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The energy consumption of existing buildings is not only affected by their physical features but also by their business activities (e.g., operating hours, number of workers, and climate). Energy Star’s energy efficiency ratio (EER) is a key energy performance indicator that has been used for more than 20 years. This method normalizes operation characteristics by calculating the estimated energy consumption of business activities using regression models and comparing it with the actual energy consumption. However, EER-based assessment is limited by the lack of information regarding the reasons for the evaluation results. This study proposes a balanced method for explaining the reasons underlying energy efficiency levels while maintaining the existing EER assessment system. The method constitutes data collection, EPI derivation, and energy performance assessment, utilizing the parameters of the change-point linear model (CPM) as an additional EPI to provide descriptive information. The results are summarized to provide a checklist guide for retrofitting, and additional energy saving potential for buildings with low and high scores can be identified by comparing the EER scores and CPM parameters. The proposed method shows that it is possible to interpret the energy efficiency assessment results by comparing CPM parameters, while maintaining the EER score.

The assessment of buildings’ energy performance plays a critical role in achieving global sustainability goals, particularly in reducing carbon emissions and improving energy efficiency. In this context, various modelling approaches have been developed to evaluate building energy performance. Among them, data-driven models, such as machine learning (ML) algorithms, have gained significant attention in recent years due to their scalability, fast development process, and high predictive accuracy. However, a key limitation of these models is their limited interpretability, which can negatively affect their application particularly in decision-making and retrofit planning processes. To address this issue, SHapley Additive exPlanations (SHAP) has emerged as a promising approach for interpreting complex ML models by quantifying the contribution of each input feature to the model’s predictions. As a result, this study developed an XGBoost ML model that predicts energy performance of residential buildings in the UK with an R2 value of more than 0.98. After that, SHAP method was applied to explore and explain the effect of individual features on model outcomes, which highlighted that SHAP framework can be a strong complementary approach for enhancing the interpretability and practical applicability of black-box models in building energy performance analysis.

This study investigates the effectiveness of passive design in low-rise residential buildings located in arid desert climates, using the Dubai Solar Decathlon Middle East (SDME) competition as a case study. This full-scale experiment offers a unique opportunity to evaluate design solutions under controlled, realistic conditions; prescriptive, modeled performance; and monitored performance assessments. The prescriptive assessment reviews geometry, orientation, envelope thermal properties, and shading. Most houses adopt compact forms, with envelope-to-volume and envelope-to-floor area ratios averaging 1 and 3.7, respectively, and window-to-wall ratios of approximately 17%, favoring north-facing openings to optimize daylight while reducing heat gain. Shading is strategically applied, horizontal on south façades and vertical on east and west. The thermal properties significantly exceed the local code requirements, with wall performance up to 80% better than that mandated. The modeled assessment uses Building Energy Models (BEMs) to simulate the impact of prescriptive measures on energy performance. Three variations are applied: assigning minimum local code requirements to all the houses to isolate the geometry (baseline); removing shading; and applying actual envelope properties. Geometry alone accounts for up to 60% of the variation in cooling intensity; shading reduces loads by 6.5%, and enhanced envelopes lower demand by 14%. The monitored assessment uses contest-period data. Indoor temperatures remain stable (22–25 °C) despite outdoor fluctuations. Energy use confirms that houses with good designs and airtightness have lower cooling loads. Airtightness varies widely (avg. 14.5 m3/h/m2), with some well-designed houses underperforming due to construction flaws. These findings highlight the critical role of passive design as the first layer for improving the energy performance of the built environment and advancing toward net-zero targets, specifically in arid desert climates.

Over the years, soil loosening, as a primary form of soil tillage, has been performed with machines of various construction characteristics that usually work at a depth of 0.40 m or sometimes even greater depths. These machines include cultivators for soil loosening, machines for deep soil loosening, and subsoilers. In Bulgaria, the Maschio-Gaspardo subsoiler, model Artiglio 300/7, has been successfully used for three years to perform this technological operation. This machine is aggregated with tractors with a nominal towing capacity of 50 kN, such as John Deere 8R280 tractors. The efficient use of this machine-tractor aggregate required a thorough energy-consumption study using the tensiometer method. The paper presents the study findings, which show that the tractor chosen for this newly formed aggregate is suitable due to its towing and power capacity. Its maximum towing resistance when performing soil loosening reaches 36.40 kN, while the normal indicator for this tractor is 50.00 kN. The maximum power consumption can reach up to 208.78 kW, with an engine load factor of 92.01% and a permissible wheelspin of a maximum of 14.9%. This machine-tractor aggregate is properly sized in terms of power consumption and ensures optimum towing and speed operation when carrying out the soil conservation method of soil loosening as main tillage on agricultural land with calcic chernozem soil.

