Weather Files Research Articles

Comparison of future weather files for Brazilian cities

Comparison of future weather files for Brazilian cities

Creating granular climate zones for future-proof building design in the UK

Climate zones play an important role in promoting climate responsive building design and implementing climate-specific prescriptions in national building standards and regulations. The existing studies on climate zoning are subject to several limitations, i.e. the incapability of distinguishing microclimates and the lack of consideration of climate change. In this research, we propose a two-tiered ensemble clustering method for the identification of granular climate zones using the projections of future climate. The first tier identifies primary climate zones using a combination of climatic features and geographical locations, whereas the second tier identifies distinct local variations within each primary climate zone using the temperature related features. The proposed ensemble clustering model is applied to the UK to create a mapping of granular climate zones for future proofing building design. The method identified 14 distinct primary zones and distinguished microclimates at a range of scales from large urban areas, such as the Greater London Area, to national parks, such as Dartmoor and the Pennines. The identified mapping resolves two major obstacles in the creation and usage of weather data for building performance assessment in the UK, i.e. the lack of guidance for selecting weather files, and the absence of scientific rationale for representing the UK climate using the current 14 locations.

Simulation and performance assessment of a heat pump coupled to a sub-slab thermal storage system

The paper explores the performance of a sub-slab ground heat exchanger, coupled to a heat pump, and the ability of the system to provide space conditioning and domestic hot water (DHW) for a multi-unit residential building (MURB) located in Ottawa, Ontario. The parametric analysis conducted to optimize the design of the building, as well as the sensitivity analysis performed to optimize the configuration of the sub-slab ground heat exchanger is also presented. According to the simulated results, the ground layer can store some of the rejected energy over medium term, without reducing the long-term performance of the heat pump. The annual energy consumption per dwelling unit (including heat pump, all auxiliary equipment, appliances, and lights) was estimated to be 28.4 GJ, significantly lower than the average annual energy consumption for a low-rise apartment in Canada (46.7 GJ per apartment). To assess the sensitivity of the system to environmental conditions, the simulation was conducted using the weather files for other five locations in Canada.

Effect of different generated weather climate data file on building energy consumption in different climates of Iran

Accurate estimation of building energy consumption is critical, and it can usually be calculated using simulation software like Energy Plus. The input weather data for software is typically a typical or reference year as a standard weather file, such as TMY2. To best estimate building energy consumption, TMY2 data must be generated intelligently. In the present study, first, several TMY2 data files have been generated from different periods (5–21 years) of available measured weather data (9 separate files). Available measured weather data is for 1998 to 2019. Also, a TMY2 file has been generated from the long-term average for the total period (1998–2019), and two TMY2 files have been generated using the CCWeatherGen software for 2020 and 2050. In the next step, the calculated building energy consumption from those files for an educational building will be compared using Energy Plus software. The primary purpose of this study is to investigate the impact of different generated TMY2 (from different weather data periods) on energy consumption and determine the best period to generate TMY2 files from the available period of 5 to 21 years for the capital of Iran (Tehran), which has a semi-arid climate, and Tabriz, which has a cold and mountainous climate. These cities have different climates. Results show that for Tehran, building heating energy consumption changes by about 10% from other generated TMY2 files (one file generated from 2009 to 2019 and another generated from the 2007 to 2019 period). The result shows that generating TMY2 files from different weather data periods affects building energy consumption. So, a suitable strategy for generating TMY2 files is necessary. As a new strategy, the present study proposes that TMY2 files should be generated based on building cooling and heating energy from recent years. Also, because climate change is an important parameter, a TMY2 file should be generated from recent years’ weather data to accurately predict building energy consumption.

