leveraging the DR strategy for sustainable building operations through flexible load regulation. Utilizing measured building data in Shanghai, a case study with extensive analysis validates the effectiveness of the methodology. The results suggest that following DR implementation, the similarity between the building load profile and the systemic renewable share improves by 34.48 %. Moreover, carbon emissions decrease by 5.27 % at the same level of building energy consumption. Further analysis highlights that energy storage can enhance load adjustment capabilities and the potential for reducing carbon emissions. Compared to conventional DR programs, the proposed strategy demonstrates significant advantages of abundant flexibility and low-carbon benefits. The widespread implementation of low-carbon operations in large-scale building groups has profound implications for integrating renewable energy and achieving system decarbonization goals.© 2017 Elsevier Inc. All rights reserved.

Assessing Residential Building Energy Efficiency Using Evolutionary Dendritic Neural Regression

Assessing building energy consumption is of paramount significance in sustainability and energy efficiency (EE) studies. The development of an accurate EE prediction model is pivotal for optimizing energy resources and facilitating effective building planning. Traditional physical modeling approaches are encumbered by high complexity and protracted modeling cycles. In this paper, we introduce a novel evolutionary dendritic neural regression (EDNR) model tailored to forecasting residential building EE. Acknowledging the vast landscape and complexity of the EDNR weight space, coupled with the inherent susceptibility of traditional optimization algorithms to local optima, we propose a complex network-guided strategy-based differential evolution algorithm for training the EDNR model. This strategy adeptly strikes a balance between exploration and exploitation during the search process, significantly enhancing the predictive and generalization capacities of EDNR. To our knowledge, this study represents the inaugural application of dendritic neural regression in real-world prediction scenarios. Extensive experimental findings demonstrate the efficacy of EDNR in accurately predicting building EE with commendable performance. Furthermore, the results of two nonparametric statistical tests affirm the validity and stability of EDNR. Consequently, our proposed methodology exhibits high potential and competitiveness in machine learning applications within the energy domain.

Evaluating the impact of material service life on embodied energy of residential villas in the United Arab Emirates

PurposeRecently, there has been a shift toward the embodied energy assessment of buildings. However, the impact of material service life on the life-cycle embodied energy has received little attention. We aimed to address this knowledge gap, particularly in the context of the UAE and investigated the embodied energy associated with the use of concrete and other materials commonly used in residential buildings in the hot desert climate of the UAE.Design/methodology/approachUsing input–output based hybrid analysis, we quantified the life-cycle embodied energy of a villa in the UAE with over 50 years of building life using the average, minimum, and maximum material service life values. Mathematical calculations were performed using MS Excel, and a detailed bill of quantities with >170 building materials and components of the villa were used for investigation.FindingsFor the base case, the initial embodied energy was 57% (7390.5 GJ), whereas the recurrent embodied energy was 43% (5,690 GJ) of the life-cycle embodied energy based on average material service life values. The proportion of the recurrent embodied energy with minimum material service life values was increased to 68% of the life-cycle embodied energy, while it dropped to 15% with maximum material service life values.Originality/valueThe findings provide new data to guide building construction in the UAE and show that recurrent embodied energy contributes significantly to life-cycle energy demand. Further, the study of material service life variations provides deeper insights into future building material specifications and management considerations for building maintenance.

Sensitivity analysis of multiple time-scale building energy using Bayesian adaptive spline surfaces