Existing buildings are likely to consume more energy and emit more greenhouse gases than new buildings because of inevitable deteriorations in physical performance. Accordingly, retrofitting of existing buildings is considered essential to reduce energy consumption and greenhouse gas emissions from the building sector. However, assessing the energy performance of existing buildings accurately has limitations because building materials undergo physical deterioration and the actual operational conditions differ from as-built documentation. There is also a difference in the level of data acquisition required for building energy performance assessment depending on the conditions of the building. The aim of this paper is to present types of methods for energy performance assessment of existing buildings considering this data acquisition level. We analyzed various assessment methods, which were classified into three prototypes of methods according to the required level of data acquisition. Type 1 assessed the target building based on literature sources. Type 2 conducted on-site audit and assessed the target building based on additional collected data. Type 3 assessed the target building by further estimating the building properties through analysis of the measured energy data. The applicability of the proposed methods were demonstrated using case studies of three buildings located in Seoul, South Korea.

Public buildings consume the largest proportion of total energy consumption and carbon dioxide emissions in the building sector of China. Enhancing building energy efficiency becomes a necessary way to reduce greenhouse gas emissions and energy waste. In this study, 10-year real data from 2000 buildings covering five different types of public buildings in a hot summer and cold winter zone in eastern coastal China were investigated to analyze energy-saving potential (ESP) and the impacts of various influencing factors on building energy performance. The concept of energy consumption limit (ECL) was proposed and used for identifying high energy-consuming buildings and the calculation of ESP. Hotels, shopping malls, and office buildings were the top three types of buildings with high ESP. ESP for the high energy-consuming buildings based on the benchmark limits was more than 18%, and that for three-star hotels and shopping malls was 51%. In addition, a correlation analysis between energy consumption and influencing factors was carried out, which laid the foundation for the development of building energy performance assessment and diagnosis tools.

In the Republic of Bulgaria, in the last few years, a DeltaRow technology of staggered sowing of the seeds of hoeing crops such as maize for grain and sunflower has been successfully applied in some agricultural regions. An Azurit 10.8 seed drill is used to implement the staggered sowing technology. This precision seeder is aggregated with tractors with a nominal towing resistance of 30 kN (New Holland T6.165). The efficient use of this machine-tractor aggregate required a comprehensive study of its energy consumption by applying the tensiometer method. This paper discusses the scheme of the main procedures of the experiment conducted, the order and method of determining the relevant energy indicators, and the results of this study. The findings demonstrate that the chosen energy source in the newly assembled machine-tractor unit is appropriately chosen due to its towing and power capabilities because the maximum towing resistance, when sowing a field after pre-sowing treatment, reaches up to 12.1 kN, while the nominal one of this tractor is 30 kN. In addition, its maximum power consumption reaches up to 56.98 kW, with an engine load factor of 53.25% and acceptable wheelspin of up to 6.3% in the operating speed range of 2.3–3.4 m·s . In addition, the researched machine-tractor aggregate in terms of energy consumption ensures an optimum towing and speed of operation when carrying out the technological operation of staggered sowing with paired sown rows of hoeing crops on the agricultural lands of the Republic of Bulgaria with calcic chernozem.

This study investigates a Proxy-Calibration method for modeling Variable Refrigerant Flow (VRF) systems in TRNSYS, addressing the absence of a dedicated simulation component. The approach approximates part-load behavior through indoor-unit combination mapping, utilizing empirical data from a public office building in Seoul. Simulation results were compared with one year of monitored data. While indoor temperature trends showed moderate agreement (R2 = 0.68), electricity consumption diverged significantly from actual measurements. The coefficient of variation in the root mean square error (CVRMSE) ranged from 95% to 118% for the boiler and 153% to 590% for the VRF system, indicating a substantial discrepancy well beyond standard calibration thresholds. These findings underscore the limitations of using static performance maps without explicit control logic. Consequently, this study defines the proposed method as an exploratory investigation; while it establishes a procedural framework for approximating VRF operation, rigorous energy prediction requires further refinement through empirical curve fitting and detailed control representation.

Based on the statistical analysis of operational energy consumption and its influential factors from data of 599 Chinese WWTPs in 2006, it is noticed that the most influential factors include treatment technology adopted, treated sewage amount, removed pollutants amount, etc. Using the conclusion above, this paper sets up an integrated system of operational energy performance assessment for municipal wastewater treatment plants. Combining with result from on-spot research and model simulation, the calculating method of benchmark value and score of 7 energy efficiency indicators grouped into 3 levels is stated. Applying the assessment system to three plants, its applicability and objectivity are proved and suggestions to improve energy performance are provided.

Renovation Wave aims to boost the uptake of deep renovation towards the CO2 emission targets for 2030. In this perspective, there is the need of technologies and solution sets for improving the deep renovation process as well as demonstrating the performances for supporting the stakeholders in the decision-making process. To cope with the issue, this work presents a methodology for setting up a repository of building deep renovation packages that integrates industrialised facade technologies and more traditional solutions. The performances feeding into the repository have been evaluated by means of transient detailed simulations on a set of reference buildings in representative European climate conditions. The renovation packages are evaluated in terms of key performance indicators dealing with five areas: energy, comfort, pollutant emissions, cost, and renovation time. The defined repository includes 289 assessed technology packages and associated performances across Europe, providing a comprehensive support to identify the most effective solutions according to the user needs. The paper presents the application of the repository with two examples of stakeholders’ decision-making paths for selecting the deep renovation packages according to different priorities and expected targets.