A spatial mapping of thermal comfort and air quality (SMTC-AQ) framework for the built environment using computational fluid dynamics approach

Indoor environmental quality (IEQ), driven by thermal comfort and indoor air quality (IAQ), is a major factor that impacts occupant's well-being and productivity since people spend 80–90 % of their time indoors. The current approaches for assessing and quantifying IEQ presume fixed values and ranges in naturally ventilated (NV) spaces within the simulation environment. However, such an approach cannot capture point-to-point changes that occur in the built environment due to localized variations of environmental variables such as air velocity and temperature. This study proposes a sensing and simulation-based framework, Spatial Mapping of Thermal Comfort and Air Quality (SMTC-AQ) in a computational fluid dynamics (CFD) environment to address this. Salient characteristics of SMTC-AQ include the modification of predicted mean vote (PMV) for NV spaces, specifically for the Indian context, and the implementation of the under-explored concept of domain-decoupling utilizing the ISHRAE weather file. Using SMTC-AQ, the study determines the optimal window-to-wall ratio (WWR) and opening-to-wall ratio (OWR) for a case study room in an Indian academic building, considering both IAQ and thermal comfort. In the study, indoor air velocity and temperature are first validated with field data, followed by a scenario analysis to assess the impact of opening characteristics on IEQ. The findings show that for building spaces in tropical climates with moderate outdoor temperatures and lower relative humidity, thermal comfort, and IAQ can be improved by implementing higher WWR and OWR. Moreover, WWR is a more relevant criterion for improving thermal comfort and a better OWR design is crucial for enhancing IAQ in NV spaces.

CBE Clima Tool: A free and open-source web application for climate analysis tailored to sustainable building design

Climate-responsive building design holds immense potential for enhancing comfort, energy efficiency, and environmental sustainability. However, many social, cultural, and economic obstacles might prevent the wide adoption of designing climate-adapted buildings. One of these obstacles can be removed by enabling practitioners to easily access, visualize and analyze local climate data. The CBE Clima Tool (Clima) is a free and open-source web application that offers easy access to publicly available weather files and has been created for building energy simulation and design. It provides a series of interactive visualizations of the variables contained in the EnergyPlus Weather Files and several derived ones like the UTCI or the adaptive comfort indices. It is aimed at students, educators, and practitioners in the architecture and engineering fields. Since its inception, Clima’s user base has exhibited robust growth, attracting over 25,000 unique users annually from across 70 countries. Our tool is poised to revolutionize climate-adaptive building design, transcending geographical boundaries and fostering innovation in the architecture and engineering fields.

Statistical analysis of the assessment of wind potential in the territory of the city of Kragujevac

Knowing the vector characteristics of the wind (direction, head, and intensity) is a prerequisite for determining the wind potential of a location. Based on the assessment of the wind potential, further analyses and research are carried out. Only when all the required conditions are met, the last two phases are reached: implementation (physical realization, i.e. installation) and exploitation (commissioning and use) of the wind generator. In this work, a statistical methodology was applied to examine the possibility of installing and using, primarily, vertical wind generators. Based on a one-year weather file (with a one-hour time step), characteristic diagrams (histogram of wind speed, wind rose, probability density function, cumulative distribution function, and height profile of wind speed) were formed for the assessment of the wind potential of the urban area of the city of Kragujevac. The proposed methodology can be used to assess the wind potential of any location, both urban and rural.

Numerical investigation of mould growth risk in a timber-based facade system under current and future climate scenarios

Due to today’s sustainability concerns, we must find ways to decrease, the CO2 footprint of the products and systems that are used in buildings. The StaticusCare project, funded by EEA/Norway Grants, emerged from this need, since it aims to decrease the greenhouse gas emissions associated to the construction industry by developing a hybrid timber and aluminium based facade system that will integrate IoT sensors. A decrease of the facade’s embodied CO2 footprint of 70-75 % and of the non-renewable energy consumption to 53-56 % is expected. However, it must be ensured that this system has the necessary quality to be installed in the Nordic climates, even for future conditions. Computational models, both at the assembly level as well as at the building level, will be developed. The first level model will allow to perform thorough hygrothermal analysis of the facade system, whilst the second level model will allow to analyse the effect of the system at the building level, i.e. in terms of energy and indoor environment. In addition, this second level model will also be used to obtain the indoor conditions – i.e. temperature and relative humidity – that the first level model needs as an input to run. In order to make this study more comprehensive and, at the same time, determine how differently the facade system will perform under different climates, several representative Nordic outdoor climates will be used in the simulations. Climate change, by means of outdoor weather files, was considered, as well as the effect of moisture penetrating in the tested facade system due to air infiltration or even using timber with a high moisture content due to unprotected storage. It is determined that the tested system is able to properly dry the excess of moisture under current and future conditions. However, if the timber is not properly protected from moisture, then the situation is completely different.

