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Manapragada, Naga Venkata Sai Kumar, Anoop Kumar Shukla, Gloria Pignatta, Komali Yenneti, Deepika Shetty, Bibhu Kalyan Nayak, and Venkataramana Boorla. "Development of the Indian Future Weather File Generator Based on Representative Concentration Pathways." Sustainability 14, no. 22 (November 16, 2022): 15191. http://dx.doi.org/10.3390/su142215191.
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India’s fossil-fuel-based energy dependency is up to 68%, with the commercial and residential sectors contributing to the rise of building energy demand, energy use, and greenhouse gas emissions. Several studies have shown that the increasing building energy demand is associated with increased space-cooling ownership and building footprint. The energy demand is predicted to grow further with the conditions of global warming and the phenomenon of urban heat islands. Building designers have been using state-of-the-art transient simulation tools to evaluate energy-efficient envelopes with present-day weather files that are generated with historical weather datasets for any specific location. Designing buildings with historical climatic conditions makes the buildings vulnerable to the predicted climate change impacts. In this paper, a weather file generator was developed to generate Indian future weather files using a geo-filtering-based spatial technique, as well as the temporal downscaling and machine learning (ML)-based bias correction approach proposed by Belcher et al. The future weather files of the three representative concentration pathways of 2.6, 4.5, and 8.5 could be generated for the years 2030, 2050, 2070, 2090, and 2100. Currently, the outputs of the second-generation Canadian Earth System Model are being used to create future weather files that will aid architects, urban designers, and planners in developing a built environment that is resilient to climate change. The novelty lies in using observed historical data from present-day weather files on the typical meteorological year for testing and training ML models. The typical meteorological weather files are composed of the concatenation of the monthly weather datasets from different years, which are referred to for testing and training ML models for bias correction.
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Aram, Kimiya, Roohollah Taherkhani, and Agnė Šimelytė. "Multistage Optimization toward a Nearly Net Zero Energy Building Due to Climate Change." Energies 15, no. 3 (January 28, 2022): 983. http://dx.doi.org/10.3390/en15030983.
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Climate change is one of the major problems of the planet. The atmosphere is overloaded with carbon dioxide caused by fossil fuels that are burned for energy. Almost 40 percent of the total energy worldwide is used by the building sector, which comes from non-renewable sources and contributes up to 30% of annual greenhouse gas emissions globally. The building sector in Iran accounts for 33.8% of Iran’s total energy usage. Within the building sector, the energy consumption of Iranian educational buildings is 2.5 times higher than educational buildings in developed countries. One of the most effective ways of reducing global energy consumption and greenhouse gas emissions is retrofitting existing buildings. This study aims to investigate whether a particular energy-optimized design under the present climate conditions would respond effectively to future climate change. This can help designers make a better decision on an optimal model, which can remain optimal over the years based on climate change. For methodological purposes, multistage optimization was used to retrofit an existing educational building. Specifically, the non-dominated sorting genetic algorithm (NSGA-II) was chosen to minimize the cooling and heating load, as well as consider investment costs for present and future weather files, using the jEPlus tool. Furthermore, the TOPSIS method was used to identify the best set of retrofit measures. For this purpose, a four-story educational building in Tehran was modeled on Design Builder software v7.0.0.116 as a case study to provide a better understanding for researchers of how to effectively retrofit a building to achieve a nearly zero energy building considering climate change. The results show that the optimized solution for the present weather file does not remain the optimized solution in 2080. Moreover, it is shown that to have an optimized building in regard to future weather files, the model should be designed for the future weather conditions. This study shows that if the building becomes optimized using the present weather file the total energy consumption will be reduced by 65.14% and 86.18% if using the future weather file. These two figures are obtained by implementing active and passive measures and show the priority of using the future weather file for designers. Using PV panels also, this building is capable of becoming a nearly net zero building, which would produce about 90% of its own energy demands.
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Lauzet, N., T. Colinart, M. Musy, and K. Lapray. "Selecting extreme weather file to assess overheating in residential building." Journal of Physics: Conference Series 2069, no. 1 (November 1, 2021): 012231. http://dx.doi.org/10.1088/1742-6596/2069/1/012231.
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Abstract Climate change is great challenge for current and newly built buildings. Nowadays, TMY weather file can be easily generated following the IPCC scenarios. Nevertheless, since these data are extrapolated with stochastic model from monthly mean values, they do not show a real pattern and do not include extreme events like heatwaves. In order to get more representative data, we propose in this work a methodology to select real measured files from a large database in light of heatwaves and climate change. This methodology is applied to the city of Lyon, for which 26 years of weather data are available. Three measured weather files projected for the time periods 2020, 2050 and 2080 are selected. These files are used in building thermal simulation of residential building with low or high thermal inertia. Summer overheating is analysed through two different comfort indicators: adaptative comfort and Givoni chart. Results indicates that summer overheating risk is obviously increased with future weather files. When compared to usual TMY files, this risk is also enhanced by using weather file including extreme events like heatwaves. Last, we note that discomfort is mainly encountered during this extreme events.
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P.Tootkaboni, Mamak, Ilaria Ballarini, Michele Zinzi, and Vincenzo Corrado. "A Comparative Analysis of Different Future Weather Data for Building Energy Performance Simulation." Climate 9, no. 2 (February 23, 2021): 37. http://dx.doi.org/10.3390/cli9020037.
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The building energy performance pattern is predicted to be shifted in the future due to climate change. To analyze this phenomenon, there is an urgent need for reliable and robust future weather datasets. Several ways for estimating future climate projection and creating weather files exist. This paper attempts to comparatively analyze three tools for generating future weather datasets based on statistical downscaling (WeatherShift, Meteonorm, and CCWorldWeatherGen) with one based on dynamical downscaling (a future-typical meteorological year, created using a high-quality reginal climate model). Four weather datasets for the city of Rome are generated and applied to the energy simulation of a mono family house and an apartment block as representative building types of Italian residential building stock. The results show that morphed weather files have a relatively similar operation in predicting the future comfort and energy performance of the buildings. In addition, discrepancy between them and the dynamical downscaled weather file is revealed. The analysis shows that this comes not only from using different approaches for creating future weather datasets but also by the building type. Therefore, for finding climate resilient solutions for buildings, care should be taken in using different methods for developing future weather datasets, and regional and localized analysis becomes vital.
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Yassaghi, Hamed, Patrick L. Gurian, and Simi Hoque. "Propagating downscaled future weather file uncertainties into building energy use." Applied Energy 278 (November 2020): 115655. http://dx.doi.org/10.1016/j.apenergy.2020.115655.
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Demanuele, C., A. Mavrogianni, M. Davies, M. Kolokotroni, and I. Rajapaksha. "Using localised weather files to assess overheating in naturally ventilated offices within London's urban heat island." Building Services Engineering Research and Technology 33, no. 4 (September 9, 2011): 351–69. http://dx.doi.org/10.1177/0143624411416064.
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Urban environments typically experience increased average air temperatures compared to surrounding rural areas – a phenomenon referred to as the Urban Heat Island (UHI). The impact of the UHI on comfort in naturally ventilated buildings is the main focus of this article. The overheating risk in urban buildings is likely to be exacerbated in the future as a result of the combined effect of the UHI and climate change. In the design of such buildings in London, the usual current practice is to view the use of one generic weather file as being adequate to represent external temperatures. However, the work reported here demonstrates that there is a considerable difference between the overheating performance of a standard building at different sites within London. This implies, for example, that a building may wrongly pass or fail criteria used to demonstrate compliance with building regulations as a result of an inappropriate generic weather file being used. The work thus has important policy implications. Practical application: The Greater London Authority has recently developed, with the Chartered Institute of Building Services Engineers, guidance for developers to address the risk of overheating in buildings via the provision of weather files for London relating to three zones. While such an initiative is welcomed, it may be that a weather file tailored to the building location would be preferable. Of course, this would add further complexity to the process and a view would have to be taken as the viability of such an approach. The work presented in this article, however, suggests that serious consideration should be given to the use of tailored weather data for regulatory purposes.