Sensitivity analysis has been widely used in building energy analysis to identify key variables influencing building energy and carbon emissions. However, most previous studies only focus on the single time scale energy use of buildings when conducting sensitivity analysis. The multiple time-scale sensitivity analysis would lead to increased complexity and challenges in building energy analysis. This paper proposes a new systematical procedure for global variance-based sensitivity analysis of multiple time-scale energy use of buildings. A case study of an office building is used to demonstrate the application of this approach using the Bayesian adaptive spline surface (BASS) models at three different time scales (annual, monthly, and daily). The results indicate that the procedure proposed here can provide fast and reliable sensitivity results for the multiple time-scale building energy assessment. The BASS learning models have the advantages of high predictive performance and the fast computation of sensitivity indicators in a closed form without Monte Carlo integrals. The application of principal component analysis can deal with the highly correlated multi-output energy use at a smaller time scale, which can further reduce computational costs. Moreover, the sensitivity analysis at the different time scales can provide new insights into energy characteristics due to the intrinsic variations of weather conditions. This systematical procedure can provide useful guidance for the multiple time-scale model calibration and multi-step ahead predictions of buildings. Moreover, the method proposed here can be extended to the sensitivity analysis of energy use in different time scales (sub-hourly, hourly, or daily) for other energy systems (such as PV, solar thermal, and wind).

Building energy characteristic evaluation in terms of energy efficiency and ecology

The assessment of energy efficiency and the environmental impact of building operation, in energy terms, determine to a large extent the energy intensity of the housing sector as a whole and the planning of energy transition measures. In order for these measures to be properly targeted and the building assessment to be reliable, it is necessary to accurately determine the energy demand of the building. The calculation model presented in the article allows for a multivariate assessment of a building's energy demand using static methods. The methodology presented in the article for the determination of energy performance certificates for buildings is complemented by an interpretation regarding the efficiency of the energy systems in the building and the environmental emissions. A model of the existing single-family building was evaluated for this purpose, and subjected to a multivariate analysis depending on the configuration of the heating system. Final and primary energy values, fuel consumption and emissions of pollutants to the environment were determined for each of total twenty-two variants. The analysis carried out and the results obtained show that there is no logical relationship between the primary energy indicator, used for the energy assessment of buildings, and the energy intensity of the building and its environmental impact.

Predicting building energy consumption in urban neighborhoods using machine learning algorithms

Assessing building energy consumption in urban neighborhoods at the early stages of urban planning assists decision-makers in developing detailed urban renewal plans and sustainable development strategies. At the city-level, the use of physical simulation-based urban building energy modeling (UBEM) is too costly, and data-driven approaches often are hampered by a lack of available building energy monitoring data. This paper combines a simulation-based approach with a data-driven approach, using UBEM to provide a dataset for machine learning and deploying the trained model for large-scale urban building energy consumption prediction. Firstly, we collected 18,789 neighborhoods containing 248,938 buildings in the Shanghai central area, of which 2,702 neighborhoods were used for UBEM. Simultaneously, building functions were defined by POI data and land use data. We used 14 impact factors related to land use and building morphology to define each neighborhood. Next, we compared the performance of six ensemble learning methods modeling impact factors with building energy consumption and used SHAP to explain the best model; we also filtered out the features that contributed the most to the model output to reduce the model complexity. Finally, the balanced regressor that had the best prediction accuracy with the minimum number of features was used to predict the remaining urban neighborhoods in the Shanghai central area. The results show that XGBoost achieves the best performance. The balanced regressor, constructed with the 9 most contributing features, predicted the building rooftop photovoltaics potential, total load, cooling load, and heating load with test set accuracies of 0.956, 0.674, 0.608, and 0.762, respectively. Our method offers an 85.5%-time advantage over traditional methods, with only a maximum of 22.75% of error.

The impact of the orientation of family houses on a slope on the environment

The contribution follows on from two previous researches, which dealt with the influence of the orientation of the construction of catalogue family houses from the point of view of CO2 production during their operation. The previous research was focused on family houses (orientation of windows on all sides of the world) and terraced buildings (orientation of windows on two opposite sides of the world). The contribution in question is focused on family houses on a slope with one-sided orientation of the windows, which is the final phase of the research. The main reason for the overall research is the fact that developers are currently trying to realize the same type of family houses (catalogue houses) regardless of which side of the world the exposed glazed areas of the house are oriented. For this reason, the approval and energy certification often do not reach A0, which is a mandatory requirement of the energy assessment of buildings from 1 January 2021. The environmental impact research compares four basic types of streets with ten identical houses. The environmental impact research compares four types of basic streets with ten identical houses. if it is a comparison of the environmental impact of the same development of family houses on a slope, with different orientations to the cardinal points and with different local houses within the streets. The benefit of the research for practice will be the formulation of principles for the orientation of transparent areas in residential construction and the formulation of urban planning principles for the siting of houses in settlements on sloping terrain, where it is possible to place windows only on the front facade.

