A step-by-step application of Sandia method in developing typical meteorological years for different locations in Oman

Sawaqed, Naseem M.; Zurigat, Yousef H.; Al-Hinai, Hilal

doi:10.1002/er.1078

Cited by 37 publications

(26 citation statements)

References 10 publications

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“…The figure does not show clear separation among the TMY, TDY, and TGY meteorological parameters. This again affirms that the solar flux held more weight in generating these data sets (Sawaqed et al 2005).…”

Section: Comparison Of Tmy Tdy and Tgy Data Setssupporting

confidence: 73%

Temporal and Spatial Comparison of Gridded TMY, TDY, and TGY Data Sets

Habte

Lopez

Sengupta

et al. 2014

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Executive SummaryTypical Meteorological Year (TMY) data sets provide industry standard resource information for building designers and are commonly used by the solar industry to estimate photovoltaic and concentrating solar power system performance. They are the basis for system performance and economic models such as PVWatts, 1 System Advisor Model (SAM), 2 and Solar Prospector. 3 Historically, TMY data sets were only available for certain station locations, but current TMY data sets are available on the same grid as the National Solar Radiation Database (NSRDB) data and are referred to as the gridded TMY. This gridded TMY data is derived from the 10-km by 10-km gridded NSRDB data (including the years from 1998 to 2009). A gridded TMY file is created by concatenating 12 typical meteorological months from statistically analyzed and selected individual months from the entire set of available years. The selected months (which may be from different years) are then joined to make one serially complete year. A typical meteorological month is generated through cumulative distribution frequency (CDF) analysis in which a typical month is selected closer to the long-term monthly average CDF (Wilcox and Marion 2008). For example, in this case the month of January was selected from the 12 Januarys during the 1998 to 2009 period. The remaining months were selected in similar fashion. Ten indices were used to select a TMY month. These are described in Table 1. The indices are essential for selecting the typical month using a weighting factor for each parameter, which ultimately includes considering the monthly mean and median and the persistence of weather. The series of steps that were carried out to acquire the typical month is described in the Users Manual for TMY3 Data Sets (Wilcox and Marion 2008).A system performance analysis for photovoltaics and concentrating solar power projects should generally consider high weighting factors for the solar components; as such, some of the meteorological parameters in Table 1 might not be relevant for these applications. For this reason, typical direct (normal irradiance) year (TDY) and typical global (horizontal irradiance) year (TGY) data sets were also produced, whereby the weighting focused on the irradiance components of the data rather than the meteorological data. The weighting factor for each solar component is described in Table 1. The process of generating TDY and TGY data sets is tailored to generating solar component values that are as close as possible to the long-term means for those parameters (as opposed to the "typical" conditions defined by the usual TMY statistical analysis) and are not influenced by other parameters. In this report, a comparison of TMY, TDY, and TGY data sets were performed to better understand the impact of ancillary weather variables upon them.We analyzed the temporal and spatial variability of the typical year data sets. The spatial variability was calculated using the coefficient of variations of adjacent pixels on a monthly basis for the TD...

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Section: Comparison Of Tmy Tdy and Tgy Data Setssupporting

confidence: 73%

Temporal and Spatial Comparison of Gridded TMY, TDY, and TGY Data Sets

Habte

Lopez

Sengupta

et al. 2014

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“…The Sandia procedure is thoroughly explained in the work of Sawaqed et al (2005). The method in its original version is based on the daily averages of the nine parameters listed in Table 1 except the direct radiation.…”

Section: Tmy Developmentmentioning

confidence: 99%

“…For each month in the year, five best candidate years are selected according to the least weighted deviations. In order to reassure that this selection was not a result of some anomalies, further screening is performed by checking the deviation of the mean and median for each month from the long term means and medians and also checking the run count and length in each month as described by Sawaqed et al (2005).…”

Section: Tmy Developmentmentioning

confidence: 99%

“…In addition to the Sandia method, which is the classical development algorithm, heuristic methodologies are also used, in which, machine learning algorithms are used in developing weather files (Chakraborty et al 2016). Marion and Urban (1995) and Wilcox and Marion (2008) d Sawaqed et al ( 2005) e The relative humidity is used in place of dew point temperature.…”

Section: Tmy Developmentmentioning

confidence: 99%

See 1 more Smart Citation

Variant Developments of Typical Meteorological Years (TMYs) for Seeb, Oman and their Impact on Energy Simulation of Residential Buildings

Al-Azri

Al‐Saadi

2018

Jou. Eng. Res.

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Typical meteorological years (TMYs) are widely used for the analysis and simulation of energy-intensive systems. The reliability of a developed typical year depends on the accuracy of the historical record of weather data as well as the fitness of the developed approach to the application. In this work, a TMY for Seeb area in the Muscat Governorate, Oman was developed using different approaches. The developed TMYs are compared to the current commonly used TMY which is based on 1985-2001 records that have many gaps and anomalies and hence have intensive interpolation treatment. The different TMYs were compared by simulating energy consumption of a typical residential building and also by studying applicability of passive cooling strategies. The findings showed that the variation in energy consumption is minimal for the different TMY development approaches for the same set of historical records but the difference is very significant when the comparison is based on the two sets from the two periods of records.

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“…Several methods for generating typical data have been developed. These include Test Reference Year (TRY) [7][8][9][10][11][12], Typical Meteorological Year (TMY) [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32], Weather Year for Energy Calculation (WYEC), Typical Weather Years for International Locations (IWEC) and others. They are also essential for the numerical analysis of sustainable and renewable energy systems.…”

Section: Introductionmentioning

confidence: 99%