Stochastic and Statistical Analysis of Utility Revenues and Weather Data Analysis for Consumer Demand Estimation in Smart Grids

Autor: J. K. Jadoon, Syed Muhammad Anwar, C. A. Mehmood, U. Farid, Muhammad Jawad, Muhammad Majid, Sahibzada Muhammad Ali, Mazhar Ali, Bilal Khan, N. Tareen, S. Usman
Rok vydání: 2016
Předmět:
Atmospheric Science
Computer science
020209 energy
media_common.quotation_subject
lcsh:Medicine
02 engineering and technology
Research and Analysis Methods
Load management
Mathematical and Statistical Techniques
Meteorology
Computer Systems
0202 electrical engineering
electronic engineering
information engineering
Econometrics
Gaussian Random Variables
Revenue
Humans
Computer Simulation
Statistical Methods
lcsh:Science
Weather
media_common
Statistical Data
Stochastic Processes
Multidisciplinary
Variables
Stochastic process
lcsh:R
020208 electrical & electronic engineering
Probabilistic logic
Statistical model
Random Variables
Humidity
Models
Theoretical
Probability Theory
Monte Carlo method
Variable (computer science)
Probability Density
Smart grid
Data Interpretation
Statistical
Physical Sciences
Earth Sciences
lcsh:Q
Mathematics
Statistics (Mathematics)
Research Article
Forecasting
Zdroj: PLoS ONE
PLoS ONE, Vol 11, Iss 6, p e0156849 (2016)
ISSN: 1932-6203
Popis: 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.
Databáze: OpenAIRE
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