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Watkins, R., GJ Levermore, and JB Parkinson. "Constructing a future weather file for use in building simulation using UKCP09 projections." Building Services Engineering Research and Technology 32, no. 3 (March 8, 2011): 293–99. http://dx.doi.org/10.1177/0143624410396661.
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Fiorito, Francesco, Giandomenico Vurro, Francesco Carlucci, Ludovica Maria Campagna, Mariella De Fino, Salvatore Carlucci, and Fabio Fatiguso. "Adaptation of Users to Future Climate Conditions in Naturally Ventilated Historic Buildings: Effects on Indoor Comfort." Energies 15, no. 14 (July 7, 2022): 4984. http://dx.doi.org/10.3390/en15144984.
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User behaviour can significantly affect indoor thermal comfort conditions, as well as energy consumption, especially in existing buildings with high thermal masses where natural cross ventilation is the main strategy to reduce cooling loads. The aims of this paper were: (i) to compare how behavioural changes evaluated by means of rule-based and stochastic models lead to changes in indoor thermal comfort levels, and (ii) to define the patterns of indoor thermal comfort in historic residential buildings in future scenarios. To this end, a historic building located in Molfetta (Southern Italy) was analysed using a dynamic energy simulation engine in five weather scenarios (Typical Meteorological Year, current extreme weather file 2018, predicted weather files for 2020, 2050, and 2080 generated by morphing method), and stochastic and rule-based models for window openings were adopted and compared. The results showed that the stochastic model was more accurate than the rule-based one, resulting in a reduction of discomfort conditions during the summer period between 30% and 50% in all climate scenarios. However, although the differences between predicted discomfort levels using rule-based and stochastic models tended to increase, discomfort levels still appeared to be not acceptable in the 2050 and 2080 scenarios due to the rising temperature driven by climate change.
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Pouriya, Jafarpur, and Berardi Umberto. "Building energy demand within a climate change perspective: The need for future weather file." IOP Conference Series: Materials Science and Engineering 609 (October 23, 2019): 072037. http://dx.doi.org/10.1088/1757-899x/609/7/072037.
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Ciancio, Virgilio, Serena Falasca, Iacopo Golasi, Pieter de Wilde, Massimo Coppi, Livio de Santoli, and Ferdinando Salata. "Resilience of a Building to Future Climate Conditions in Three European Cities." Energies 12, no. 23 (November 27, 2019): 4506. http://dx.doi.org/10.3390/en12234506.
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Building energy need simulations are usually performed using input files that contain information about the averaged weather data based on historical patterns. Therefore, the simulations performed are not able to provide information about possible future scenarios due to climate change. In this work, future trends of building energy demands due to the climate change across Europe were studied by comparing three time steps (present, 2050, and -2080) in three different European cities, characterized by different Köppen-Geiger climatic classes. A residential building with modern architectural features was taken into consideration for the simulations. Future climate conditions were reached by applying the effects of climate changes to current hourly meteorological data though the climate change tool world weather file generator (CCWorldWeatherGen) tool, according to the guidelines established by the Intergovernmental Panel on Climate Change. In order to examine the resilience of the building, the simulations carried out were compared with respect to: peak power, median values of the power, and energy consumed by heating and cooling system. The observed trend shows a general reduction in the energy needs for heating (–46% for Aberdeen, –80% for Palermo, –36% for Prague in 2080 compared to the present) and increase (occurrence for Aberdeen) in cooling requirements. These results imply a revaluation of system size.
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Passos Filho, José Aderson Araújo, Bruno de Payva y. Raviolo, Natasha Catunda, Nayana Helena Barbosa de Castro, Karoline Cordeiro de Andrade, and Daniel Ribeiro Cardoso. "Synthesizing test Reference Year files from known climate patterns of nearby cities." PARC Pesquisa em Arquitetura e Construção 10 (December 29, 2019): e019030. http://dx.doi.org/10.20396/parc.v10i0.8653706.
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The importance of an architecture adapted to its climatic context is often debated. In order to avoid future unexpected environmental behavior or failure of a building during its use, building simulation tools are used in the design and require complete and consistent weather data. However, such data are not always available for the locations where buildings are simulated, and the use of data from neighboring cities becomes usual. There are, though, several uncertainties involved in the behavior of environmental variables when the climate of large urban centers is attributed to nearby localities and areas with more significant vegetation cover, water bodies, different topography, among others. The present paper aims to present the process of preparing a weather file for the Pecém Industrial and Port Complex, located at 40 km from the capital Fortaleza, Brazil, in order to be used in simulations during the design process of buildings. The synthesis of the file was achieved through the collection and treatment of information measured in loco, the application of recommended models for the estimation of missing data, and the development of an alternative method for the estimation of a Test Reference Year of localities without weather data of several years.
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Sommer, Philipp S., and Jed O. Kaplan. "A globally calibrated scheme for generating daily meteorology from monthly statistics: Global-WGEN (GWGEN) v1.0." Geoscientific Model Development 10, no. 10 (October 16, 2017): 3771–91. http://dx.doi.org/10.5194/gmd-10-3771-2017.
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Abstract. While a wide range of Earth system processes occur at daily and even subdaily timescales, many global vegetation and other terrestrial dynamics models historically used monthly meteorological forcing both to reduce computational demand and because global datasets were lacking. Recently, dynamic land surface modeling has moved towards resolving daily and subdaily processes, and global datasets containing daily and subdaily meteorology have become available. These meteorological datasets, however, cover only the instrumental era of the last approximately 120 years at best, are subject to considerable uncertainty, and represent extremely large data files with associated computational costs of data input/output and file transfer. For periods before the recent past or in the future, global meteorological forcing can be provided by climate model output, but the quality of these data at high temporal resolution is low, particularly for daily precipitation frequency and amount. Here, we present GWGEN, a globally applicable statistical weather generator for the temporal downscaling of monthly climatology to daily meteorology. Our weather generator is parameterized using a global meteorological database and simulates daily values of five common variables: minimum and maximum temperature, precipitation, cloud cover, and wind speed. GWGEN is lightweight, modular, and requires a minimal set of monthly mean variables as input. The weather generator may be used in a range of applications, for example, in global vegetation, crop, soil erosion, or hydrological models. While GWGEN does not currently perform spatially autocorrelated multi-point downscaling of daily weather, this additional functionality could be implemented in future versions.
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Hutchison, Keith D., Barbara D. Iisager, Sudhakar Dipu, Xiaoyan Jiang, Johannes Quaas, and Randy Markwardt. "A Methodology for Verifying Cloud Forecasts with VIIRS Imagery and Derived Cloud Products—A WRF Case Study." Atmosphere 10, no. 9 (September 5, 2019): 521. http://dx.doi.org/10.3390/atmos10090521.
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A methodology is presented to evaluate the accuracy of cloud cover fraction (CCf) forecasts generated by numerical weather prediction (NWP) and climate models. It is demonstrated with a case study consisting of simulations from the Weather Research and Forecasting (WRF) model. In this study, since the WRF CCf forecasts were initialized with reanalysis fields from the North American Mesoscale (NAM) Forecast System, the characteristics of the NAM CCf products were also evaluated. The procedures relied extensively upon manually-generated, binary cloud masks created from VIIRS (Visible Infrared Imager Radiometry Suite) imagery, which were subsequently converted into CCf truth at the resolution of the NAM and WRF gridded data. The initial results from the case study revealed biases toward under-clouding in the NAM CCf analyses and biases toward over-clouding in the WRF CCf products. These biases were evident in images created from the gridded NWP products when compared to VIIRS imagery and CCf truth data. Thus, additional simulations were completed to help assess the internal procedures used in the WRF model to translate moisture forecast fields into layered CCf products. Two additional sets of WRF CCf 24 h forecasts were generated for the region of interest using WRF restart files. One restart file was updated with CCf truth data and another was not changed. Over-clouded areas in the updated WRF restart file that were reduced with an update of the CCf truth data became over-clouded again in the WRF 24 h forecast, and were nearly identical to those from the unchanged restart file. It was concluded that the conversion of WRF forecast fields into layers of CCf products deserves closer examination in a future study.