From Zero Energy to Zero Power Buildings: A new paradigm for a sustainable transition of the building stock

This research introduces an innovative method aimed at evaluating building energy performance within urban environments and communities, departing from the conventional Zero Energy Building (ZEB) concept and introducing the Zero Power Building (ZPB) framework. This approach, overcoming the limitations of the ZEB concept, offers a holistic definition in line with the goal of achieving a Zero-emission Building stock. By employing a power balance assessment coupled with dynamic Key Performance Indicators (KPIs), this methodology surpasses traditional techniques, providing a more accurate assessment of energy consumption and generation patterns. The method was applied to a district case study encompassing seven nearly Zero Energy Buildings (nZEBs), demonstrating its potential efficacy. Findings indicate how the proposed framework can accurately evaluate the integration of energy storage and sharing strategies, increasing the Zero Power target from 29 % to 51 % of annual hours. KPIs were also dynamically examined at the building and district level, introducing the concept of KPI accuracy and identifying pivotal hourly patterns to guide a sustainable transistion. The adoption of the Zero Power Building framework and the utilization of dynamic KPIs offer viable solutions to address conventional limitations in building energy assessments. This approach aids in guiding targeted energy efficiency measures aimed at fostering sustainable and Zero-emission Communities.

Estimating omnidirectional urban vertical wind speed with direction-dependent building morphologies

A main external factor that limits the energy assessment of high-rise buildings in urban areas is the vertical wind conditions. Conventional buildings' energy assessment tools apply a universal vertical wind profile estimation equation in all directions and only consider four typical terrain types. However, the various shape and configurations of buildings in cities can induce the unique wind speed in each direction, and the vertical wind speeds in the same terrain type can be different even in the same city. To get more realistic vertical wind conditions, this study developed an omnidirectional urban vertical wind speed estimation method with direction-dependent building morphologies. A pie-shaped segment method is applied to calculate building morphological features in approaching wind direction. Machine learning methods are developed to calculate parameters of the modified direction-dependent power law urban wind function to convert the Typical Meteorological Year (TMY) wind speed to urban vertical wind speeds. To validate the proposed method, wind tunnel data in Hong Kong are collected for a case study. The results indicate that the proposed method with morphological inputs can effectively estimate the vertical wind speed at different wind directions and heights, and deep neural networks and support vector regression have better performance.

Model simplification of geometry and facilities, for energy and indoor environment towards more reliable energy labelling

Reliable building energy and indoor climate performance assessment require adequate and often time-consuming modelling. Currently, energy performance certification (EPC) is carried out in Member States (MS) using simple steady-state tools. These tools simplify not only building geometry but also HVAC systems, boundary conditions, and building loads. These simplifications can cause the so-called “performance gap”, which is the difference between modelled prediction and actual operation. This creates a lack of trust in the EPCs and scepticism. This paper is scouting toward shifting to dynamic models of different detail levels, considering the zoning, heating and ventilation facilitie’s complexity. In the scope of this dynamic thermal simulation study, a residential multi-apartment building located in Denmark is investigated. The thermal zone simplification has 5 levels of complexity, from modelling each room as a thermal zone to the whole staircase as one thermal zone. The heating system was modelled as: i) ideal loads, ii) electrical radiators, iii) water radiators. Ventilation was modelled as i) zone ventilation, and ii) airflow network. Modelling results are evaluated for heat demand, thermal and atmospheric comfort.

