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 Traffic Light Priority for Trams in Warsaw as a Tool for Transport Policy and Reduction of Energy Consumption

2021 ◽  
Vol 13 (8) ◽  
pp. 4180
Author(s):  
Andrzej Czerepicki ◽  
Tomasz Krukowicz ◽  
Anna Górka ◽  
Jarosław Szustek
Keyword(s):  
Control System ◽  
Energy Consumption ◽  
Electric Energy ◽  
Fixed Time ◽  
Pollutant Emissions ◽  
Computer Simulation Model ◽  
Traffic Light ◽  
Electric Energy Consumption ◽  
Time Control ◽  
Reduction Of Energy Consumption

The article presents an analysis of priority solutions for trams at a selected sequence of intersections in Warsaw (Poland). An analysis of the literature has shown the topicality of this issue. A computer simulation model of a coordinated sequence of intersections was constructed. Three test scenarios were designed: the existing control system, the new coordinated fixed-time control system, and the adaptive control system with active priority. In the simulation process, detailed travel characteristics of trams and other traffic participants in a selected section were obtained for the three varying scenarios. Electric energy consumption for traction needs and pollutant emissions was then estimated for each of the variants. It was concluded that for the analyzed configuration, implementation of the adaptive priority will result in a reduction of tram time losses by up to 25%, a reduction in energy consumption by up to 23%, and a reduction in the emission of pollutants from individual vehicles by up to 3% in relation to the original variant. The conducted research may be the basis for a comprehensive method of assessing the effectiveness of applying the adaptative priority when designing new tramway lines and modernizing the existing ones.

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.

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.

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 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.

scholarly journals An Efficient Adaptive Traffic Light Control System for Urban Road Traffic Congestion Reduction in Smart Cities

Information ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 119
Author(s):  
Dex R. ALEKO ◽  
Soufiene Djahel
Keyword(s):  
Control System ◽  
Travel Time ◽  
Traffic Congestion ◽  
Fixed Time ◽  
City Centre ◽  
Light Control ◽  
Traffic Light ◽  
Long Distance ◽  
Traffic Lights ◽  
Traffic Light Control

Traffic lights have been used for decades to control and manage traffic flows crossing road intersections to increase traffic efficiency and road safety. However, relying on fixed time cycles may not be ideal in dealing with the increasing congestion level in cities. Therefore, we propose a new Adaptive Traffic Light Control System (ATLCS) to assist traffic management authorities in efficiently dealing with traffic congestion in cities. The main idea of our ATLCS consists in synchronizing a number of traffic lights controlling consecutive junctions by creating a delay between the times at which each of them switches to green in a given direction. Such a delay is dynamically updated based on the number of vehicles waiting at each junction, thereby allowing vehicles leaving the city centre to travel a long distance without stopping (i.e., minimizing the number of occurrences of the ‘stop and go’ phenomenon), which in turn reduces their travel time as well. The performance evaluation of our ATLCS has shown that the average travel time of vehicles traveling in the synchronized direction has been significantly reduced (by up to 39%) compared to non-synchronized fixed time Traffic Light Control Systems. Moreover, the overall achieved improvement across the simulated road network was 17%.

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