A revised morphing algorithm for creating future weather for building performance evaluation

Climate change is one of the greatest challenges the building industry faces. Engineers and architects require representative future weather data if they would like to see how their buildings and designs will fare under a changing climate. The most common method used to create future weather involves manipulating observations commonly known as morphing, but the most used algorithms can create implausible weather conditions due to their unbounded nature. Here, bounded morphing algorithms will be described and their effectiveness proved mathematically. The improved bounded method applies two additional conditions on the morphed distribution to the maximum and minimum values, in addition to the mean values. The benefits over the standard approach will also be illustrated considering the changes in the distribution of temperature and solar irradiation due to climate change. The improved algorithms outperform the standard morphing procedures in terms of preserving the underlying climate signal while not creating unrealistic or implausible weather conditions. This method should give engineers confidence that the generated future weather series are more robust and representative of potential future weather. Practical application: The use of future weather to inform building design is now commonplace within the industry. Reliable weather files are crucial to support and deliver strategies for decarbonisation and adaptation to climate change in the built environment and the wider industry. This article provides support for the use of revised morphing algorithms which result in improved future weather time series which can be used in building simulation. For example, when applied to the temperature, it can be used to produce more accurate representations of future temperature profiles due to climate change, and for building performance assessment, such as energy consumption and overheating. It plays an important role in producing reliable and realistic weather data for future-proof building design.

Defining generation parameters with an adaptable data-driven approach to construct typical meteorological year weather files

Weather files play a critical role in building energy simulation by providing crucial information on the environmental condition. Typical meteorological year (TMY) weather files are often used in building energy simulation to approximate the long-term performance. These files are generated using long-term weather data and are meant to reflect the typical weather conditions within the dataset. Common generation methods used to construct TMY weather files typically rely on expert-defined universal generation parameters. However, the weather parameters and weighting factors used to generate these files, termed generation parameters, often overlook variations in climate and applications. This study introduces a data-driven approach to define the generation parameters used in the Sandia method to produce TMY weather files. The study aims to enhance the generation method by providing a framework to define the generation parameters which considers data availability, climate and application.The proposed methodology employs machine learning and feature importance to define the generation parameters used to construct TMY weather files. The generated TMY weather files are evaluated for a Montreal case study using building energy demand. The results show an improvement in the representativeness of the long-term average building energy demand for the weather file generated using the model-defined generation parameters.

Multi-scenario Extreme Weather Simulator application to heat waves: Ko’olauloa community resilience hub

Heat waves are increasing in severity, duration, and frequency. The Multi-Scenario Extreme Weather Simulator (MEWS) models this using historical data, climate model outputs, and heat wave multipliers. In this study, MEWS is applied for planning of a community resilience hub in Hau’ula, Hawaii. The hub will have normal operations and resilience operations modes. Both these modes were modeled using EnergyPlus. The resilience operations mode includes cutting off air conditioning for many spaces to decrease power requirements during emergencies. Results were simulated for 300 future weather files generated by MEWS for 2020, 2040, 2060, and 2080. Shared socioeconomic pathways 2–4.5, 3–7.0 and 5–8.5 were used. The resilience operations mode results show two to six times increase of hours of exceedance beyond 32.2 °C from present conditions, depending on climate scenario and future year. The resulting decrease in thermal resilience enables an average decrease of energy use intensity of 26% with little sensitivity to climate change. The decreased thermal resilience predicted in the future is undesirable, but was not severe enough to require a more energy-intensive resilience mode. Instead, planning is needed to assure vulnerable individuals are given prioritized access to air-conditioned parts of the hub if worst-case heat waves occur.