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Dannenberg, Valentin, Robert Schüler, and Achill Schürmann. "A Data Processing Framework for Polar Performance Diagrams." Applied Sciences 12, no. 6 (March 17, 2022): 3085. http://dx.doi.org/10.3390/app12063085.
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Polar performance diagrams are commonly used to predict the performance of a sailing vessel under given wind conditions. They are, in particular, an essential part of robotic sailing vessels and a basis for weather routing algorithms. In this paper we introduce a new framework for scientific work with such diagrams, which we make available as an open source Python package. It contains a model for the creation of polar performance diagrams from measurement data and supports different representations of polar performance diagrams for different tasks. The framework also includes several methods for the visualisation of polar performance diagrams, for example for scientific publications. Additionally, the presented framework solves basic tasks for the future development of weather-routing algorithms in a far more general manner than other methods did previously: it provides the calculation of costs of a sailing trip using custom cost functions, suggestions of optimal steering using convex hull calculations and a more flexible calculation of isochrone points, using custom weather models. Altogether, the presented framework allows future researchers to more easily handle polar performance diagrams. The corresponding Python package is compatible with various established file formats.
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Yassaghi, Hamed, and Simi Hoque. "Impact Assessment in the Process of Propagating Climate Change Uncertainties into Building Energy Use." Energies 14, no. 2 (January 11, 2021): 367. http://dx.doi.org/10.3390/en14020367.
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Buildings are subject to significant stresses due to climate change and design strategies for climate resilient buildings are rife with uncertainties which could make interpreting energy use distributions difficult and questionable. This study intends to enhance a robust and credible estimate of the uncertainties and interpretations of building energy performance under climate change. A four-step climate uncertainty propagation approach which propagates downscaled future weather file uncertainties into building energy use is examined. The four-step approach integrates dynamic building simulation, fitting a distribution to average annual weather variables, regression model (between average annual weather variables and energy use) and random sampling. The impact of fitting different distributions to the weather variable (such as Normal, Beta, Weibull, etc.) and regression models (Multiple Linear and Principal Component Regression) of the uncertainty propagation method on cooling and heating energy use distribution for a sample reference office building is evaluated. Results show selecting a full principal component regression model following a best-fit distribution for each principal component of the weather variables can reduce the variation of the output energy distribution compared to simulated data. The results offer a way of understanding compound building energy use distributions and parsing the uncertain nature of climate projections.
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Yassaghi, Hamed, and Simi Hoque. "Impact Assessment in the Process of Propagating Climate Change Uncertainties into Building Energy Use." Energies 14, no. 2 (January 11, 2021): 367. http://dx.doi.org/10.3390/en14020367.
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Buildings are subject to significant stresses due to climate change and design strategies for climate resilient buildings are rife with uncertainties which could make interpreting energy use distributions difficult and questionable. This study intends to enhance a robust and credible estimate of the uncertainties and interpretations of building energy performance under climate change. A four-step climate uncertainty propagation approach which propagates downscaled future weather file uncertainties into building energy use is examined. The four-step approach integrates dynamic building simulation, fitting a distribution to average annual weather variables, regression model (between average annual weather variables and energy use) and random sampling. The impact of fitting different distributions to the weather variable (such as Normal, Beta, Weibull, etc.) and regression models (Multiple Linear and Principal Component Regression) of the uncertainty propagation method on cooling and heating energy use distribution for a sample reference office building is evaluated. Results show selecting a full principal component regression model following a best-fit distribution for each principal component of the weather variables can reduce the variation of the output energy distribution compared to simulated data. The results offer a way of understanding compound building energy use distributions and parsing the uncertain nature of climate projections.
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Wallace, Carlington W., Dennis C. Flanagan, and Bernard A. Engel. "Quantifying the Effects of Future Climate Conditions on Runoff, Sediment, and Chemical Losses at Different Watershed Sizes." Transactions of the ASABE 60, no. 3 (2017): 915–29. http://dx.doi.org/10.13031/trans.12094.
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Abstract. Quantifying the effects of climate change on watershed hydrology and agricultural chemical losses is imperative when developing appropriate management practices for agricultural watersheds. Agricultural management practices are often assessed at the watershed scale; therefore, understanding the influence of climate change at different watershed sizes can provide insight into the effectiveness of watershed management strategies. In this study, the Soil and Water Assessment Tool (SWAT) and downscaled weather data generated using the MarkSim weather file generator were used to evaluate the potential impact of climate change in the hydrologically modified Cedar Creek (CCW), F34, AXL, and ALG watersheds located in northeastern Indiana. This study evaluated changes in surface flow, tile flow, sediment, and agricultural chemical losses based on an ensemble mean of the 17 general circulation models (GCMs) from the Coupled Model Intercomparison Project Phase 5 (CMIP5). We found no clear evidence that watershed size had an impact on the simulation of climate change effects on discharge or nutrient losses. Results of this study indicated that predicted surface flow decreased significantly toward the end of this century (ranging from 9% in CCW to 22% in ALG), while predicted subsurface tile flow increased significantly (ranging from 20% in CCW to 26% in AXL). The percentage increases in predicted sediment loss for the CCW, AXL, and ALG watersheds were significant at a = 0.05, although the magnitudes of overall sediment losses were low, especially in the smaller monitored watersheds (F34, AXL, and ALG) in which several best management practices are implemented. Differences in predicted atrazine, soluble N, total N, and total P losses between the baseline period (1961-1990) and the end of this century were not significant for any of the watersheds, while increased predicted soluble P losses were only significant for the larger CCW and F34 watersheds. Keywords: Climate change, MarkSim, Surface flow, SWAT, Tile flow.
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Pandit, Sameer, and Nawraj Bhattarai. "Impact of Rise in Atmosphere Temperature on an Official Building’s Energy Consumption in Kathmandu." Journal of the Institute of Engineering 13, no. 1 (June 22, 2018): 102–7. http://dx.doi.org/10.3126/jie.v13i1.20354.
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The objective of this research is to study the impact of rise in atmospheric temperature, in buildings energy consumption in the future. An existing Typical Meteorological Year (TMY) weather file of time span 1973-1996 composed of weather data by Solar and Wind Energy Resource Assessment (SWERA) project is assumed as the baseline climate in this study. Monthly average temperature of future years in business as usual scenario predicted by Meteonorm is downscaled to hourly temperature data using downscaling method, morphing. This showed that annual average air temperature of the atmosphere will increase by 1.64¬0C, 2.12¬0C, 2.52¬0C and 2.28¬0C from baseline year in the years 2010, 2020, 2030, and 2040 respectively. Building energy simulation tool eQUEST is used to analyze the energy consumption pattern of a selected building located in Kathmandu. Study has shown that total energy consumption of the building for heating, cooling, lighting and miscellanies equipment will increase by 4.9%, 6.2%, 7.3% and 8.3% for the years 2010, 2020, 2030 and 2040 respectively from baseline year whereas cooling load will increase by 19%, 23%, 27% and 31%. The study also has shown that insulating a building will decrease the energy consumption. There will be decrease in the cooling energy consumption by 2.29%, 2.98%, 4.06% and 4.78% in the years 2010, 2020, 2030 and 2040 respectively after addition of insulation material mineral wool/fiber.Journal of the Institute of Engineering, 2017, 13(1): 102-107
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Risner, Jamie, and Anna Sutherland. "Static grid carbon factors – Can we do better?" Building Services Engineering Research and Technology 42, no. 3 (February 11, 2021): 257–77. http://dx.doi.org/10.1177/0143624421991964.
