Generation of Hourly Ambient Temperature from Generalized Cumulative Frequency Distributions

2004 ◽  
Vol 126 (2) ◽  
pp. 683-695 ◽  
Author(s):  
V. V. Satyamurty ◽  
P. Ravikumar
Keyword(s):  
Standard Deviation ◽  
Ambient Temperature ◽  
Data Base ◽  
Present Article ◽  
Large Data ◽  
Cumulative Frequency ◽  
Frequency Distributions ◽  
Large Data Base ◽  
Ambient Temperatures ◽  
Monthly Average

The present article is devoted to make available a method to generate hourly ambient temperature values when the monthly average hourly ambient temperature, T¯ah is known. Cumulative frequency distributions for hourly ambient temperature have been developed employing normalized variables. The normalized variables used in the present study contain not only the monthly average hourly ambient temperature but also the maximum, Tah,max, and minimum, Tah,min. In order that the frequency distributions developed in the present study become available for the purpose of calculating hourly ambient temperature when T¯ah only is known, correlations to estimate Tah,max and Tah,min from known parameters have been presented. The ability of the present distributions to generate hourly ambient temperatures during a day has also been established. A large data base of 239 locations has been employed in developing and validating the distributions and the needed additional correlations. The predicted hourly ambient temperature values have been found to be in agreement with data values within a % rms difference of 0.30 (when Tah is in K) and the standard deviation has been found to be 0.85 K. The agreement between the predicted hourly ambient temperatures during a day employing the present distributions and the data values has been excellent.

Cumulative Frequency Distributions for Daily Global Illuminance

Solar Energy ◽  
2004 ◽  
Author(s):  
V. V. Satyamurty ◽  
P. RaviKumar
Keyword(s):  
Standard Deviation ◽  
Data Base ◽  
Linear Form ◽  
Large Data ◽  
Cumulative Frequency ◽  
Clearness Index ◽  
Frequency Distributions ◽  
Large Data Base ◽  
Average Value ◽  
Monthly Average

The present article describes the development of cumulative frequency distributions (similar to clearness index distributions) for daily global horizontal illuminance based on the modeled data of 56 US locations. Normalized variables have been employed to account for seasonal and location dependencies. The normalized variables contain the maximum and minimum illuminance values in the array of daily values in addition to the daily illuminance (the variable to be related to cumulative frequency) and the monthly average daily illuminance. The distributions thus developed discrete in terms of the normalized monthly average daily global illuminance have been presented in a convenient linear form which accommodates continuous values for the parameter. When data are not available, the maximum and minimum of the daily values needed in estimating the parameter first and eventually the daily illuminance values have been correlated to other more readily available predictors. Present distributions have been validated by comparing against the daily illuminance values available in the large data base employed. Daily global illuminance has been predicted with a standard deviation of 23.44 klux and rms difference of 4.64%. The present correlations can be conveniently applied to obtain the 30 or 31 daily illuminance values in a month from the monthly average value.

Frequency Distributions of Daily Ambient Temperatures Through Generalized Parameters

1999 ◽  
Vol 121 (3) ◽  
pp. 176-183 ◽  
Author(s):  
V. V. Satyamurty ◽  
K. Sarath Babu
Keyword(s):  
Ambient Temperature ◽  
Present Article ◽  
Frequency Distributions ◽  
Ambient Temperatures ◽  
Average Value ◽  
Monthly Average ◽  
Generalized Distributions ◽  
Standard Deviations ◽  
Generalized Parameters

Studies on frequency distributions of ambient temperature available in the literature are limited and are developed assuming that the distributions are symmetric about its average value. The present article examines the suitability of a new parameter, T¯aˆ, and a variable, Taˆ to depict generalized distributions. T¯aˆ is a normalized monthly average daily ambient temperature, while Taˆ is a normalized daily ambient temperature. They comprise maximum and minimum ambient temperatures (Ta,max and Ta,min), in addition to monthly average daily ambient temperature, T¯a, and daily ambient temperature, Ta. Correlations have been developed to predict Ta,max and Ta,min needed, in the absence of data. The distributions, developed in terms of Taˆ and T¯aˆ have been suitably represented and tested over 269 locations, latitudes ranging from 8°N to 64°N. The daily ambient temperature values have been predicted within a %rms difference and standard deviations of 0.43% (when Ta’s are in K) and 1.19 K with the expressions developed in the present study.

Generation of Monthly Average Hourly Ambient Temperatures From Monthly Average Daily Temperature

Solar Energy ◽  
2004 ◽  
Author(s):  
P. Ravikumar
Keyword(s):  
Standard Deviation ◽  
Ambient Temperature ◽  
Daily Temperature ◽  
Average Daily Temperature ◽  
Ambient Temperatures ◽  
Additional Information ◽  
Monthly Average

A method to generate monthly average hourly ambient temperature values, Tah,n, in terms of the monthly average daily ambient temperature, Ta and latitude, φ is presented here. The present correlations do not require the additional information needed in applying the correlations available in [1,2,3]. The predicted monthly average hourly ambient temperature, Tah,n values have been found to be in agreement with data values within a % rms difference of 0.43 (when Tah,n is in K) and the standard deviation has been found to be 1.243 K, for 56 primary locations of TMY2, comparable to the prediction of Erbs, Klein and Beckman [1].

A two-level architecture for a large data base

1976 ◽  
Vol 10 (4) ◽  
pp. 23-23 ◽  
Author(s):  
Tomas Lang
Keyword(s):  
Data Base ◽  
Large Data ◽  
Large Data Base

Numeric data bases on‐line

Online Review ◽  
1977 ◽  
Vol 1 (3) ◽  
pp. 207-215 ◽  
Author(s):  
James A. Luedke
Keyword(s):  
Data Base ◽  
Distributed Processing ◽  
Large Data ◽  
Training Needs ◽  
Data Bases ◽  
Textual Information ◽  
Large Data Base ◽  
User Training ◽  
On Line ◽  
Numeric Data

The activities that are at present furthering the visibility of numeric data bases and systems are discussed. A tentative estimate of the numbers of existing numeric data bases and systems in various categories of accessibility (on‐line, batch, and remotely accessible) and availability (public, restricted, and in‐house) is made. Numeric data bases and systems are becoming offered by information retrieval services through many of the channels that made bibliographic and textual information systems successful. These include remote accessibility and marketing by large data base vendors. The user community for numeric data bases is generally more restricted, with the possible exception of business and financial numeric data bases. User training needs are generally greater as well. Numeric data bases may nevertheless reach large user audiences with the advent of distributed processing and, ultimately, data base maps which can automatically locate and link together data bases in many locations.

Cumulative Frequency Distributions for Hourly Global Illuminance

2011 ◽  
Author(s):  
V. V. Satyamurty ◽  
P. RaviKumar
Keyword(s):  
Present Article ◽  
Cumulative Frequency ◽  
Frequency Distributions

In the present article, cumulative frequency distributions for hourly global horizontal illuminance based on the modeled data of 56 US locations have been presented. Measured illuminance information from other sources also has been utilized. Suitable normalized variables have been introduced to depict the generalized cumulative frequency distributions for hourly global illuminance. The cumulative distributions developed based on representative set locations have been shown to be applicable for another set of locations. In addition, the present article brings out, the major differences between daily and hourly cumulative frequency distributions and, a method to generate hourly (synthetic) global illuminance data from the frequency distributions.

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