An empirical review of methods to assess overheating in buildings in the context of changes to extreme heat events

Under climate change, extreme heat events are projected to become more frequent and intense. With people spending approximately 90% for their time indoors and buildings having long lifetimes, it is important that the built environment is resilient to these changes. Current methods to assess building performance in a future climate typically use morphed weather files and annual metrics. We compare 30 metrics and 2 weather data sources to assess and improve the representation of extreme heat events in building simulation. We show that morphing an extreme observed year may not necessarily result in an equally extreme year under the future climate and that current annual metrics do not correlate well with heatwave severity. We suggest that weather data from climate models is more robust in representing future weather for the UK and explore the recent UKCP18 data. We propose novel metrics which are able to capture heatwave severity inside buildings.

Assessing the impact of morphed CMIP6 climate model outputs on building energy performance simulations

In this paper we utilise the latest generation of climate change models from Climate Model Intercomparison Project 6 (CMIP6) to morph typical meteorological year (TMY) weather files. These morphed files are used in conjunction with a standard set of reference models for the climate zones in the United States. Building energy demand, comfort, and photovoltaic (PV) yield are all simulated and evaluated for how they can be expected to change under each climate scenario. We find that the shifts in various preference metrics may vary seasonally in both magnitude and direction. Additionally, the overall distribution of the change can be highly variable across the US climate zones. The results indicate that buildings, as designed now, may not be able to provide comfortable conditions in the future. Furthermore the impact of increased cooling demand will change how building-based PV electricity yield is utilised.

Reconstruction of narrowband solar radiation for enhanced spectral selectivity in building-integrated solar energy simulations

Solar radiation is a critical factor in advanced envelope design and solar energy's building integration, necessitating a shift from broadband solar radiation analyses towards more precise narrowband or spectrum-focused approaches. Understanding the performance potential of spectral-selective materials or structures requires accurate solar spectral information at specified locations, a feature often overlooked by conventional modeling tools. This work presents an innovative solar decomposing model capable of differentiating key solar irradiation components—visible and infrared—from broadband solar irradiance, without the need for expensive spectrum measurements. Our approach employs the extreme boosting regression tree method and leverages existing or easily derivable data from typical weather files. An exploratory analysis of the importance and interaction of different features in predicting solar irradiation components is also conducted. The results show that the proposed algorithm has an R2 of 0.981 and 0.990, RMSE of 18.280 and 18.390, and MAE of 7.989 and 8.011, for predicting VIS and NIR amount in DNI, respectively (for the strongest model using all the predictors within the dataset). This research offers an added layer of practicality by including case studies demonstrating how the solar decomposition models serve in real-world applications, especially in the integration of wavelength-selective devices like window systems and transparent solar cells into advanced envelope designs. Such real-world testing has verified the presence of a disparity between the power output calculation of NIR-selective transparent photovoltaics upon the broadband solar radiation data and the suggested narrowband solar radiation data, potentially resulting in a maximum deviation of 15.7 %. The decomposing models developed empower researchers and designers to generate new weather files comprising narrowband solar irradiance data, thereby enhancing their capacity to examine the influence of spectral-selective materials on a building's solar performance using existing solar simulation programs. The proposed method will be further improved by including more data from different climate zones and weather characteristics in the training model and validating through field measurements.

Climate and soil-based constraints to rainfed cotton yield in the Northern Territory, Australia – A modelling approach using APSIM-OZCOT

Cotton production in Australia is expanding to the northern semi-arid tropical regions as a means to droughtproof the industry. The majority of expansion is expected to come from rainfed farming systems. However, little is known on tropical rainfed cotton, particularly on climate and soil-based constraints that determine cotton production. Using the Northern Territory (NT) as case study, we investigated the effects of between and intra seasonal rainfall variability and key soil types on soil moisture dynamics,timely operations, lint yield, and industry viability. We examined several regions with greatest potential for rainfed agriculture in the NT, including sites in the Daly Basin, Mataranka and Larrimah. To this purpose we applied a locally calibrated and validated cotton growth model (APSIM-OZCOT) using weather files sourced from the SILO database of Australian climate data, and soil profile parameters for three red Kandosols: Blain, Oolloo and Tippera. Our study shows that simulated median lint yields ranged from 570 to 996 kg ha−1, with larger yields achieved on soils that hold more moisture (Blain > Oolloo > Tippera), and at locations that received greater amounts of seasonal rainfall. Planting window largely governed soil-moisture availability and thus sowing opportunities. An early planting (1–15 December) increased yields (greater available soil-moisture) but reduced planting opportunities due to insufficient mulch cover at planting. A late planting (15 Jan to 1 Feb) reduced cotton yields due to less seasonal rainfall, but increased the number of sowing opportunities in the drier regions since more time was allowed to establish sufficient cover crop. Availability of deep soil-moisture was important, particularly in the later crop phases when the roots reach deeper layers, and sufficient soil moisture is required to grow and finish bolls. Our assessment further highlights the potential impact of excessively wet conditions on cotton production. Short periods of intense rainfall (known as “burst” events); a typical characteristic of the monsoon, reduce cotton yield through waterlogging. Scaling our simulated yields to the potential area of suitable soils in the NT shows considerable benefits to the regional economy and should be considered when planning industry development, including the establishment of cotton gins. The insights gained from this study are relevant to industry developments in similar environments, such as in parts of Brazil, India, and Sahelian Africa

