Analysis and modeling of regional electric power consumption subject to influence of external factors

2021 ◽  
Vol 3 ◽  
pp. 12-17
Author(s):  
Sergey Karpenko ◽  
Nadezhda Karpenko
Keyword(s):  
Time Series ◽  
Power Consumption ◽  
Electric Power ◽  
Multiplicative Model ◽  
External Factors ◽  
Electric Power Consumption ◽  
Climate Factors ◽  
Factors Affecting ◽  
Correlation And Regression Analysis ◽  
Forecasting Methods

Electric power consumption along with a large variety of factors affecting it can be forecasted and modelled by using econometric forecasting methods, including time series and correlation and regression analysis. For the purpose of this research, electric power consumption in the Moscow Region, Russia, was modelled with consideration of economic and climate factors based on 2019–2020 power usage data. A multiplicative model for regional electric power consumption and correlations between electric power consumption and an air temperature as well as a total number of cloudy days a month were built. The results will be helpful for analyzing and forecasting of processes involved in power consumption.

scholarly journals Fuzzy Time-Series Model of Electric Power Consumption

2000 ◽  
Vol 4 (3) ◽  
pp. 188-194 ◽  
Author(s):  
Kazuhiro Ozawa ◽  
◽  
’Takahide Niimura ◽  
Tomoaki Nakashima ◽  
Keyword(s):  
Time Series ◽  
Power Consumption ◽  
Electric Power ◽  
Time Series Data ◽  
Simple Procedure ◽  
Estimation Procedure ◽  
Fuzzy Time Series ◽  
Ar Model ◽  
Electric Power Consumption ◽  
Series Data

In this paper, the authors present a data analysis and estimation procedure of electrical power consumption under uncertain conditions. Tiraditional methods are based on statistical and probabilistic approaches but it may not be quite suitable to apply purely stochastic models to the data generated by human activities such as the power consumption. The authors introduce a new approach based on possibility theory and fuzzy autoregression, and apply it to the analysis of time-series data of electric power consumption. Two models, which are different in complexity, are presented, and the performance of the models are evaluated by vagueness and α-cuts. The proposed fuzzy Auoregression model represents the rich information of uncertainty that the original data contain, and it can be a powerful tool for flexible decision-making with uncertainty. The fuzzy AR model can also be constructed in relatively simple procedure compared with the conventional approaches.

scholarly journals PROGRESS FOR STABLE ARTIFICIAL LIGHTS DISTRIBUTION EXTRVCTION ACCURACY AND ESTIMATION OF ELECTRIC] POWER CONSUMPTION BY MEANS OF DMSP/OLS NIGHTTIME IMAGERY

2010 ◽  
Vol 1 (1) ◽  
Author(s):  
MASANAO HARA ◽  
SHUHEI OKADA ◽  
HIROSI YAGI ◽  
TAKASHI MORIYAMA ◽  
KOJI SHIGEHARA ◽  
...  
Keyword(s):  
Time Series ◽  
Power Consumption ◽  
Electric Power ◽  
High Frequency ◽  
Dynamic Range ◽  
Electric Power Consumption ◽  
Analysis Techniques ◽  
Frequency Components ◽  
Narrow Dynamic Range ◽  
Sensor Gain

The Noise Reduction Filter (NRF) that is developed by the authors is applied to extract artificial nightlight components of a time series DMSP/OLS-VIS dataset. High frequency components from the time series DMSP/OLS-VIS dataset are exhausted and a direct current component is extracted by the NRF that is one of the Fourier analysis techniques. The inference of cloud and other disturbance noise are also removed, and a stable artificial nightlight is extracted by the NRF filtration. The intensity value in high power light areas observed by DMSP/OLS-VIS is saturated because of narrow dynamic range of the sensor gain. A simple model called "Deltaic Model" developed by authors corrected those saturated value. Verification of the accuracy of correction methods above described is carried out by comparison with electric power consumption of the calculated values from the model and statistical ones of each prefecture in Japan. Correlation of the values is satisfactory as shown R2 = 0.725. The results of this work shows the remote sensing method by using the DMSP/OLS-VIS nighttime imagery with the correction methods above described is useful to estimate the electric power consumption through a year of fixed areas. Keyword: DMSP/OLS-VIS, NRF filtration, Deltaic Model.

Software including the functions of automated analysis of electric power consumption in pipeline oil transportation

2011 ◽  
Vol 8 (1) ◽  
pp. 233-238
Author(s):  
R.M. Bogdanov ◽  
S.V. Lukin
Keyword(s):  
Power Consumption ◽  
Electric Power ◽  
Data Model ◽  
Automated Analysis ◽  
Petroleum Products ◽  
Electric Power Consumption ◽  
Scientific Center ◽  
Relational Data Model ◽  
Oil Transportation ◽  
Academy Of Sciences

Oil and petroleum products transportation is characterized by a significant cost of electric power. Correct oil and petroleum products accounting and forecasting requires knowledge of many factors. The software for norms of electric power consumption analysis for the planned period was developed at the Ufa Scientific Center of the Russian Academy of Sciences. Based on the principles of the relational data model, a schematic diagram/arrangement for the main oil transportation objects was developed, which allows to hold the initial data and calculated parameters in a structured manner.

Characteristics of losses incurred from restriction of water use and electric power consumption

1985 ◽  
Vol 19 (9) ◽  
pp. 478-483
Author(s):  
S. B. Elakhovskii ◽  
S. I. Sorokina ◽  
E. N. Smirnova
Keyword(s):  
Power Consumption ◽  
Electric Power ◽  
Water Use ◽  
Electric Power Consumption
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