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The average carbon intensity (gCO2e/kWh) of electricity provided by the UK National Grid is decreasing and becoming more time variable. This paper reviews the impact on energy calculations of using various levels of data resolution (half hourly, daily, monthly and annual) and of moving to region specific data. This analysis is in two parts, one focused on the potential impact on Part L assessments and the other on reported carbon emissions for existing buildings. Analysis demonstrated that an increase in calculated emissions of up to 12% is possible when using an emissions calculation methodology employing higher resolution grid carbon intensity data. Regional analysis indicated an even larger calculation discrepancy, with some regions annual emissions increasing by a factor of ten as compared to other regions. This paper proposes a path forward for the industry to improve the accuracy of analysis by using better data sources. The proposed change in calculation methodology is analogous to moving from using an annual average external temperature to using a CIBSE weather profile for a specific city or using a future weather file. Practical application: This paper aims to quantify the inaccuracy of a calculation methodology in common use in the industry and key to building regulations (specifically Building Regulations Part L – Conservation of Fuel and Power) – translating electricity consumption into carbon emissions. It proposes an alternative methodology which improves the accuracy of the calculation based on improved data inputs.
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Fajilla, Gianmarco, Emiliano Borri, Marilena De Simone, Luisa F. Cabeza, and Luís Bragança. "Effect of Climate Change and Occupant Behaviour on the Environmental Impact of the Heating and Cooling Systems of a Real Apartment. A Parametric Study through Life Cycle Assessment." Energies 14, no. 24 (December 11, 2021): 8356. http://dx.doi.org/10.3390/en14248356.
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Climate change has a strong influence on the energy consumption of buildings, affecting both the heating and cooling demand in the actual and future scenario. In this paper, a life cycle assessment (LCA) was performed to evaluate the influence of both the occupant behaviour and the climate change on the environmental impact of the heating and cooling systems of an apartment located in southern Italy. The analysis was conducted using IPCC GWP and ReCiPe indicators as well as the Ecoinvent database. The influence of occupant behaviour was included in the analysis considering different usage profiles during the operational phase, while the effect of climate change was considered by varying the weather file every thirty years. The adoption of the real usage profiles showed that the impact of the systems was highly influenced by the occupant behaviour. In particular, the environmental impact of the heating system appeared more influenced by the operation hours, while that of the cooling system was more affected by the natural ventilation schedules. Furthermore, the influence of climate change demonstrated that more attention has to be dedicated to the cooling demand that in the future years will play an ever-greater role in the energy consumption of buildings.
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Horton, Pascal. "AtmoSwing: Analog Technique Model for Statistical Weather forecastING and downscalING (v2.1.0)." Geoscientific Model Development 12, no. 7 (July 12, 2019): 2915–40. http://dx.doi.org/10.5194/gmd-12-2915-2019.
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Abstract. Analog methods (AMs) use synoptic-scale predictors to search in the past for similar days to a target day in order to infer the predictand of interest, such as daily precipitation. They can rely on outputs of numerical weather prediction (NWP) models in the context of operational forecasting or outputs of climate models in the context of climate impact studies. AMs require low computing capacity and have demonstrated useful potential for application in several contexts. AtmoSwing is open-source software written in C++ that implements AMs in a flexible way so that different variants can be handled dynamically. It comprises four tools: a Forecaster for use in operational forecasting, a Viewer to display the results, a Downscaler for climate studies, and an Optimizer to establish the relationship between predictands and predictors. The Forecaster handles every required processing internally, such as NWP output downloading (when possible) and reading as well as grid interpolation, without external scripts or file conversion. The processing of a forecast requires low computing efforts and can even run on a Raspberry Pi computer. It provides valuable results, as revealed by a 3-year-long operational forecast in the Swiss Alps. The Viewer displays the forecasts in an interactive GIS environment with several levels of synthesis and detail. This allows for the provision of a quick overview of the potential critical situations in the upcoming days, as well as the possibility for the user to delve into the details of the forecasted predictand and criteria distributions. The Downscaler allows for the use of AMs in a climatic context, either for climate reconstruction or for climate change impact studies. When used for future climate studies, it is necessary to pay close attention to the selected predictors so that they contain the climate change signal. The Optimizer implements different optimization techniques, such as a semiautomatic sequential approach, Monte Carlo simulations, and a global optimization technique, using genetic algorithms. Establishing a statistical relationship between predictors and predictands is computationally intensive because it requires numerous assessments over decades. To this end, the code was highly optimized for computing efficiency, is parallelized (using multiple threads), and scales well on a Linux cluster. This procedure is only required to establish the statistical relationship, which can then be used for forecasting or downscaling at a low computing cost.
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Fürtön, Balázs, Dóra Szagri, and Balázs Nagy. "The Effect of European Climate Change on Indoor Thermal Comfort and Overheating in a Public Building Designed with a Passive Approach." Atmosphere 13, no. 12 (December 7, 2022): 2052. http://dx.doi.org/10.3390/atmos13122052.
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Dynamic building energy performance modeling is becoming increasingly important in the architectural, engineering, and construction (AEC) industry because of the sector’s significant environmental impact. For such analysis, a climate file representing a typical meteorological year (TMY) is needed, including hourly values for the most important weather-related parameters. However, TMY shows little resemblance to the future of the particular location where a building has been used for decades. Therefore, using predicted future climates during building design is unfortunately rarely practiced, potentially undermining the strategies that should be the fundamental basis of the design. To explore this question, our study compared the heating and cooling energy consumption, indoor thermal comfort, and summer overheating potential of a selected building for three distinctive European climates, in Hungary, Portugal, and Lithuania. All of them were changed according to the IPCC RCP4.5 scenario, and were examined for the present, the 2050, and the 2100 scenarios. We also tested adaptive clothing to evaluate the indoor comfort parameters. The results show a 10% increase in heating and cooling energy use for the same construction and location between 2020 and 2100. The continental climate of Budapest is the most threatened by summer overheating, with an increase of 69% for the ODH26 indicator. A more balanced warming for Lisbon was found (23%), and moderate changes for the city of Kaunas (a 153% increase from a very low baseline).
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Pollak, Cheryl L. ""Hurricane" Sandy." Texas A&M Journal of Property Law 5, no. 2 (December 2018): 157–92. http://dx.doi.org/10.37419/jpl.v5.i2.3.
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On the evening of October 29, 2012, “Hurricane” Sandy made land- fall on the New York coastline, battering the land with strong winds, torrential rain, and record-breaking storm surges. Homes and commercial structures were destroyed; roads and tunnels were flooded; and more than 23,000 people sought refuge in temporary shelters, with many others facing weeks without power and electricity. At the time, Sandy was heralded as one of the costliest hurricanes in the his- tory of the United States; the second costliest hurricane only to Katrina, which hit New Orleans in 2005. Unfortunately, recent experience with Hurricanes Florence, Maria, Harvey, and Irma suggest that this pattern of devastating superstorms may become the new norm as climate change produces more extreme and unpredictable weather events. In Sandy’s aftermath, as individuals returned to their homes, or what remained of them, and communities began to rebuild, the true cost of the storm became apparent. A year after the storm, the Federal Emergency Management Agency (“FEMA”) estimated that over $1.4 billion in assistance was provided to 182,000 survivors of the dis- aster; another $3.2 billion was provided to state and local governments for debris removal, infrastructure repair, and emergency protective measures. More than $2.4 billion was provided to individuals and businesses in the form of low-interest loans through the Small Business Administration (“SBA”), and millions more were spent on grants de- signed to implement mitigation measures in the future and to provide unemployment assistance to survivors. Before the storm, homeowners paid premiums for flood insurance provided through the National Flood Insurance Program (“NFIP”), and for homeowner’s insurance provided by dozens of private insurers. In the months following the storm, they began to file claims for assistance in rebuilding their homes. While many such claims were re- solved successfully, many homeowners were unhappy with the settlement amounts offered by their insurance carriers and felt compelled to file lawsuits in the surrounding state and federal courts. Many of those lawsuits were filed in the United States District Court for the Eastern District of New York (“EDNY”). This case study describes the EDNY’s specifically crafted, unique approach to handling the mass litigation that ensued from Sandy’s devastation, documents some of the problems that the Court faced during that mass litigation, and describes some of the lessons learned from the Court’s experience.