Air infiltration and related building energy consumption: A case study of office buildings in Changsha, China

Past studies reveal that air infiltration through the building envelope and its impact on the indoor environment and energy consumption are significantly influenced by climate characteristics. However, little relevant information is available for buildings in southern China, where the building design traditionally follows a philosophy of being open and shaded. The present study employs both experimental measurements and numerical simulations to investigate the airtightness of buildings in Hot Summer and Cold Winter (HSCW) climate region of southern China and the associated energy consumption. The measurements and simulations are based on a typical office building in Changsha. Measurement results show that the air change rate of six tested spaces at the natural pressure difference ranges from 0.10 to 0.30 h−1 with an average of 0.17 h−1 in summer, and from 0.09 to 0.32 h−1 with an average of 0.16 h−1 in winter. The operation of the air-conditioning system affects largely air infiltration, and each unit change in setpoint air temperature can result in an average of one-third or more change in air infiltration rate. Simulation results show that a decrease in air infiltration rate from 0.17 h−1 to 0.01 h−1 reduces the infiltration-related cooling energy consumption from 14.29 to 0.75 kWh/m2·year and heating energy consumption from 8.20 to 0.39 kWh/m2·year. The same change in the setpoint air temperature of air-conditioning system in summer and winter results in different infiltration-related energy consumption. The findings would contribute to an improved energy simulation and assessment of buildings in southern China.

Predicting the Energy Consumption of Commercial Buildings Based on Deep Forest Model and Its Interpretability

Building energy assessment models are considered to be one of the most informative methods in building energy efficiency design, and most of the current building energy assessment models have been developed based on machine learning algorithms. Deep learning models have proved their effectiveness in fields such as image and fault detection. This paper proposes a deep learning energy assessment framework with interpretability to support building energy efficiency design. The proposed framework is validated using the Commercial Building Energy Consumption Survey dataset, and the results show that the wrapper feature selection method (Sequential Forward Generation) significantly improves the performance of deep learning and machine learning models compared with the filtered (Mutual Information) and embedded (Least Absolute Shrinkage and Selection Operator) feature selection algorithms. Moreover, the Deep Forest model has an R2 of 0.90 and outperforms the Deep Multilayer Perceptron, the Convolutional Neural Network, the Backpropagation Neural Network, and the Radial Basis Function Network in terms of prediction performance. In addition, the model interpretability results reveal how the features affect the prediction results and the contribution of the features to the energy consumption in a single building sample. This study helps building energy designers assess the energy consumption of new buildings and develop improvement measures.

A Review of the Evaluation of Building Energy Code Compliance in the United States

Building energy codes are essential tools for achieving energy efficiency in buildings. However, the full energy savings potential of these codes can only be realized if buildings are constructed in compliance with them. Therefore, evaluating building energy code compliance is crucial in bridging the gap between the energy efficiency requirements set by energy codes and the actualized energy savings achieved. An energy code compliance evaluation serves as a mechanism to assess construction practices, evaluate the effectiveness of code enforcement, identify gaps in compliance, and guide strategies for improvement through training and education. Conducting code compliance evaluation activities involves field studies that require careful design and significant resources. Historically, more emphasis has been placed on developing and adopting building energy codes, while efforts to evaluate compliance have been relatively limited and lacking consistent approaches. The passage of the 2009 American Recovery and Reinvestment Act (ARRA), which mandated that states create plans for achieving 90% compliance within eight years, stimulated the need for an energy code compliance evaluation. As a result, federal, state, and local governments, and utilities have invested in the development of methodologies and tools for code compliance evaluation studies. This paper reviews the code compliance evaluation studies conducted in the United States over the past three decades. It describes and compares the methodologies and metrics used to assess building energy code compliance, summarizes the general elements and steps involved in the evaluation process, and discusses common issues in these studies. Over time, code compliance evaluation methodologies have evolved from isolated development within individual states, regions, and utilities, to widely accepted protocols applicable across different states and local jurisdictions. There has been a transition in compliance metrics, shifting from historical compliance rates to energy-consumption-oriented approaches.