Solar radiation along with other weather variables are commonly processed on typical meteorological years (TMYs) to be applied in the design of various energy systems. However, in several regions of ...the world, solar radiation data usually lacks a suitable and/or representative measurement, which leads to its modeling and prediction to properly fill this information in the databases. Consequently, the accuracy of these models can influence the viability and proper design of such energy systems. Within this context, the present contribution aims to assess the quality of solar radiation data included in the most recent TMY databases with Brazilian data and how that quality can influence the selection of months that create TMYs as well as the building performance simulation (BPS) results. Because two different approaches to generate the solar radiation data are used, we evaluate the global horizontal irradiation data in the two latest versions of recent Brazilian TMY databases against the corresponding satellite-derived ones obtained from the POWER database (NASA). Simultaneously, as another alternative approach, global solar radiation data are calculated for the same studied locations and period through the modeling method used to generate the current version of the International Weather for Energy Calculations (IWEC2), and its performance is also compared against the corresponding reanalysis data (POWER). Finally, a set of case studies applying the local building performance regulations are exhaustively analyzed to quantify the impact of the uncertainty of solar radiation models on BPS results throughout Brazil. The results indicate that the accuracy of solar radiation models can highly influence the resulting TMY configurations. These changes can drive differences up to 40% on the prediction of the ideal annual loads of the residential buildings while, regardless of design performance, differences lower than 10% are found for the commercial case studies in most locations. Conversely, the prediction of peak loads for cooling shows to be more sensitive to the climate data changes in the commercial buildings than in the residential ones.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Many of the popular building energy simulation programs around the world are reaching maturity — some use simulation methods (and even code) that originated in the 1960s. For more than two decades, ...the US government supported development of two hourly building energy simulation programs, BLAST and DOE-2. Designed in the days of mainframe computers, expanding their capabilities further has become difficult, time-consuming, and expensive. At the same time, the 30 years have seen significant advances in analysis and computational methods and power — providing an opportunity for significant improvement in these tools.
In 1996, a US federal agency began developing a new building energy simulation tool, EnergyPlus, building on development experience with two existing programs: DOE-2 and BLAST. EnergyPlus includes a number of innovative simulation features — such as variable time steps, user-configurable modular systems that are integrated with a heat and mass balance-based zone simulation — and input and output data structures tailored to facilitate third party module and interface development. Other planned simulation capabilities include multizone airflow, and electric power and solar thermal and photovoltaic simulation. Beta testing of EnergyPlus began in late 1999 and the first release is scheduled for early 2001.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
The IPCC and many others predict significant changes to our climates over the rest of this century, including average temperature increases for 2–5°C. However, we can see possible indications of ...change already – increasing frequency of severe storms and other weather events. However, many of the major weather data sets used around the world for building energy simulation are more than 15 years old. Does it matter? This paper compares several of the major data sets used in building performance simulation against newer data derived from the past 15 years. Ten of the past 15 years are the hottest on record and this rapidly changing climate already is evident in the temperature record. We use energy simulation to demonstrate how the various data sets impact energy use. In addition, the design conditions for heating and cooling calculations are already seeing slight changes over the past 20 years. Data for 12 locations around the world is used to demonstrate the changing climate that we already see.
Practical application
: This paper encourages building designers to use the most up-to-date climatic data in their design and evaluation of building performance.
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Some individuals with systemic sclerosis (SSc) report positive mental health, despite severe disease manifestations, which may be associated with resilience, but no resilience measure has been ...validated in SSc. This study was undertaken to assess the validity, reliability, and differential item functioning (DIF) between English- and French-language versions of the 10-item Connor-Davidson Resilience Scale (CD-RISC-10) in SSc.
Eligible participants were enrolled in the Scleroderma Patient-centered Intervention Network Cohort and completed the CD-RISC-10 between August 2022 and January 2023. We used confirmatory factor analysis (CFA) to evaluate the CD-RISC-10 factor structure and conducted DIF analysis across languages with Multiple Indicators Multiple Causes models. We tested convergent validity with another measure of resilience and measures of self-esteem and depression and anxiety symptoms. We assessed internal consistency and test-retest reliability using Cronbach's alpha and intraclass correlation coefficient (ICC).
A total of 962 participants were included in this analysis. CFA supported a single-factor structure (Tucker-Lewis index = 0.99, comparative fit index = 0.99, root mean square error of approximation = 0.08 90% confidence interval (90% CI) 0.07, 0.09). We found no meaningful DIF. Internal consistency was high (α = 0.93 95% CI 0.92, 0.94), and we found that correlations with other measures of psychological functioning were moderate to large (|r| = 0.57-0.78) and confirmed study hypotheses. The scale showed good 1-2-week test-retest reliability (ICC 0.80 95% CI 0.75, 0.85) in a subsample of 230 participants.
The CD-RISC-10 is a valid and reliable measure of resilience in SSc, with score comparability across English and French versions.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Building performance simulation tools have significantly improved in quality and depth of analysis capability over the past 35 years. Yet, despite these increased capabilities, simulation programs ...still depend on user entry for significant data about building components, loads, and other typically scheduled inputs. This often forces users to estimate values or find previously compiled sets of data for these inputs. Often there is little information about how the data were derived, what purposes it is fit for, which standards apply, uncertainty associated with each data field as well as a general description of the data.
A similar problem bedeviled access to weather data and Crawley et al. 1999. Improving the weather information available to simulation programs, In: Proceedings of building simulation ‘99, vol. 2,Kyoto, Japan, 13–15 September 1999. IBPSA, p. 529–36. described a generalized weather data format developed for use with two energy simulation programs, which has subsequently led to a repository which is accessed by thousands of practitioners each year.
This paper describes a generalized format and data documentation for such user inputs—whether it is building envelope components, scheduled loads, or environmental emissions—the widgets upon which all models are dependant. We present several examples including building envelope component, a scheduled occupant load, and environmental emissions and speculate on how such data might be incorporated in existing data schemes and simulation tools.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
DOE has developed a new building energy performance simulation program that combines the merits of BLAST and DOE-2 with new functions. The new EnergyPlus code is primarily a simulation engine that ...features an integrated solution technique. The overall program structure is described, as are the three basic components: simulation manager, heat balance simulation module, and building systems simulation module. EnergyPlus can be applied to calculate heat balance loads and to analyze integrated loads, system, and plant calculations in the same time step.
ENERGYPLUS: NEW, CAPABLE, AND LINKED Crawley, Drury B.; Lawrie, Linda K.; Pedersen, Curtis O. ...
Journal of architectural and planning research,
12/2004, Volume:
21, Issue:
4
Journal Article
Peer reviewed
A new building energy simulation program, known as EnergyPlus, was first released in April 2001. EnergyPlus builds on the capabilities and features of BLAST and DOE-2 and includes many simulation ...features, such as variable time steps, configurable modular systems that are integrated with a heat balance-based zone simulation, and input and output data structures tailored to facilitate third party module and interface development — features that have not been available together in a mainstream building energy simulation program. Other simulation capabilities include three thermal comfort models, extensive daylighting and advanced fenestration capabilities, multi-zone airflow modeling, more robust HVAC equipment models, more flexible system modeling, and photovoltaic simulation. Currently, more than 10 private sector companies have stated their intentions to create user interfaces for EnergyPlus. Since EnergyPlus was released in April 2001, more than 20,000 copies have been downloaded, with users in more than 90 countries.
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