Buildings have the potential to mitigate climate change effects by integrating energy-efficient solutions. Building resilient design strategies based on forthcoming weather predictions offers an ...effective means of adaptation. The study focuses on the impact of climate change on residential buildings in Istanbul and Izmir, two Turkish cities with distinctive climate characteristics. By creating future weather scenarios and conducting dynamic simulations, buildings’ and improvement measures’ performance under RCP 4.5 and RCP 8.5 climate scenarios and short- and long-term periods are evaluated. The findings reveal varying degrees of climate change impact on the two regions, with decreased heating degree days (HDDs) and increased cooling degree days (CDDs). Notably, the RCP 8.5 scenario projects significant temperature increases, with a rise of 4.3 °C in Istanbul and 5 °C in Izmir, leading to profound consequences for buildings. The CDD can be doubled in July and reach 292 in Izmir and quadrupled, reaching 158 in Istanbul. Without retrofit, in a well-naturally ventilated building, primary heating energy consumption can be decreased by 36–41 %, while primary cooling energy consumption tripled in both cities. With the aid of improvements, a ∼ 5–6 °C decrease was observed in the highest temperature predictions in naturally ventilated spaces in summer in Istanbul, while a ∼ 4–5 °C decrease was observed in Izmir.
•Fenestration & shading devices influence building cooling and heating performance.•Comparison of vertical and horizontal shading numbers, depths, and tilt angles.•Zone solar gain and ventilation ...vary with varied fenestrations and shading devices.•Brute-force parametric & Monte Carlo sensitivity studies of building performances.
Consideration of reducing energy consumption and improving occupant comfort are crucial in sustainable building designs and retrofitting. In the built environment, fenestration and shading device (F&SD) installations are common strategies applied in buildings to minimize solar heat gains towards reducing cooling and overall energy. The influence of F&SD strategies on building performance is contingent upon their designs; however, existing research does not provide performance trends and distributions of F&SD with different configurations. This study investigated the influence of varied F&SD configurations on the ventilation and energy performance of an office unit in a building in Shanghai using brute-force parametric analysis and Monte Carlo sensitivity analysis. The evaluated strategies included window-facing orientation, window-to-wall ratio, shading device types, number of shadings, shading device depths, and shading tilt angles. The results show that changes in F&SD configurations resulted in reductions in solar gains, winter natural ventilation loss, and summer natural ventilation gains by up to 93.8 %, 80.2 %, and 75.6 %, respectively. For all F&SD configurations investigated, the difference between the maximum and minimum zone temperatures for summer was 1.39 °C and for winter, 1.21 °C. Heating energy demands increased up to 0.75 %; besides, cooling energy reductions were 3.03 % and 2.7 % for horizontal and vertical shading devices respectively. This study’s findings can aid building designers in comprehending the energy and ventilation performance of varied F&SD configurations and provide insights and references for sustainable design processes.
•Local performance indicators are aggregated to management system indicators.•Performance deviations on site level are allocated to plants or plant sections.•The aggregation method facilitates the ...verification of energy efficiency improvements.•The method is demonstrated on an industrial use case.•The aggregated indicators fulfill the requirements of ISO 50001:2018.
The mitigation of the climate change requires a significant reduction of the fossil energy consumption in all industrial sectors. The implementation of formalized management systems supports the industry to continuously improve the energy performance which is measured using so called “Energy Performance Indicators”. One essential requirement for the evaluation is the correction of these indicators and the corresponding baselines by the influences of external static or dynamic factors, e.g. the ambient conditions, the product spectrum or the plant load. This is in particular difficult for large integrated production sites as e.g. in the chemical industry. In this contribution, an aggregation method is proposed to exploit the analysis of the factors on lower hierarchical layers for the evaluation of the performance of an aggregated domain. Thereby, the resulting aggregated indicator and the corresponding baseline consider all the identified factors from the lower layers, which facilitates the analysis and allocation of possible savings potentials. The concept is applied exemplarily to an integrated chemical production site and the contributions of each plant to the deviation of the energy performance of the site are analyzed. The method facilitates the verification of the improvement of the energy performance as required by ISO 50001:2018 and helps decision makers to prioritize investments in energy efficiency projects. The results can be used for discussions with policy-makers, certification bodies and other stakeholders on energy efficiency targets.
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•Energy performance certificate (EPC) to c contribute in defining a energy poverty index.•Mapping building energy poverty risks to support policy actions for tackling energy ...poverty.•Merging GIS tool and EPC database to obtain city energy poverty maps at city (Bologna) level.