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Medojevic, Milana, Milovan Medojevic, and Villar Díaz. "Simulation-based design of solar photovoltaic energy generation system for manufacturing support." Thermal Science, no. 00 (2020): 161. http://dx.doi.org/10.2298/tsci190719161m.
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Having in mind that energy is being regarded as indispensable to the socio-economic progress of developing and developed nations, where the main objective implies replacement and reduction of a major portion of the fossil fuels utilization, implementation of renewable energy technologies where natural phenomena are transformed into beneficial types of energy are becoming more and more appreciated and needed. Among renewable energy resources we know today, solar energy is the most beneficial, relatively limitless, effective, and dependable. Having this in mind, the aim of this paper is primarily to help key decision-makers understand the process when considering integration of solar energy to meet their own manufacturing energy needs, or how it is called today, to become ?prosumers?. Given the aforementioned, this paper provides an overview of detailed simulation methodology for Photovoltaic (PV) system sizing and design for metal-forming manufacturing system energy needs. The simulation is based on NREL (National Renewable Energy Laboratory) photovoltaic performance model which combines module and inverter sub-models with supplementary code to calculate a photovoltaic power system?s hourly AC output is given a weather file and data describing the physical characteristics of the module, inverter, and array. Furthermore, the characteristic losses are calculated and presented for a fixed array PV system and illustratively given in the form of a Sankey diagram. A variety of graphical data representations are available while the most important ones are given in the study. Lastly, future research topics were filtered and briefly summarized.
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Herrera, Manuel, Sukumar Natarajan, David A. Coley, Tristan Kershaw, Alfonso P. Ramallo-González, Matthew Eames, Daniel Fosas, and Michael Wood. "A review of current and future weather data for building simulation." Building Services Engineering Research and Technology 38, no. 5 (April 21, 2017): 602–27. http://dx.doi.org/10.1177/0143624417705937.
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This article provides the first comprehensive assessment of methods for the creation of weather variables for use in building simulation. We undertake a critical analysis of the fundamental issues and limitations of each methodology and discusses new challenges, such as how to deal with uncertainty, the urban heat island, climate change and extreme events. Proposals for the next generation of weather files for building simulation are made based on this analysis. A seven-point list of requirements for weather files is introduced and the state-of-the-art compared to this via a mapping exercise. It is found that there are various issues with all current and suggested approaches, but the two areas most requiring attention are the production of weather files for the urban landscape and files specifically designed to test buildings against the criteria of morbidity, mortality and building services system failure. Practical application: Robust weather files are key to the design of sustainable, healthy and comfortable buildings. This article provides the first comprehensive assessment of their technical requirements to ensure buildings perform well in both current and future climates.
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Machard, Anaïs, Christian Inard, Jean-Marie Alessandrini, Charles Pelé, and Jacques Ribéron. "A Methodology for Assembling Future Weather Files Including Heatwaves for Building Thermal Simulations from the European Coordinated Regional Downscaling Experiment (EURO-CORDEX) Climate Data." Energies 13, no. 13 (July 2, 2020): 3424. http://dx.doi.org/10.3390/en13133424.
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With increasing mean and extreme temperatures due to climate change, it becomes necessary to use—not only future typical conditions—but future heatwaves in building thermal simulations as well. Future typical weather files are widespread, but few researchers have put together methodologies to reproduce future extreme conditions. Furthermore, climate uncertainties need to be considered and it is often difficult due to the lack of data accessibility. In this article, we propose a methodology to re-assemble future weather files—ready-to-use for building simulations—using data from the European Coordinated Regional Downscaling Experiment (EURO-CORDEX) dynamically downscaled regional climate multi-year projections. It is the first time that this database is used to assemble weather files for building simulations because of its recent availability. Two types of future weather files are produced: typical weather years (TWY) and heatwave events (HWE). Combined together, they can be used to fully assess building resilience to overheating in future climate conditions. A case study building in Paris is modelled to compare the impact of the different weather files on the indoor operative temperature of the building. The results confirm that it is better to use multiple types of future weather files, climate models, and or scenarios to fully grasp climate projection uncertainties.
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Gavidia-Calderón, Mario Eduardo, Sergio Ibarra-Espinosa, Youngseob Kim, Yang Zhang, and Maria de Fatima Andrade. "Simulation of O<sub>3</sub> and NO<sub><i>x</i></sub> in São Paulo street urban canyons with VEIN (v0.2.2) and MUNICH (v1.0)." Geoscientific Model Development 14, no. 6 (June 3, 2021): 3251–68. http://dx.doi.org/10.5194/gmd-14-3251-2021.
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Abstract. We evaluate the performance of the Model of Urban Network of Intersecting Canyons and Highways (MUNICH) in simulating ozone (O3) and nitrogen oxides (NOx) concentrations within the urban street canyons in the São Paulo metropolitan area (SPMA). The MUNICH simulations are performed inside the Pinheiros neighborhood (a residential area) and Paulista Avenue (an economic hub), which are representative urban canyons in the SPMA. Both zones have air quality stations maintained by the São Paulo Environmental Agency (CETESB), providing data (both pollutant concentrations and meteorological) for model evaluation. Meteorological inputs for MUNICH are produced by a simulation with the Weather Research and Forecasting model (WRF) over triple-nested domains with the innermost domain centered over the SPMA at a spatial grid resolution of 1 km. Street coordinates and emission flux rates are retrieved from the Vehicular Emission Inventory (VEIN) emission model, representing the real fleet of the region. The VEIN model has an advantage to spatially represent emissions and present compatibility with MUNICH. Building height is estimated from the World Urban Database and Access Portal Tools (WUDAPT) local climate zone map for SPMA. Background concentrations are obtained from the Ibirapuera air quality station located in an urban park. Finally, volatile organic compound (VOC) speciation is approximated using information from the São Paulo air quality forecast emission file and non-methane hydrocarbon concentration measurements. Results show an overprediction of O3 concentrations in both study cases. NOx concentrations are underpredicted in Pinheiros but are better simulated in Paulista Avenue. Compared to O3, NO2 is better simulated in both urban zones. The O3 prediction is highly dependent on the background concentration, which is the main cause for the model O3 overprediction. The MUNICH simulations satisfy the performance criteria when emissions are calibrated. The results show the great potential of MUNICH to represent the concentrations of pollutants emitted by the fleet close to the streets. The street-scale air pollutant predictions make it possible in the future to evaluate the impacts on public health due to human exposure to primary exhaust gas pollutants emitted by the vehicles.
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Andresen, Inger, Matthias Haase, and Anne Grete Hestnes. "The Development of Future Weather Data Files for Norway." International Journal of Climate Change: Impacts and Responses 2, no. 3 (2011): 1–24. http://dx.doi.org/10.18848/1835-7156/cgp/v02i03/37327.
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Liu, C., W. Chung, F. Cecinati, S. Natarajan, and D. Coley. "Current and future test reference years at a 5 km resolution." Building Services Engineering Research and Technology 41, no. 4 (October 8, 2019): 389–413. http://dx.doi.org/10.1177/0143624419880629.
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Frequently, the computer modelling of the natural and human-made environment requires localised weather files. Traditionally, the weather files are based on the observed weather at a small number of locations (14 for the UK). Unfortunately, both the climate and the weather are known to be highly variable across the landscape, so the small number of locations has the potential to cause large errors. With respect to buildings, this results in incorrect estimates of the annual energy use (sometimes by a factor of 2), or of overheating risk. Here we use a validated weather generator running on a 5 × 5 km grid to create probabilistic test reference years (pTRYs) for the UK at 11,326 locations. We then investigate the spatial variability of these pTRYs and of annual energy estimates and temperatures in buildings generated by them, both now and in 2080. Further pTRYs targeted at understanding the impact of minimum and maximum temperatures are proposed and produced at the same locations. Finally, we place these pTRYs, which represent the first set of reference weather files at this spatial resolution in the world and that include the urban heat island effect, into a publicly accessible database so researchers and industry can access them. Practical applications: Insufficiently localised weather data for building simulations have limited the accuracy of previous estimations of energy use and overheating risk in buildings. This work produces localised probabilistic test reference years (pTRYs) across the whole UK for now and future climates. In addition, a new pTRY method has been proposed in order to overcome an unexpected shortcoming of traditional pTRYs in representing typical maximum and minimum temperatures. These current and future weather data will be of interest to various disciplines including those interested in low carbon design, renewable energy and climate resilience.
