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globalchange > 过去全球变化的重建

DOI:	10.1371/journal.pone.0156849
论文题名:	Stochastic and Statistical Analysis of Utility Revenues and Weather Data Analysis for Consumer Demand Estimation in Smart Grids
作者:	S. M. Ali; C. A Mehmood; B. Khan; M. Jawad; U Farid; J. K. Jadoon; M. Ali; N. K. Tareen; S. Usman; M. Majid; S. M. Anwar
刊名:	PLOS ONE
ISSN:	1932-6203
出版年:	2016
发表日期:	2016-6-17
卷:	11, 期:6
语种:	英语
英文关键词:	Forecasting ; Meteorology ; Gaussian random variables ; Probability density ; Statistical data ; Stochastic processes ; Humidity ; Monte Carlo method
英文摘要:	In smart grid paradigm, the consumer demands are random and time-dependent, owning towards stochastic probabilities. The stochastically varying consumer demands have put the policy makers and supplying agencies in a demanding position for optimal generation management. The utility revenue functions are highly dependent on the consumer deterministic stochastic demand models. The sudden drifts in weather parameters effects the living standards of the consumers that in turn influence the power demands. Considering above, we analyzed stochastically and statistically the effect of random consumer demands on the fixed and variable revenues of the electrical utilities. Our work presented the Multi-Variate Gaussian Distribution Function (MVGDF) probabilistic model of the utility revenues with time-dependent consumer random demands. Moreover, the Gaussian probabilities outcome of the utility revenues is based on the varying consumer n demands data-pattern. Furthermore, Standard Monte Carlo (SMC) simulations are performed that validated the factor of accuracy in the aforesaid probabilistic demand-revenue model. We critically analyzed the effect of weather data parameters on consumer demands using correlation and multi-linear regression schemes. The statistical analysis of consumer demands provided a relationship between dependent (demand) and independent variables (weather data) for utility load management, generation control, and network expansion.
URL:	http://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0156849&type=printable
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资源类型:	期刊论文
标识符:	http://119.78.100.158/handle/2HF3EXSE/25345
Appears in Collections:	过去全球变化的重建影响、适应和脆弱性科学计划与规划气候变化与战略全球变化的国际研究计划气候减缓与适应气候变化事实与影响

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File Name/ File Size	Content Type	Version	Access	License
journal.pone.0156849.PDF（1882KB）	期刊论文	作者接受稿	开放获取	CC BY-NC-SA	View Download

作者单位:

Electrical Engineering Department, COMSATS Institute of IT, Abbottabad, KPK, Pakistan;Electrical Engineering Department, COMSATS Institute of IT, Abbottabad, KPK, Pakistan;Electrical Engineering Department, COMSATS Institute of IT, Abbottabad, KPK, Pakistan;Electrical Engineering Department, COMSATS Institute of IT, Lahore, Pakistan;Electrical Engineering Department, COMSATS Institute of IT, Abbottabad, KPK, Pakistan;Electrical Engineering Department, COMSATS Institute of IT, Abbottabad, KPK, Pakistan;Computer Science Department, COMSATS Institute of IT, Abbottabad, KPK, Pakistan;Electrical Engineering Department, COMSATS Institute of IT, Abbottabad, KPK, Pakistan;Electrical Engineering Department, COMSATS Institute of IT, Sahiwal, Pakistan;Computer Engineering Department, University of Engineering and Technology, Taxila, Pakistan;Software Engineering Department, University of Engineering and Technology, Taxila, Pakistan

Recommended Citation:

S. M. Ali,C. A Mehmood,B. Khan,et al. Stochastic and Statistical Analysis of Utility Revenues and Weather Data Analysis for Consumer Demand Estimation in Smart Grids[J]. PLOS ONE,2016-01-01,11(6)

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