Investigation of urban heat island and climate change and their combined impact on building cooling demand in the hot and humid climate of Qatar

Urban Heat Island (UHI) and climate change are two critical factors affecting the energy demand of buildings. However, the previous literature often overlooked the concurrent impacts of these factors, which leads to an erroneous estimation of the current and future energy demand of buildings. To address this issue, this paper investigates the UHI and climate change and their combined impacts on the current and future cooling demands of high-rise residential buildings in the hot and humid climate of Qatar. The impacts of UHI and climate change on the climatic conditions of the Marina district of Lusail City, Qatar, are evaluated using Urban Weather Generator (UWG) and World Weather Generator (WWG) tools, respectively, for 2050 and 2080. A total of eight weather sources, two for 2020 and six for 2050 and 2080, are compared to the weather data collected from the established local weather stations in the city. Two important methods are adopted to elaborate on the combined impact of the UHI and climate change on building cooling demand. In the first method (M1), the future weather file obtained from the Open Weather Map (OM) is processed by UWG for the UHI impact analysis and then by WWG for the climate change impact analysis, while in the second method (M2), the future weather file is first processed by WWG, followed by UWG. According to the results, for the hot and humid climate of Qatar, the cooling energy consumption of the high-rise residential building increases by 19% and 33.5% for 2050 and 2080, respectively, by the first method, and by 20% and 34.4% for 2050 and 2080, respectively by the second method. Both methods yield fairly similar results on the combined impact of UHI and climate change on building cooling demand in hot and humid climates. The findings of this study highlight the importance and necessity of considering UHI and climate change impact in building energy simulations to estimate the present and future energy demand of buildings accurately.

Current and future geographical distribution of the indoor conditions for high thermal inertia historic buildings across Portugal via hygrothermal simulation

Meteorological conditions play a major influence on the indoor conditions of buildings, especially in southern European countries, where the hygrothermal requirements are systematically neglected, on contrast to northern European countries. Outdoor conditions will greatly differ with a building's location, even more if climate change is considered. Designing or retrofitting should take the climate requirements and, if necessary, adapt appropriately.This paper presents a methodology that can easily assess the variance of the indoor climate or even the performance of a retrofit measure in accordance with location. The methodology encompasses five steps: 1) Set meteorological data; 2) Build outdoor weather files; 3) Obtain interface soil/slab temperature; 4) Obtain simulation outputs; and 5) Build map. This methodology is flexible in terms of location/region, computational model, requirements and weather data.A model of a historic church was used to compute current and future climates’ distribution for Portugal. It was shown that the indoor climates are more moderate on locations near the coast than on those in the interior of the country, and that the buffering effect is higher in the west coast than in the south coast. A significant increase in terms of indoor temperature from scenario RCP 4.5 to RCP 8.5 is expected. In most of the coastal zones, an increase in RH is expected in the near future, whilst in some interior regions, a decrease is expected. Finally, it was shown that when using an adequate interpolation function, the coarser grid can correctly simulate the geographical distribution of the indoor conditions.

