scholarly journals Assessment of the impact of the main technological characteristics of wells on the power consumption of pumps

2021 ◽  
Vol 939 (1) ◽  
pp. 012019
Author(s):  
S Khushiev ◽  
O Ishnazarov ◽  
J Izzatillaev ◽  
S Juraev ◽  
Sh Karakulov
Keyword(s):  
Energy Consumption ◽  
Power Consumption ◽  
Electric Energy ◽  
Electricity Consumption ◽  
Hydrodynamic Resistance ◽  
Electric Energy Consumption ◽  
Technological Characteristics ◽  
Binding Function ◽  
Pump Aggregate ◽  
The Impact

Abstract The issue of assessing the impact of the main technological characteristics of wells on the power consumption of pumps is one of the important issues. Based on the analysis of the data obtained in the article, the electric energy consumption of the well pump device the rotational speed of the pump (co); the density of the solution (liquid) (p); the pressure generated by the pump (H); the performance of the pump aggregate (q); depth of the well (H); hydrodynamic resistance (dp); Also, on the basis of the STATISTICA program, the calculation work is carried out, the binding function of the pumps is determined to what extent the factor affects the electricity consumption, and is described in the Pareto diagram.

MATHEMATICAL MODELS FOR MONITORING AND CONTROL OF ELECTRIC ENERGY CONSUMPTION WITH ACCOUNT OF THE SEASONAL FACTOR

2017 ◽  
Vol 11 (5) ◽  
pp. 133-140 ◽  
Author(s):  
Сергей Лавренченко ◽  
Sergey Lavrenchenko ◽  
Людмила Згонник ◽  
Lyudmila Zgonnik ◽  
Инна Гладская ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Power Consumption ◽  
Electric Energy ◽  
Electricity Consumption ◽  
Daily Basis ◽  
Monitoring And Control ◽  
Theoretical Level ◽  
Electric Energy Consumption ◽  
Energy Meter ◽  
Seasonal Factor

The article proposes a method for predicting the daily energy consumption level for every day of a whole year, taking into account the season-al factor, based on only twelve actual power consumption data by the months of the year. Then a mathematical model is developed for moni-toring and controlling the level of electricity consumption on a daily basis, taking into account the seasonal factor. The model is consistent with a common model for the length of daylight (in hours). In addition, on the basis of this model, a method of monitoring and diagnostics of electricity consumption is presented, which will allow users to monitor the level of power consumption and be timely notified of any deviations from the theoretical level. Finally, this method gives rise to the operational principle for a proposed device, a smart energy meter, for detecting suspicious deviations from the theoretical level. The device will help timely detect over-consumption (or under-consumption) of electricity in order to take preventive measures. The proposed method consists of the following steps: (1) choice of a function to model the level of electricity consumption (theoretical calculated level), (2) choice of a tubular control neighborhood of the graph of the model function, (3) choice of a criterion on when the smart energy meter should notify the user of an unexpected deviation from the theoretical level in the case of exit from the tubular control neighborhood.

scholarly journals Analysis of Trams’ Consumption Depending on the Type of Traffic Light Used

2019 ◽  
Vol 18 (6) ◽  
pp. 490-494
Author(s):  
A. Czerepicki ◽  
A. Górka ◽  
J. Szustek
Keyword(s):  
Energy Consumption ◽  
Electric Energy ◽  
Electricity Consumption ◽  
Fixed Time ◽  
Urban Transport ◽  
Traffic Light ◽  
Traffic Lights ◽  
Electric Energy Consumption ◽  
Traffic Light Control ◽  
The Impact

In the XXI century, when environmental awareness is growing and the impact of human activity on the planet is more and more noticeable, striving to minimize energy consumption seems to be a necessary direction in the development of technology. This development cannot take place without an initial understanding and describing the relationships influencing specific technologies. It also needs empirical verification of assumed theories. Modern trams play an important role in the functioning of urban transport. Being one of the oldest modes of environmentally friendly transport, in European capitals they are currently perceived as one of the most convenient means of transport. This is due, among other things, to the high velocity of transport along the route. The energy consumed by trams indirectly depends on the driving characteristics, i. e. speed, acceleration and stops on the route, which are also caused by stopping at traffic light controlled junctions. This paper presents the results of an experiment showing the change in the level of electric energy consumption depending on the applied method of traffic light control. This article presents the conditions influencing the power consumption in trams, describes the possible strategies of traffic lights control and their consequences for other traffic participants. The research was carried out in real conditions in everyday traffic, measuring the level of electricity consumption in case of both fixed-time and actuated signaling with full priority for trams. On the examined section there were both modern asynchronous-drive as well as traditional resistor-drive vehicles. The conclusions drawn from the survey confirm the validity of introducing modern solutions and may be useful for estimating investment costs.

scholarly journals Parallel Evolutionary Biclustering of Short-term Electric Energy Consumption

