Constructing Demand Response Models for Electric Power Consumption

2010 ◽  
Author(s):  
John D. Hobby
Keyword(s):  
Power Consumption ◽  
Electric Power ◽  
Demand Response ◽  
Electric Power Consumption ◽  
Response Models

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

Optimization of a Raw Water Source Heat Pump for a Vertical Water Treatment Building

2015 ◽  
Vol 23 (01) ◽  
pp. 1550002
Author(s):  
Sunhee Oh ◽  
Yong Cho ◽  
Rin Yun
Keyword(s):  
Power Consumption ◽  
Electric Power ◽  
Flow Rate ◽  
Storage Tank ◽  
Water Flow Rate ◽  
Water Source ◽  
Thermal Storage ◽  
Electric Power Consumption ◽  
Air Flow Rate ◽  
Circulating Water

The optimum operation conditions of a raw water source heat pump for a vertical water treatment building were derived by changing operation parameters, such as temperature of thermal storage tank, temperature and inlet air flow rate of the conditioned spaces, and circulating water flow rate between thermal storage tank and air handling unit (AHU) through dynamic simulator of a transient system simulation program (TRNSYS). Minimum electric power consumption was found at temperature of thermal storage tank, which was ranged 18–23°C for cooling season. In heating season, temperature 40–45°C brings the highest coefficient of performance (COP) and temperature range of 30–35°C brings the lowest power consumption. When the temperature of the conditioned spaces was controlled between 27–28°C for cooling season, and 18–20°C for heating season the minimum electric power consumption was obtained. Inlet air flow rate of 1.1 m3/h for the conditioned spaces shows the highest performance of the present system, and effects of circulating water flow rate between thermal storage tank and AHU on minimum electric power consumption of the system were negligible.

scholarly journals Complex statistical data analysis of electric power consumption, consumption of energy resources and production volumes

2020 ◽  
pp. 22-28
Author(s):  
А. Voloshko ◽  
Ya. Bederak ◽  
T. Dzheria
Keyword(s):  
Statistical Analysis ◽  
Power Consumption ◽  
Electric Power ◽  
Distribution Systems ◽  
Energy Resources ◽  
Compressed Air ◽  
Electric Power Consumption ◽  
Supply And Distribution ◽  
Consumption Data ◽  
Complex Statistical Analysis

Aims of this research are development of a complex statistical analysis algorithm for active electric power consumption data, consumption of energy resources and manufacturing products, implementation of statistical analysis in practice. Proposed parameters and criteria, which can help to technical staff in factories, to provide optimal and economical operating of supply and distribution systems as electricity, water, gas, heat, compressed air, etc. for production facilities, based on the collected active electric power consumption data for previous periods, information about consumption dynamic. It is concluded that the statistical analysis of the data, obtained for each type of engineering equipments (water supply and sewage, supply systems of compressed air, gas, electricity and steam) and various consumables coefficients (in the proposed algorithm) make possible to identify "weak areas" and to determine the most rational ways to optimize energy usage.

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.

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