Comparison of ground-based and floating solar photovoltaic systems performance based on monofacial and bifacial modules in Ghana

This research aims to compare the energy output potential of land-based and floating bifacial photovoltaic (PV) systems of 50 MW and 400 kW with an existing land-based and floating monofacial PV system of the same dimensions and design in Bui, Ghana. The study uses ground weather files and PV system configurations to compare the energy yield from simulations of the different PV systems based on capacity factor, performance ratio, and monthly, final/annual energy yield. The results show that the bifacial gain of land-based and floating bifacial PV systems is 2.51% and 4.57%, respectively, and the capacity factor and performance ratio of the bifacial PV system are higher than the monofacial PV system. However, the additional energy generated by the bifacial PV system is not significant enough to justify the installation of a new system. Therefore, the study recommends optimizing system parameters such as albedo, tilt angle, and ground cover ratio to 0.5, 15°, and 0.3 respectively to enhance the performance and increase the bifacial gain of the PV system. Overall, this study provides valuable insights into the potential of bifacial PV systems in Bui, Ghana, and highlights the importance of optimizing system parameters for maximum energy output.

Novel indicators to evaluate PCM performance under different ventilation strategies by considering the impact of climate change

Due to climate change, the cooling energy demand in the building sector is projected to increase significantly. According to International Energy Agency, for a sustainable future, energy-efficient technology should be implemented in buildings. The potential of Phase change materials (PCM) for reducing building energy consumption has been widely acknowledged by researchers. However, the selection of appropriate PCM and its latent heat exploitation is crucial when PCM is integrated into the building envelope. This research evaluates the implementation of PCM for building cooling energy savings (CES) with climate change in thirty-nine cities located in 13 different zones of the future Koppen-Geiger climate map. Novel performance indicators, energy charging, and discharging coefficients are presented, to evaluate the PCM performance based on the exact latent heat exploitation. A range of PCMs (PCM 18–32) were assessed by coupling PCM with free night ventilation and changeover ventilation using the hybrid downscaled weather files for 2095. The results showed that the optimum PCM varied from PCM27 to PCM30 resulting in substantial CES (1214 kWh-7240 kWh) in all zones except Cfb, Dfa, and Dfb under the indoor HVAC controlled conditions. In natural ventilation conditions, combining PCM with free night ventilation increased CES up to 2490 kWh compared to night ventilation alone, while combining PCM with changeover ventilation, reduced energy consumption by up to 2772 kWh compared to changeover ventilation alone. Overall, free night ventilation with PCM improved the CES by up to a maximum of 35% while changeover ventilation in combination with PCM further increased the CES by up to 96% and appeared to be the best strategy for maximizing the CES in all climate zones.

Impacts of climate change and building energy efficiency improvement on city-scale building energy consumption

Facing global warming, the construction industry, as one of the main sources of carbon emissions, is facing challenges. Assessing the changes in urban building energy consumption and the potential for energy savings is crucial. This paper evaluated the impacts of climate change and building energy conservation measures (ECMs) on the energy consumption of 59,332 urban buildings in Changsha, China. Firstly, the weather files in 2050 and 2080 of Changsha City under the future low-emission scenario are obtained using the Weather Morph. Secondly, the benchmark model for large office buildings was established by OpenStudio-Standards. Then the models of urban buildings in Changsha were created to expand the research scope, including 22 types of buildings using AutoBPS. Finally, four ECMs and rooftop photovoltaic (PV) systems were added according to the energy saving standards, including envelope, heating, ventilation and air conditioning (HVAC) system, lighting, and equipment. The results indicated that the annual total primary source energy use intensity (EUI) of large office buildings in the base year, 2050 and 2080 are 365.93, 372.99, and 374.89 kWh/m2, respectively. And for the urban buildings is 47,862.22, 49,387.34, and 50,043.90 GWh, with an increase of 3.19% and 4.56%, respectively. Taking the base year as an example, when combined with four ECMs and PV systems, the EUI of large office buildings can be reduced to 211.61 kWh/m2, achieving an energy saving rate of 46.67%. And when all four ECMs were applied, the EUI for the urban building model could be reduced by 22,278.56 GWh (46.55%). These results can provide valuable insights for urban energy planning to address future climate change and reduce carbon emissions.