Clustering-based probability distribution model for monthly residential building electricity consumption analysis

This paper targets the future energy sustainability and aims to estimate the potential energy production from installing photovoltaic (PV) systems on the rooftop of apartment’s residential buildings, which represent the largest building sector. Analysis of the residential building typologies was carried out to select the most used residential building types in terms of building roof area, number of floors, and the number of apartments on each floor. A computer simulation tool has been used to calculate the electricity production for each building type, for three different tilt angles to estimate the electricity production. Tilt angle, spacing between the arrays, the building shape, shading from PV arrays, and other roof elements were analyzed for optimum and maximum electricity production. The electricity production for each household has been compared to typical household electricity consumption and its future consumption in 2030. The results show that installing PV systems on residential buildings can speed the transition to renewable energy and energy sustainability. The electricity production for building types with 2–4 residential units can surplus their estimated future consumption. Building types with 4–8 residential units can produce their electricity consumption in 2030. Building types of 12–24 residential units can produce more than half of their 2030 future consumption.

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Probability distribution model of stress impact factor for corrosion pits of high-strength prestressing wires

Engineering Structures ◽

10.1016/j.engstruct.2020.111686 ◽

2021 ◽

Vol 230 ◽

pp. 111686

Author(s):

Xiguang Liu ◽

Weiping Zhang ◽

Xianglin Gu ◽

Zhiwen Ye

Keyword(s):

Probability Distribution ◽

Impact Factor ◽

High Strength ◽

Distribution Model ◽

Corrosion Pits

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Development of a probability distribution model for the SCFs in tubular X-connections retrofitted with FRP

Structures ◽

10.1016/j.istruc.2021.10.033 ◽

2022 ◽

Vol 36 ◽

pp. 233-247

Author(s):

Hossein Nassiraei ◽

Pooya Rezadoost

Keyword(s):

Probability Distribution ◽

Distribution Model

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The Strength Evaluation and Reliability Analysis of Periodic Symmetric Struts Support

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.462-463.1164 ◽

2011 ◽

Vol 462-463 ◽

pp. 1164-1169

Author(s):

Jing Xiang Yang ◽

Ya Xin Zhang ◽

Mamtimin Gheni ◽

Ping Ping Chang ◽

Kai Yin Chen ◽

...

Keyword(s):

Distribution Function ◽

Probability Distribution ◽

Stress Distribution ◽

Least Squares ◽

Reliability Analysis ◽

Least Squares Method ◽

Nonlinear Least Squares ◽

Distribution Model ◽

Different Types ◽

Nonlinear Least Squares Method

In this paper, strength evaluations and reliability analysis are conducted for different types of PSSS(Periodically Symmetric Struts Supports) based on the FEA(Finite Element Analysis). The numerical models are established at first, and the PMA(Prestressed Modal Analysis) is conducted. The nodal stress value of all of the gauss points in elements are extracted out and the stress distributions are evaluated for each type of PSSS. Then using nonlinear least squares method, curve fitting is carried out, and the stress probability distribution function is obtained. The results show that although using different number of struts, the stress distribution function obeys the exponential distribution. By using nonlinear least squares method again for the distribution parameters a and b of different exponential functions, the relationship between number of struts and distribution function is obtained, and the mathematical models of the stress probability distribution functions for different supports are established. Finally, the new stress distribution model is introduced by considering the DSSI(Damaged Stress-Strength Interference), and the reliability evaluation for different types of periodically symmetric struts supports is carried out.

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Risk Planning with Discrete Distribution Analysis Applied to Petroleum Spills

International Journal of Risk and Contingency Management ◽

10.4018/ijrcm.2013100105 ◽

2013 ◽

Vol 2 (4) ◽

pp. 61-78 ◽

Cited By ~ 9

Author(s):

Roy L. Nersesian ◽

Kenneth David Strang

Keyword(s):

Probability Distribution ◽

Probability Distributions ◽

Low Cost ◽

Discrete Distribution ◽

Distribution Model ◽

Distribution Analysis ◽

Distribution Models ◽

Discrete Probability ◽

Nonparametric Statistical ◽

Spreadsheet Software

This study discussed the theoretical literature related to developing and probability distributions for estimating uncertainty. A theoretically selected ten-year empirical sample was collected and evaluated for the Albany NY area (N=942). A discrete probability distribution model was developed and applied for part of the sample, to illustrate the likelihood of petroleum spills by industry and day of week. The benefit of this paper for the community of practice was to demonstrate how to select, develop, test and apply a probability distribution to analyze the patterns in disaster events, using inferential parametric and nonparametric statistical techniques. The method, not the model, was intended to be generalized to other researchers and populations. An interesting side benefit from this study was that it revealed significant findings about where and when most of the human-attributed petroleum leaks had occurred in the Albany NY area over the last ten years (ending in 2013). The researchers demonstrated how to develop and apply distribution models in low cost spreadsheet software (Excel).

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