Energy poverty is defined as the condition in which low-income people is no more able to face the costs of energy bills and consequently accept to live in cold and uncomfortable houses. In recent years the scientific literature about this specific issue has registered a significant growth, despite the problem is mainly approached from an econometric point of view to which instead medical, social and energy aspects have to be coupled. Recent EU policies like the Clean Energy Package and the Energy Building Performance Directive Recast III encourage to increase the efforts to tackle and eradicate the energy poverty.
The paper approaches the problem focusing on one of its complementary causes: the building energy performance. The adopted methodology includes the calculation of the energy costs for each household, the definition of the energy poverty threshold, the calculation of the related building energy performance limit, otherwise leading to the energy poverty condition. These data, associated with the energy performance certificates, are used to create a GIS based mapping of the buildings potentially affected by energy poverty. Maps can be used to support decision making process in addressing appropriate strategies at urban level to tackle the energy poverty risk. The paper includes a case study in the city of Bologna where the proposed methodology is tested.
The “human dimensions” of energy use in buildings refer to the energy-related behaviors of key stakeholders that affect energy use over the building life cycle. Stakeholders include building ...designers, operators, managers, engineers, occupants, industry, vendors, and policymakers, who directly or indirectly influence the acts of designing, constructing, living, operating, managing, and regulating the built environments, from individual building up to the urban scale. Among factors driving high-performance buildings, human dimensions play a role that is as significant as that of technological advances. However, this factor is not well understood, and, as a result, human dimensions are often ignored or simplified by stakeholders. This paper presents a review of the literature on human dimensions of building energy use to assess the state-of-the-art in this topic area. The paper highlights research needs for fully integrating human dimensions into the building design and operation processes with the goal of reducing energy use in buildings while enhancing occupant comfort and productivity. This research focuses on identifying key needs for each stakeholder involved in a building’s life cycle and takes an interdisciplinary focus that spans the fields of architecture and engineering design, sociology, data science, energy policy, codes, and standards to provide targeted insights.
Greater understanding of the human dimensions of energy use has several potential benefits including reductions in operating cost for building owners; enhanced comfort conditions and productivity for building occupants; more effective building energy management and automation systems for building operators and energy managers; and the integration of more accurate control logic into the next generation of human-in-the-loop technologies. The review concludes by summarizing recommendations for policy makers and industry stakeholders for developing codes, standards, and technologies that can leverage the human dimensions of energy use to reliably predict and achieve energy use reductions in the residential and commercial buildings sectors.
Performance gap between predicted and measured building energy use can be caused by simplifications and assumptions introduced in the building energy modeling process, uncertainties in building ...operation such as heating and cooling setpoint temperature, operational hours, occupant behavior, heat generated from lights and equipment, etc. In South Korea, the building energy efficiency certification system (BEEC) evaluates annual energy use intensity (EUI, kWh/m2/yr) and assigns ten certificate levels using ECO2, a EUI calculation tool developed by the Korean government that is based on ISO 52016 and DIN V 18599. Firstly, we collected information on 158 non-residential BEEC-certified buildings and their actual energy usage data. Subsequently, we analyzed the performance gap between predicted and measured EUI for 158 non-residential buildings in terms of building types, heating and cooling degree days, chiller types, gross floor area, window-wall ratio, and envelope U-value. The study reveals that the performance gap is significant, showing mean absolute error ranging from 21.3 to 417.4 kWh/m2/yr, mean biased error from −3.2 to 73.4 %, and the coefficient of variance of the root mean squared error from 57.0 to 304.9 %, respectively. It is also noteworthy that even individual building’s annual consumption during eight years (2014–2021) is annually fluctuating, indicating that even the performance gap is a moving target. This study reassures that building energy certification in the design stage should be understood as a rating, not for a prediction because of many unpredictable and stochastic behavior of building energy systems and occupants.
Ventilated façades can reduce heat gains through the opaque envelope of buildings, and consequently help to lower the cooling energy demand and the relative greenhouse gas emissions. However, the ...influence of the design features and climatic variables on their energy performance is not known enough.
In this article, the influence of different parameters of the ventilated façade has been assessed. The cladding material, the relative position between mass and thermal insulation in the main wall, the air cavity geometry, and the open/closed joint configurations have been evaluated through a numerical calculation with a model that considers all these parameters, validated with experimental data.