2021 ◽  
Author(s):  
Diego P. Pinto-Roa ◽  
Hernán Medina ◽  
Federico Román ◽  
Miguel García-Torres ◽  
Federico Divina ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Electric Energy ◽  
Electricity Consumption ◽  
Search Space ◽  
Consumption Pattern ◽  
Electric Energy Consumption ◽  
System A ◽  
Time Period ◽  
Computation Scheme ◽  
Significant Patterns

The discovery and description of patterns in electric energy consumption time series is fundamental for timely management of the system. A bicluster describes a subset of observation points in a time period in which a consumption pattern occurs as abrupt changes or instabilities homogeneously. Nevertheless, the pattern detection complexity increases with the number of observation points and samples of the study period. In this context, current bi-clustering techniques may not detect significant patterns given the increased search space. This study develops a parallel evolutionary computation scheme to find biclusters in electric energy. Numerical simulations show the benefits of the proposed approach, discovering significantly more electricity consumption patterns compared to a state-of-the-art non-parallel competitive algorithm.

scholarly journals Electric energy consumption of milking process on some Finnish dairy farms

2017 ◽  
Vol 26 (3) ◽  
Author(s):  
Mari Rajaniemi ◽  
Tapani Jokiniemi ◽  
Laura Alakukku ◽  
Jukka Ahokas
Keyword(s):  
Energy Consumption ◽  
Energy Savings ◽  
Electric Energy ◽  
Cooling Water ◽  
Electricity Consumption ◽  
Dairy Farms ◽  
Energy Costs ◽  
Heating Systems ◽  
Electric Energy Consumption ◽  
Energy Saving Potential

The aim of this study was to examine the electric energy consumption of milking process on dairy farms and to evaluate the methods to improve the energy efficiency. The electricity consumption of the milking process was measured on three dairy farms in Southern Finland, and it varied between 37–62 Wh kg-1 milk.  The largest energy saving potential was identified in milk cooling and the heating of cleaning water. Even simple methods, such as placing the condenser of the refrigeration system outside, may reduce the energy consumption of milk cooling by 30%. Efficient milk pre-cooling can reduce the energy consumption of the whole milking process by more than 25%. Even larger energy savings are possible with a sophisticated milk cooling – water heating systems. It was concluded that there is a significant potential to reduce the energy consumption and energy costs of the milking process, and thus to improve the profitability and sustainability of the sector at the same time.

scholarly journals Bagging Ensemble of Multilayer Perceptrons for Missing Electricity Consumption Data Imputation

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1772 ◽  
Author(s):  
Seungwon Jung ◽  
Jihoon Moon ◽  
Sungwoo Park ◽  
Seungmin Rho ◽  
Sung Wook Baik ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Energy Management ◽  
Missing Values ◽  
Electric Energy ◽  
Electricity Consumption ◽  
Multilayer Perceptrons ◽  
Imputation Methods ◽  
Electric Energy Consumption ◽  
Consumption Data ◽  
Bagging Ensemble

For efficient and effective energy management, accurate energy consumption forecasting is required in energy management systems (EMSs). Recently, several artificial intelligence-based techniques have been proposed for accurate electric load forecasting; moreover, perfect energy consumption data are critical for the prediction. However, owing to diverse reasons, such as device malfunctions and signal transmission errors, missing data are frequently observed in the actual data. Previously, many imputation methods have been proposed to compensate for missing values; however, these methods have achieved limited success in imputing electric energy consumption data because the period of data missing is long and the dependency on historical data is high. In this study, we propose a novel missing-value imputation scheme for electricity consumption data. The proposed scheme uses a bagging ensemble of multilayer perceptrons (MLPs), called softmax ensemble network, wherein the ensemble weight of each MLP is determined by a softmax function. This ensemble network learns electric energy consumption data with explanatory variables and imputes missing values in this data. To evaluate the performance of our scheme, we performed diverse experiments on real electric energy consumption data and confirmed that the proposed scheme can deliver superior performance compared to other imputation methods.

scholarly journals The Impact of the COVID-19 Pandemic on Electricity Consumption and Economic Growth in Romania

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2394
Author(s):  
Georgeta Soava ◽  
Anca Mehedintu ◽  
Mihaela Sterpu ◽  
Eugenia Grecu
Keyword(s):  
Economic Growth ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Negative Impact ◽  
Electric Energy ◽  
Electricity Consumption ◽  
Electric Energy Consumption ◽  
The One ◽  
The Impact

This paper analyzes the impact of the COVID-19 pandemic on economic growth and electricity consumption and investigates the hypothesis of the influence of this consumption on the gross domestic product (GDP) for Romania. Using time series on monthly electricity consumption and quarterly GDP and a multi-linear regression model, we performed an analysis of the evolution of these indicators for 2007–2020, a comparison between their behavior during the financial crisis vs. COVID-19 crisis, and empirically explore the relationships between GDP and electricity consumption or some of its components. The results of the analysis confirm that the shock of declining activity due to the COVID-19 pandemic had a severe negative impact on electric energy consumption and GDP in the first half of 2020, followed by a slight recovery. By using a linear regression model, long-term relationships between GDP and domestic and non-household electricity consumptions were found. The empirically estimated elasticity coefficients confirm the more important impact of non-household electricity consumption on GDP compared to the one of domestic electricity consumption. In the context of the COVID-19 pandemic, the results of the study could be useful for optimizing energy and economic growth policies at the national and European levels.