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Cox, Rimante A., Martin Drews, Carsten Rode, and Susanne Balslev Nielsen. "Simple future weather files for estimating heating and cooling demand." Building and Environment 83 (January 2015): 104–14. http://dx.doi.org/10.1016/j.buildenv.2014.04.006.
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Arango-Díaz, Lucas, Maria Alejandra Garavito-Posada, Juan Sebastian Calle-Medina, Adriana Marcela Murcia-Cardona, Olga Lucia Montoya-Flórez, and Sebastián Pinto-Quintero. "Suficiencia lumínica de ambientes interiores en escenarios de cambio climático." Revista Hábitat Sustentable 12, no. 2 (December 31, 2022): 40–51. http://dx.doi.org/10.22320/07190700.2022.12.02.03.
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The bioclimatic performance of buildings under climate change scenarios has been extensively studied from a thermo-energy perspective but hardly studied at all from the perspective of indoor daylight sufficiency. This shortcoming is related to the invariability of radiation data in the available weather files of future scenarios. This research proposes identifying the impacts that the variability of radiation data in weather files of future scenarios would have on daylight sufficiency in indoor spaces. The methodology includes the adaptation of available weather files and the running of daylight simulations for hypothetical workspaces located in Medellín, Colombia. The results show differences in the Spatial Daylight Autonomy – SDA metric of up to 18% in different future scenarios. In conclusion, the need is outlined to refine predictions of outdoor daylight availability that allow improving daylight performance evaluations under climate change scenarios.
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Eames, M., T. Kershaw, and D. Coley. "The appropriate spatial resolution of future weather files for building simulation." Journal of Building Performance Simulation 5, no. 6 (November 2012): 347–58. http://dx.doi.org/10.1080/19401493.2011.608133.
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Nielsen, C. N., and J. Kolarik. "Utilization of Climate Files Predicting Future Weather in Dynamic Building Performance Simulation – A review." Journal of Physics: Conference Series 2069, no. 1 (November 1, 2021): 012070. http://dx.doi.org/10.1088/1742-6596/2069/1/012070.
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Abstract As the climate is changing and buildings are designed with a life expectancy of 50+ years, it is sensible to take climate change into account during the design phase. Data representing future weather are needed so that building performance simulations can predict the impact of climate change. Currently, this usually requires one year of weather data with a temporal resolution of one hour, which represents local climate conditions. However, both the temporal and spatial resolution of global climate models is generally too coarse. Two general approaches to increase the resolution of climate models - statistical and dynamical downscaling have been developed. They exist in many variants and modifications. The present paper aims to provide a comprehensive overview of future weather application as well as critical insights in the model and method selection. The results indicate a general trend to select the simplest methods, which often involves a compromise on selecting climate models.
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Alhindawi, Ibrahim, and Carlos Jimenez-Bescos. "Assessing the Performance Gap of Climate Change on Buildings Design Analytical Stages Using Future Weather Projections." Environmental and Climate Technologies 24, no. 3 (November 1, 2020): 119–34. http://dx.doi.org/10.2478/rtuect-2020-0091.
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AbstractWith the higher pace of climate change, temperatures are rising each year, resulting in various effects on the thermal status of buildings. This paper takes the opportunity of analysing different scenarios of greenhouse gas (GHG) emissions using hourly weather data of future projections by implementing EPW weather files on EnergyPlus software dynamic simulations, coupled with architectural science methods of climate analysis, to test the effect of high and medium-high emission scenarios for the 2050s and 2080s future timelines on thermal comfort range, passive zones potential, and heating/cooling periods, as compared to the weather data from 2003–2017. Simulations results have shown a remarkable effect on the scale of daily cooling hours and monthly coverage under the high GHG emission scenario, expanding its range by 60 %, with 6 hours on summer peak days and 3 months/year, as well as an annual decrease in heating period by 33.3 %. Thermal comfort zones of tested periods have also witnessed an alternation, translating the effect on the passive cooling and passive heating zones’ way of variating, where the ranges are pushed towards their potential limits. Results have also demonstrated that if future weather data is not included in simulations, a weather-related performance gap is generated.
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Jentsch, Mark F., AbuBakr S. Bahaj, and Patrick A. B. James. "Climate change future proofing of buildings—Generation and assessment of building simulation weather files." Energy and Buildings 40, no. 12 (January 2008): 2148–68. http://dx.doi.org/10.1016/j.enbuild.2008.06.005.
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Adekunle, Timothy Oluseun. "Summer performance, comfort, and heat stress in structural timber buildings under moderate weather conditions." Smart and Sustainable Built Environment 8, no. 3 (July 3, 2019): 220–42. http://dx.doi.org/10.1108/sasbe-11-2018-0059.
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Purpose The purpose of this paper is to examine the Summer performance, comfort, and heat stress in structural timber buildings. The research utilises building simulation as a tool to investigate the performance of the case study buildings under non-extreme weather conditions. Design/methodology/approach The research explores three UK sites using the test reference year (TRY) weather files for the current and future weather conditions. The study focuses on the Summer performance and heat stress in non-extreme weather conditions; therefore, the Design Summer Year (DSY) weather files are not used for the simulations. The simulation data are calibrated and validated using the measured data from the field study. Findings The results revealed the mean predicted temperatures varied from 20.2–20.8°C for the 2000s. The mean temperatures for the 2030s ranged from 23.1 to 24.2°C. Higher temperatures are predicted at the buildings in the Southeast site than the Midlands and the Northwest sites. The results revealed that there is no significant improvement in the thermal environment when the floor area and the floor-to-ceiling height are increased. However, the study showed that the integration of different design interventions can improve the future performance and resilience of the buildings in various weather conditions. Research limitations/implications By applying the wet-bulb globe temperature (WBGT) and the Universal Thermal Comfort Index (UTCI) mathematical models to calculate the heat stress at the buildings, the study proposes the WBGT of 20.0°C and the UTCI of 24.1°C as possible heat stress indicators for occupants of the buildings in the 2030s. Practical implications On the one hand, the results revealed the maximum temperatures in some of the case study buildings exceed the comfort threshold (28°C). On the other hand, the study showed that occupants of the buildings are not prone to extreme Summertime overheating and heat stress under moderate weather conditions. However, different outcomes may be predicted if DSY weather files for the selected sites are considered. Originality/value This study is the first reported work to explore building simulation and mathematical equations to investigate Summer performance, comfort and heat stress indexes in timber buildings under moderate weather conditions in different regional sites in the UK.
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Bender, Fabiani Denise, and Paulo Cesar Sentelhas. "Solar Radiation Models and Gridded Databases to Fill Gaps in Weather Series and to Project Climate Change in Brazil." Advances in Meteorology 2018 (July 5, 2018): 1–15. http://dx.doi.org/10.1155/2018/6204382.
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The quantification of climate change impacts on several human activities depends on reliable weather data series, without gaps and long enough to build up future climate. Based on that, this study aimed to evaluate the performance of temperature-based models for estimating global solar radiation and gridded databases (AgCFSR, AgMERRA, NASA/POWER, and XAVIER) as alternative ways for filling gaps in historical weather series (1980–2009) in Brazil and to project climate change scenarios based on measured and gridded weather data. Projections for mid- and end-of-century periods (2040–2069 and 2070–2099), using seven global climate models from CMIP5 under intermediate (RCP4.5) and high (RCP8.5) emission scenarios, were performed. The Bristow–Campbell model was the one that best estimated solar radiation, whereas the XAVIER gridded database was the closest to observed weather data. Future climate projections, under RCP4.5 and RCP8.5 scenarios, as expected, showed warmer conditions for all scenarios over Brazil. On the contrary, rainfall projections are more uncertain. Despite that, the rainfall amounts will be reduced in the North-Northeast region and increased in Southern Brazil. No significant differences between projections using the observed and XAVIER gridded database were observed; therefore, such a database showed to be reliable for both to fill gaps and to generate climate change scenarios.
