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Energy and environmental problems have attracted attention worldwide. Energy consumption in residential sectors accounts for a large percentage of total consumption. Several retrofit schemes, which insulate building envelopes to increase energy efficiency, have been adapted to address residential energy problems. However, these schemes often fail to balance the installment cost with savings from the retrofits. To maximize the benefit, selecting houses with low thermal performance by a cost-effective method is inevitable. Therefore, an accurate, low-cost, and undemanding housing assessment method is required. This paper proposes a thermal performance assessment method for residential housing. The proposed method enables assessments under the existing conditions of residential housings and only requires a simple and affordable monitoring system of power meters for an air conditioner (AC), simple sensors (three thermometers at most), a BLE beacon, and smartphone application. The proposed method is evaluated thoroughly by using both simulation and experimental data. Analysis of estimation errors is also conducted. Our method shows that the accuracy achieved with the proposed three-room model is 9.8% (relative error) for the simulation data. Assessments on the experimental data also show that our proposed method achieved Ua value estimations using a low-cost system, satisfying the requirements of housing assessments for retrofits.

Based on the European energy directives, the building sector has to provide comfortable levels for occupants with minimum energy consumption as well as to reduce greenhouse gas emissions. This paper aims to compare the impact of climate change on the energy performance of residential buildings in order to derive potential design strategies. Different climate file inputs of Madrid have been used to quantify comparatively the thermal needs of two reference residential buildings located in this city. One of them represents buildings older than 40 years built according to the applicable Spanish regulations prior to 1979. The other refers to buildings erected in the last decade under more energy-restrictive constructive regulations. Three different climate databases of Madrid have been used to assess the impact of the evolution of the climate in recent years on the thermal demands of these two reference buildings. Two of them are typical meteorological years (TMY) derived from weather data measured before 2000. On the contrary, the third one is an experimental file representing the average values of the meteorological variables registered in Madrid during the last decade. Annual and monthly comparisons are done between the three climate databases assessing the climate changes. Compared to the TMYs databases, the experimental one records an average air temperature of 1.8 °C higher and an average value of relative humidity that is 9% lower.

The authors would like to acknowledge the support given by the rest of the members and institutions participating in the REHABILITAGEOSOL (RTC-2016-5004-3) project. The computationalwork has been carried out using the computer facilities of the Extremadura Center for Advanced Technologies (CETA-CIEMAT).

In Europe, more and more data on building energy use will be collected in the future as a result of the energy performance of buildings directive (EPBD), issued by the European Union. Moreover, both at European level and globally it became evident that the real energy performance of new buildings and the existing building stock needs to be documented better. Such documentation can, for example, be done with data-driven methods based on mathematical and statistical approaches. Even though the methods to extract energy performance characteristics of buildings are numerous, they are of varying reliability and often associated with a significant amount of human labour, making them hard to apply on a large scale. A classical approach to identify certain thermal performance parameters is the energy signature method. In this study, an automatised, nonlinear and smooth approach to the well-known energy signature is proposed, to quantify key thermal building performance parameters. The research specifically aims at describing the linear and nonlinear heat usage dependency on outdoor temperature, wind and solar irradiation. To make the model scalable, we realised it so that it only needs the daily average heat use of buildings, the outdoor temperature, the wind speed and the global solar irradiation. The results of applying the proposed method on heat consumption data from 16 different and randomly selected Danish occupied houses are analysed.

Many public social housing building stocks were constructed before the introduction of national thermal regulations, and, as a result, in some situations, energy poverty conditioning during severe winter seasons results in little to no heating habits involving active systems in order to improve building thermal performances. Besides rigorous summer seasons, climate change predictions also indicate rigorous winter seasons will occur that will prevail in some Iberia Peninsula locations, worsening this scenario for this Southern European region. Among others, understanding the extension of discomfort in social housing buildings during heating seasons is therefore essential so as to perceive the suitability of the building stock to deal with present and future climate scenarios. Thus, this article presents a thermal comfort assessment during a winter season period applied to two social housing dwellings located in Covilhã, Portugal, inhabited by elderly residents, under realistic heating habits. An experimental campaign was performed and the results show that discomfort was found to be extremely significant for the majority of the occupied time. Passive means alone and resident heating habits were not enough to achieve proper indoor thermal and humidity conditions, resulting in important losses of well-being to the risk group of the elderly.