scholarly journals Analytical Treatment of Forecasts of Electric Energy Consumption in Latvia

2014 ◽  
Vol 51 (3) ◽  
pp. 3-14
Author(s):  
M. Balodis ◽  
V. Gavars ◽  
J. Andersons
Keyword(s):  
Energy Consumption ◽  
Structural Changes ◽  
Electric Energy ◽  
Electricity Consumption ◽  
Time Span ◽  
Analytical Treatment ◽  
East European ◽  
Electric Energy Consumption ◽  
West Central ◽  
Specific Electricity Consumption

Abstract In the paper, the changes in electric energy consumption are analyzed as associated with structural changes in the Latvian economy of postsocialistic period. To the analysis, a particular approach is applied, which consists in comparison of the basic and specific electricity consumption indices in West-, Central-, and East-European states for the time span of 1990-2010, with differences and tendencies of changes revealed. Tendencies of the type are determined for the electric energy consumption in Latvia, and recommendations are given for the use of such indices in the relevant forecasts.

Risk Management of Electric Energy Consumption in Industrial

2011 ◽  
Vol 301-303 ◽  
pp. 691-694
Author(s):  
Marut Khodpan ◽  
Sanguan Vongchavalitkul ◽  
Sanguan Patamatumakul
Keyword(s):  
Risk Management ◽  
Energy Consumption ◽  
Electric Energy ◽  
Production Unit ◽  
Risk Level ◽  
Load Factor ◽  
Lighting System ◽  
Water Pump ◽  
Electric Energy Consumption ◽  
The Impact

This study presents a risk management of the electric energy consumption in Nakhonratchasima industrial, case study in Thai Sanguan Cassava Starch Co.,Ltd. by AS/NZS 4360:2004 risk management standard. The objective of this study is to reduce the risk of electric energy consumption. Based on the risk inspection, the impact analysis of electric energy consumption is applied to manage an occasion risk. The analyzed risks are consumption risks and device risks. The consumption risks are load factor, the ratio of electric energy consumption to production unit and power consumption of each unbalance phase. The device risks are an efficiency of machinery and equipment, water pump usages, lighting system and air condition system. The results show that the use of machinery and equipment is the highest risk. Load factor, the ratio of electric energy consumption to production unit and water pump usages are ranged in medium risk level, while each phase of electric energy consumption, lighting system and air condition system are in low risk level. As a priority result, machine maintenance is an initial management to decrease the electric energy consumption. For reducing the electric energy demand, work schedule has been rearranged to enlarge the load factor. Operator controlling will reduce the ratio of electric energy consumption to production unit. The inverter, which is used to control motor in water pump usages, also reduce the risk of electric energy consumption. Finally, the low impact factor from lighting and air condition system are neglected.

scholarly journals Practical assessment of grinding capacity and power consumption based on Hardgrove grindability index and coal characteristics

2019 ◽  
Vol 23 (Suppl. 5) ◽  
pp. 1533-1542
Author(s):  
Marko Obradovic ◽  
Dejan Radic ◽  
Dusan Todorovic ◽  
Aleksandar Jovovic ◽  
Nikola Karlicic ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Power Consumption ◽  
Negative Correlation ◽  
Thermal Power ◽  
Electric Energy ◽  
Specific Power ◽  
Specific Power Consumption ◽  
Electric Energy Consumption ◽  
Before And After ◽  
Hardgrove Grindability Index

This paper analyzes the effects of coal grindability and its characteristics on the grinding capacity and power consumption for beater wheel mill during exploitation in thermal power plant TENT B in Obrenovac, Serbia. For this purpose, experiments were made on the mill, before and after its reconstruction. Experiments included the determination of grinding capacity, mill power consumption, and laboratory analysis of coal characteristics and Hardgrove grindability index (HGI). The analysis of experimental results found that the grinding capacity has a negative correlation with the ash content in coal. Moisture content in analysis sample of coal has a positive correlation with the consumption of electricity and grinding capacity. Between the grinding capacity and the value of HGI exists a negative correlation. Analysis of the influence of grindability of coal and coal characteristics on grinding capacity and energy consumption was carried out. Based on coal characteristics and values of HGI, mathematical expressions were derived for the calculation of grinding capacity and electric energy consumption. In addition, ability to predict specific power consumption of the mill on the basis of HGI values, were carried out. Specific power consumption obtained from HGI values showed good agreement with the experimentally determined specific power consumption of the mill.

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