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Eames, M., T. Kershaw, and D. Coley. "The creation of wind speed and direction data for the use in probabilistic future weather files." Building Services Engineering Research and Technology 32, no. 2 (October 20, 2010): 143–58. http://dx.doi.org/10.1177/0143624410381624.
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Tamer, T., D. Baker, I. Gursel Dino, and C. Meral Akgul. "Future performance evaluation of PCM integrated buildings under changing climate." IOP Conference Series: Earth and Environmental Science 1085, no. 1 (September 1, 2022): 012058. http://dx.doi.org/10.1088/1755-1315/1085/1/012058.
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Abstract The high energy consumption and associated carbon emissions due to the heating and cooling of buildings create a heavy environmental burden. One of the cost-efficient solutions to reduce the heating and cooling demands is to incorporate phase change materials (PCMs) in the building components, increasing the thermal mass of the building and providing latent heat thermal storage. However, the rising temperatures over the years will alter the effectiveness of PCM in building envelopes. In this study, four cities in Turkey with different climatic characteristics were selected. For each city, future weather files representing the climatic conditions of 2050 and 2080 were generated from the current weather data using CCWorldWeatherGen. A typical office building that utilizes gypsum wallboards was modeled with EnergyPlus as a reference case. Alternative energy models were generated by modifying the wallboard compositions (PCM melting temperature: 19-27°C). The building’s annual heating and cooling energy demands were calculated for each city, year, and wallboard alternative. Generated data were analyzed to evaluate the future efficiency of the wallboards with the changing climate over the years in order to maximize the long-term performance gains from PCM incorporating wallboards. The results showed that the selection of the optimum PCM melting temperature of a location should not only depend on thermo-physical and layer properties of the PCM wallboard as the optimum melting temperature of the PCM is subject to change with rising temperatures. The impact of climate change should be considered to fully evaluate the long-term performance of the PCM wallboard in terms of energy use and CO2 emissions.
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Morss, Rebecca E., and F. Martin Ralph. "Use of Information by National Weather Service Forecasters and Emergency Managers during CALJET and PACJET-2001." Weather and Forecasting 22, no. 3 (June 1, 2007): 539–55. http://dx.doi.org/10.1175/waf1001.1.
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Abstract Winter storms making landfall in western North America can generate heavy precipitation and other significant weather, leading to floods, landslides, and other hazards that cause significant damage and loss of life. To help alleviate these negative impacts, the California Land-falling Jets (CALJET) and Pacific Land-falling Jets (PACJET) experiments took extra meteorological observations in the coastal region to investigate key research questions and aid operational West Coast 0–48-h weather forecasting. This article presents results from a study of how information provided by CALJET and PACJET was used by National Weather Service (NWS) forecasters and forecast users. The primary study methodology was analysis of qualitative data collected from observations of forecasters and from interviews with NWS personnel, CALJET–PACJET researchers, and forecast users. The article begins by documenting and discussing the many types of information that NWS forecasters combine to generate forecasts. Within this context, the article describes how forecasters used CALJET–PACJET observations to fill in key observational gaps. It then discusses researcher–forecaster interactions and examines how weather forecast information is used in emergency management decision making. The results elucidate the important role that forecasters play in integrating meteorological information and translating forecasts for users. More generally, the article illustrates how CALJET and PACJET benefited forecasts and society in real time, and it can inform future efforts to improve human-generated weather forecasts and future studies of the use and value of meteorological information.
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Hosseini, Mirata, Anahita Bigtashi, and Bruno Lee. "Generating future weather files under climate change scenarios to support building energy simulation – A machine learning approach." Energy and Buildings 230 (January 2021): 110543. http://dx.doi.org/10.1016/j.enbuild.2020.110543.
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Maduabuchi, Chika, Chinedu Nsude, Chibuoke Eneh, Emmanuel Eke, Kingsley Okoli, Emmanuel Okpara, Christian Idogho, Bryan Waya, and Catur Harsito. "Renewable Energy Potential Estimation Using Climatic-Weather-Forecasting Machine Learning Algorithms." Energies 16, no. 4 (February 5, 2023): 1603. http://dx.doi.org/10.3390/en16041603.
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The major challenge facing renewable energy systems in Nigeria is the lack of appropriate, affordable, and available meteorological stations that can accurately provide present and future trends in weather data and solar PV performance. It is crucial to find a solution to this because information on present and future solar PV performance is important to renewable energy investors so that they can assess the potential of renewable energy systems in various locations across the country. Although Nigerian weather provides favorable weather conditions for clean power generation, there is little penetration of renewable energy systems in the region, since over 95% of the power is fossil-fuel-generated. This is because there has been no detailed report showing the potential of clean power generation systems due to the dysfunctional meteorological stations in the country. This paper sought to fill this knowledge gap by providing a machine-learning-inspired forecasting of environmental weather parameters that can be used by manufacturing companies in evaluating the profitability of siting renewable energy systems in the region. Crucial weather parameters such as daily air temperature, relative humidity, atmospheric pressure, wind speed, and rainfall were obtained from NASA for a period of 19 years (viz. 2004–2022), resulting in the collection of 6664 high-resolution data points. These data were used to build diverse regressive neural networks with varying hyperparameters to find the best network arrangement. In summary, a low mean-squared error of 7 × 10−3 and high regression correlations of 96% were obtained during the training.
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Yaqubi, Obaidullah, Auline Rodler, Sihem Guernouti, and Marjorie Musy. "Creation and application of future typical weather files in the evaluation of indoor overheating in free-floating buildings." Building and Environment 216 (May 2022): 109059. http://dx.doi.org/10.1016/j.buildenv.2022.109059.
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Doutreloup, Sébastien, Xavier Fettweis, Ramin Rahif, Essam Elnagar, Mohsen S. Pourkiaei, Deepak Amaripadath, and Shady Attia. "Historical and future weather data for dynamic building simulations in Belgium using the regional climate model MAR: typical and extreme meteorological year and heatwaves." Earth System Science Data 14, no. 7 (July 6, 2022): 3039–51. http://dx.doi.org/10.5194/essd-14-3039-2022.
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Abstract. Increasing temperatures due to global warming will influence building, heating, and cooling practices. Therefore, this data set aims to provide formatted and adapted meteorological data for specific users who work in building design, architecture, building energy management systems, modelling renewable energy conversion systems, or others interested in this kind of projected weather data. These meteorological data are produced from the regional climate model MAR (Modèle Atmosphérique Régional in French) simulations. This regional model, adapted and validated over Belgium, is forced firstly, by the ERA5 reanalysis, which represents the closest climate to reality and secondly, by three Earth system models (ESMs) from the Sixth Coupled Model Intercomparison Project database, namely, BCC-CSM2-MR, MPI-ESM.1.2, and MIROC6. The main advantage of using the MAR model is that the generated weather data have a high resolution (hourly data and 5 km) and are spatially and temporally homogeneous. The generated weather data follow two protocols. On the one hand, the Typical Meteorological Year (TMY) and eXtreme Meteorological Year (XMY) files are generated largely inspired by the method proposed by the standard ISO15927-4, allowing the reconstruction of typical and extreme years, while keeping a plausible variability of the meteorological data. On the other hand, the heatwave event (HWE) meteorological data are generated according to a method used to detect the heatwave events and to classify them according to three criteria of the heatwave (the most intense, the longest duration, and the highest temperature). All generated weather data are freely available on the open online repository Zenodo (https://doi.org/10.5281/zenodo.5606983, Doutreloup and Fettweis, 2021) and these data are produced within the framework of the research project OCCuPANt (https://www.occupant.uliege.be/ (last access: 24 June 2022) – ULiège).