Thermal simulations of buildings play a critical role in optimizing energy efficiency, thermal comfort, and heating, ventilation and air conditioning (HVAC) systems design. Accurate weather data is essential for reliable simulations, as local weather and climate have a significant impact on energy requirements for space heating and cooling and thermal comfort. This study conducted a literature review regarding the sources, types, and uncertainties of weather data used for thermal simulations of buildings, including typical meteorological years (TMYs) and extreme weather files under current and future climates. Additionally, this paper evaluates methods for weather data processing, including interpolation, downscaling, and synthetic generation, to improve simulation accuracy. Finally, approaches are proposed for constructing weather files for the future and extreme conditions under a changing climate. This review aims to provide a guide for researchers and practitioners to enhance the reliability of thermal modeling through informed construction, selection, and application of weather data.

Due to their benefits in interior thermal comfort, energy saving, and noise reduction, radiant cooling systems have received wide attention. Radiant cooling systems can be viewed as a part of buildings’ maintenance structure and a component of cooling systems, depending on their construction. This article reviews studies on heat exchange in rooms utilizing radiant cooling systems, including research on conduction in radiant system structures, system cooling loads, cooling capacity, heat transfer coefficients of cooling surfaces, buildings’ thermal performance, and radiant system control strategy, with the goal of maximizing the benefits of energy conservation. Few studies have examined how radiant cooling systems interact with the indoor environment; instead, earlier research has focused on the thermal performance of radiant cooling systems themselves. Although several investigations have noted variations between the operating dynamics of radiant systems and conventional air conditioning systems, the cause has not yet been identified and quantified. According to heat transfer theory, the authors suggest that additional research on the performance of radiant systems should consider the thermal properties of inactive surfaces and that buildings’ thermal inertia should be used to coordinate radiant system operation.

The aim of this study was to elaborate and validate a reduced order model able to forecast solar heat gains as a function of the architectural parameters that determine the solar heat gains. The study focused on office buildings in Catalonia and Spain and their physical values were taken from the Spanish Building Technical Code and European Union Directive 2018/844. A reduced order model with three direct variables (solar heat gain coefficient, shade factor, window to wall ratio) and one indirect design variable (building orientation) was obtained and validated in respect to the International Performance Measurement and Verification Protocol. Building envelope properties were fixed and the values were taken from the national standards of Spain. This work validates solar heat gain coefficient as a primary variable in determining the annual solar heat gains in a building. Further work of developed model could result in building energy need quick evaluation tool in terms of solar heat gains for architects in building early stage as it has an advantage over detailed building simulation programs in terms of instant calculation and the limited need for predefined input data.

Accurate solar radiation data are essential for reliable building energy simulations, particularly for policy making. However, uncertainty in solar input, especially in regions with limited ground-based measurements, can significantly affect simulation outcomes. This study investigates the impact of different solar radiation datasets on building energy performance simulations across two climatically distinct years, 2015 and 2024, in a subtropical urban environment. Solar inputs from ERA5, CAMS, and NASA POWER were compared against a regional reference from the Brazilian National Institute for Space Research (INPE). In addition to energy simulations, the datasets were evaluated using statistical metrics including root mean square error (RMSE), mean bias error (MBE), and Pearson correlation. NASA POWER showed the best agreement with ground data and yielded simulation results that were reasonably aligned with observed cooling loads and thermal comfort in both years, with slight overestimations in cooling demand and overheating hours. CAMS maintained consistent performance across both years and produced the lowest cooling and overheating estimates, slightly underestimating results while closely matching monthly trends. ERA5 exhibited the largest positive bias in solar input, particularly in DNI, leading to substantial overestimation of cooling demand, up to 34% in 2024, especially during heatwaves. These discrepancies highlight the sensitivity of energy simulations to solar input selection and the importance of using validated high-quality datasets to ensure reliable modeling under increasing climate variability.

In building thermal energy characterisation, the relevance of proper modelling of the effects caused by solar radiation, temperature and wind is seen as a critical factor. Open geospatial datasets are growing in diversity, easing access to meteorological data and other relevant information that can be used for building energy modelling. However, the application of geospatial techniques combining multiple open datasets is not yet common in the often scripted workflows of data-driven building thermal performance characterisation. We present a method for processing time-series from climate reanalysis and satellite-derived solar irradiance services, by implementing land-use, and elevation raster maps served in an elevation profile web-service. The article describes a methodology to: (1) adapt gridded weather data to four case-building sites in Europe; (2) calculate the incident solar radiation on the building facades; (3) estimate wind and temperature-dependent infiltration using a single-zone infiltration model and (4) including separating and evaluating the sheltering effect of buildings and trees in the vicinity, based on building footprints. Calculations of solar radiation, surface wind and air infiltration potential are done using validated models published in the scientific literature. We found that using scripting tools to automate geoprocessing tasks is widespread, and implementing such techniques in conjunction with an elevation profile web service made it possible to utilise information from open geospatial data surrounding a building site effectively. We expect that the modelling approach could be further improved, including diffuse-shading methods and evaluating other wind shelter methods for urban settings.