It has been observed that, in summer conditions, the best strategy to prevent heat gains is to block the energy in the outermost layers. This suggests adopting non-thermal conductor materials for claddings and the insulation of the main wall on the outer layer. Higher cavities imply a reduction of the ventilation benefits; the air remains more time in the cavity, and thus heat fluxes per unit façade area increase. On the contrary, lower air cavities allow more fresh air entrances from outside, as occurs for open joint claddings, reducing net heat gains. Additionally, widening the air cavity, up to 10 cm, results in lower average heat flux.
All these different façade configurations are compared in a cradle-to-gate environmental impact assessment demonstrating that the lowest energy-demanding solution during the service life might not be the best one in the whole life cycle, thus a deeper study is needed.
The considerable amount of energy utilized by buildings has led to various environmental challenges that adversely impact human existence. Predicting buildings' energy usage is commonly acknowledged ...as encouraging energy efficiency and enabling well-informed decision-making, ultimately leading to decreased energy consumption. Implementing eco-friendly architectural designs is paramount in mitigating energy consumption, particularly in recently constructed structures. This study utilizes clustering analysis on the original dataset to capture complex consumption patterns over various periods. The analysis yields two distinct subsets that represent low and high consumption patterns and an additional subset that exclusively encompasses weekends, attributed to the specific behavior of occupants. Ensemble models have become increasingly popular due to advancements in machine learning techniques. This research utilizes three discrete algorithms, namely Artificial Neural Network (ANN), K-nearest neighbors (KNN), and Decision Trees (DT). In addition, the application employs three more machine learning algorithms bagging and boosting: Random Forest (RF), Extreme Gradient Boosting (XGB), and Gradient Boosting Trees (GBT). To augment the accuracy of predictions, a stacking ensemble methodology is employed, wherein the forecasts generated by many algorithms are combined. Given the obtained outcomes, a thorough examination is undertaken, encompassing the techniques of stacking, bagging, and boosting, to conduct a comprehensive comparative study. It is pertinent to highlight that the stacking technique consistently exhibits superior performance relative to alternative ensemble methodologies across a spectrum of heterogeneous datasets. Furthermore, using a genetic algorithm enables the optimization of the combination of base learners, resulting in a notable enhancement in prediction accuracy. After implementing this optimization technique, GA-Stacking demonstrated remarkable performance in Mean Absolute Percentage Error (MAPE) scores. The improvement observed was substantial, surpassing 90 percent for all datasets. In addition, in subset-1, subset-2, and subset-3, the achieved R2 scores were 0.983, 0.985, and 0.999, respectively. This represents a substantial advancement in forecasting the energy consumption of residential buildings. Such progress underscores the potential advantages of integrating this framework into the practices of building designers, thereby fostering informed decision-making, design management, and optimization prior to construction.
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•Due to the COVID-19, rising residential energy consumption has become a challenge.•Applying k-means clustering to detect patterns, emphasizing various demand periods.•Optimizing model performance using Recursive Feature Elimination with Random Forest.•Identifying the best ensemble technique for practitioners in a specific domain.•Refining ensemble models with genetic algorithms significantly boosts accuracy.
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•Data-driven energy quantification of 25,000 residential buildings with XAI.•Quantifying explainability and accuracy in predicting building energy consumption.•Evaluating the ...human-centered explainability with an extensive survey.•Applying appropriate XAI methods fosters more informed retrofit decisions.•Results encourage using XAI methods for data-driven energy quantification methods.
Accurate predictions of building energy consumption are essential for reducing the energy performance gap. While data-driven energy quantification methods based on machine learning deliver promising results, the lack of Explainability prevents their widespread application. To overcome this, Explainable Artificial Intelligence (XAI) was introduced. However, to this point, no research has examined how effective these explanations are concerning decision-makers, i.e., property owners. To address this, we implement three transparent models (Linear Regression, Decision Tree, QLattice) and apply four XAI methods (Partial Dependency Plots, Accumulated Local Effects, Local Interpretable Model-Agnostic Explanations, Shapley Additive Explanations) to an Artificial Neural Network using a real-world dataset of 25,000 residential buildings. We evaluate their Prediction Accuracy and Explainability through a survey with 137 participants considering the human-centered dimensions of explanation satisfaction and perceived fidelity. The results quantify the Explainability-Accuracy trade-off in building energy consumption forecasting and how it can be counteracted by choosing the right XAI method to foster informed retrofit decisions. For research, we set the foundation for further increasing the Explainability of data-driven energy quantification methods and their human-centered evaluation. For practice, we encourage using XAI to reduce the acceptance gap of data-driven methods, whereby the XAI method should be selected carefully, as the Explainability within the methods varies by up to 10 %.
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