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Ntinopoulos, Nikolaos, Marios Spiliotopoulos, Lampros Vasiliades, and Nikitas Mylopoulos. "Contribution to the Study of Forest Fires in Semi-Arid Regions with the Use of Canadian Fire Weather Index Application in Greece." Climate 10, no. 10 (September 30, 2022): 143. http://dx.doi.org/10.3390/cli10100143.
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Forest fires are of critical importance in the Mediterranean region. Fire weather indices are meteorological indices that produce information about the impact as well as the characteristics of a fire event in an ecosystem and have been developed for that reason. This study explores the spatiotemporal patterns of the FWI system within a study area defined by the boundaries of the Greek state. The FWI has been calculated and studied for current and future periods using data from the CFSR reanalysis model from the National Centers for Environmental Protection (NCEP) as well as data from NASA satellite programs and the European Commission for Medium-Range Weather Forecasts (ECWMF) in the form of netCDF files. The calculation and processing of the results were conducted in the Python programming language, and additional drought- and fire-related indices were calculated, such as the standardized precipitation index (SPI), number of consecutive 50-day dry periods (Dry50), the Fosberg fire weather index (FFWI), the days where the FWI exceeds values of 40 and 50 days (FWI > 40) and (days FWI > 50). Similar patterns can easily be noted for all indices that seem to have their higher values concentrated in the southeast of the country owing to the higher temperatures and more frequent drought events that affect the indices’ behavior in both the current and future periods.
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Chan, A. L. S. "Developing future hourly weather files for studying the impact of climate change on building energy performance in Hong Kong." Energy and Buildings 43, no. 10 (October 2011): 2860–68. http://dx.doi.org/10.1016/j.enbuild.2011.07.003.
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Trnka, M., J. Balek, M. Možný, E. Cienciala, P. Čermák, D. Semerádová, F. Jurečka, et al. "Observed and expected changes in wildfire-conducive weather and fire events in peri-urban zones and key nature reserves of the Czech Republic." Climate Research 82 (November 5, 2020): 33–54. http://dx.doi.org/10.3354/cr01617.
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Recent drought and a surge in days with weather conditions conducive to wildfire occurrence during 2015-2019 reminded the Czech Republic that it is not immune to this type of natural hazard. Although Central Europe has not been at the center of such events, observed climate data and climate projections suggest a tendency toward more years with wet and mild winters and dry and hot summers. To fill the existing knowledge gap, we used an ensemble of 9 fuel aridity metrics, including 3 dedicated fire weather indices, and evaluated their level of agreement with actual fire occurrence and their temporal trends. The analysis included peri-urban zones of the 36 largest cities and towns in the Czech Republic (home of 3.8 million inhabitants) and the 29 largest protected areas (covering 13.7% of the territory). Fire weather climatology, based on both the Fire Weather Index and the Forest Fire Danger Index, agreed well with the long-term frequency of fires both in peri-urban zones and within protected areas. Future projections based on regional and global model ensembles indicated a significant increase in fuel aridity and an increase in the area affected by fire-conducive conditions both around urban areas and within protected regions. In particular, the area affected by days with very high risk fire weather conditions is likely to increase significantly relative to the past 60 yr. However, the magnitude of the projected change depends to a large degree on the selected fire weather metric and whether RCM- or GCM-based scenarios are used.
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Koranteng, C., B. Simons, K. A. Gyimah, and S. Amos-Abanyie. "THE IDEALIZED AND THE REALISTIC VALIDATION STUDIES OF BUILDING SIMULATION MODELS IN GHANA." Journal of Green Building 17, no. 3 (June 1, 2022): 141–60. http://dx.doi.org/10.3992/jgb.17.3.141.
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ABSTRACT The research is based on the premise that in order for a building energy model to contribute to a sustainable energy future, the model’s accuracy must be ensured in order for the model’s results to be trusted. Therefore, validation processes in continuing Ghanaian building performance studies are outlined. The process started with long-term monitoring of low-rise, multi-storey and test cells structures. Combined with weather data from meteorological offices, reliance on synthetic weather files and local measurements, appropriate matching periods of weather data and measurements were used to simulate indoor parameters. Further, the simulated and measured data were in good agreement in terms of regression values (r2of 0.53–0.96). Energy use bills were used to validate energy loads of a multi-story building which resulted in a difference of 0.09% between the simulated and billed data. Furthermore, an approach of using the Coefficient of Variance for Root Mean Square Error (CV (RMSE)) was also presented. Considering the range of the regression values which could be due to the difficulty in the validating process; one can confidently rely on the outcome to predict building performance. Sampled challenges are the potential of synthetic weather files to overlook microclimatic conditions such as urban heat island effects; difficulty in predicting internal loads as comprehensive monitoring devices are lacking, e.g., occupancy sensors to monitor the actual number of people present at a time and their behaviour within spaces; system performance values which are known to decline with time, therefore, affecting measured versus simulated values; most firms not keeping energy bills and their unwillingness to provide the information to researchers; etc. The validated models can be used as scientific-based data and analysis to inform building designers decisions to reduce the economic and environmental burden in Ghana.
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Amin, Muhd Arshad, Hafiza Shukor, L. S. Yin, Farizul Hafiz Kasim, Noor Fazliani Shoparwe, Muaz Mohd Zaini Makhtar, and Abu Zahrim Yaser. "Methane Biogas Production in Malaysia: Challenge and Future Plan." International Journal of Chemical Engineering 2022 (October 14, 2022): 1–16. http://dx.doi.org/10.1155/2022/2278211.
Full textAbstract:
Biomethane is a sustainable energy that is produced from an organic and renewable resource. As the second-largest oil palm producer in the world, palm oil mill effluent (POME) is the primary source of biomethane generation in Malaysia. POME is the by-product of palm oil extraction and is extensively employed as a feedstock for the production of biomethane. Malaysia has an equatorial environment with humid and hot weather; this climate is conducive to the cultivation of numerous agricultural crops. A considerable number of agricultural wastes and residues are produced by agricultural crops, however, only 27% of them are used as fuel or to create useable products. Several publications have been published on the production of biomethane from POME; nevertheless, additional research is required on the use of other bioresources and technologies for biomethane production in Malaysia. In addition, there is a lack of comprehensive information on the future development of biomethane production in Malaysia; thus, to fill this gap, this review paper focuses on the challenges and future of Malaysia, which puts an emphasis on POME and also includes other alternative options of bioresources that can be the future feedstock for biomethane production in Malaysia. To the best of our knowledge, this is the first paper to provide a comprehensive overview of the biogas trend in Malaysia in terms of challenges and current biomethane development, as well as detailed information on a number of leading companies that are currently active in Malaysia biogas industry.
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Shaw, Emma, and Victor Sparrow. "Modeling acoustic impedance and atmospheric absorption around airports using high-fidelity weather data." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 264, no. 1 (June 24, 2022): 104–10. http://dx.doi.org/10.3397/nc-2022-698.
Full textAbstract:
It is known that environmental factors have an impact on the propagation of noise and as such, the integration of high-fidelity meteorological data into noise modeling programs has given hope for improved predictions of outdoor noise level, particularly in the field of aircraft noise. Through the use of these high-fidelity weather data sets taken over a specific region surrounding an airport, there is an ability to calculate adjusted coefficients for the acoustic impedance and atmospheric absorption of the environment in external programs. These new calculations offer insight into whether more formal noise modeling programs will benefit from the use of these high-fidelity meteorological files and how the accuracy of future aircraft noise predictions will be affected. [This research was funded by the U.S. Federal Aviation Administration Office of Environment and Energy through ASCENT, the FAA Center of Excellence for Alternative Jet Fuels and the Environment, project 62 through FAA Award Number 13-C-AJFE-PSU under the supervision of Chris Hobbs. Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the FAA.]