scholarly journals COVID-19 Pandemic Effect on Energy Consumption in State Universities: Michoacan, Mexico Case Study

Energies ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 7642
Author(s):  
Luis Bernardo López-Sosa ◽  
José Juan Alvarado-Flores ◽  
Teresita del Niño Jesús Marín-Aguilar ◽  
Juan Carlos Corral-Huacuz ◽  
Arturo Aguilera-Mandujano ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Electric Energy ◽  
Electricity Consumption ◽  
State Universities ◽  
Multiple Impacts ◽  
Electric Energy Consumption ◽  
Production And Consumption ◽  
New Energy ◽  
Virtual Class

The COVID-19 pandemic has generated multiple impacts. In particular, in the educational sector, the virtual class modality generated changes in the patterns of energy consumption at the institutional level; the identification of this consumption will allow us to reflect on new energy saving and efficient use strategies. In this research, we present a case study of the effects of the COVID-19 pandemic on electricity consumption in 13 state universities in Michoacán, Mexico. Electric energy consumption has been evaluated before and during the presence of the COVID-19 between 2019 and 2020. The comparative analysis estimated the reduction in energy consumption and its economic and environmental impact. The results show a considerable decrease in electricity consumption, generating an average saving of 76.24 MWh/month, which translates into an annual emission reduction from 2019 to 2020 of approximately 497 TnCO2e, and in economic terms of $ 8,882.25 USD each month. In general, it was identified that consumption patterns in the use of machinery and computer equipment for administrative activities were drastically reduced. If education continues in virtual or hybrid modes, energy consumption schemes will continue to decline and institutions could move towards resilient, affordable, and sustainable models of energy production and consumption.

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 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 Prospects of the Russian Coal Industry in the Conditions of Transition to a New Energy Pattern

2020 ◽  
Vol 9 (4) ◽  
pp. 1831-1835
Keyword(s):  
Energy Production ◽  
Electric Energy ◽  
Electricity Consumption ◽  
Coal Industry ◽  
Electric Energy Consumption ◽  
The World ◽  
New Energy ◽  
Forecast Models ◽  
To Come ◽  
Competitive Products

The article analyzes the prospects for changes in the volume of exports of Russian coal due to the reduction of its consumption for energy production by the importing countries. The importance of Russian power generating coal for the energy supply to the world as a whole and to individual importing regions in particular is shown. It is justified that a radical and abrupt refusal to use coal as an energy source in the next few years would hardly be possible because the per capita and the total electric energy consumption has been growing all over the world. To a large extent, this is due to the increase in the number of data centers around the world, which centers do not yet have enough “green” sources of energy to ensure the uninterrupted operation thereof. Therefore, coal exports from Russia to the European countries, China and DPRK will continue to grow in the years to come; this is confirmed by the forecast models built by the authors, which models consider changes in the coal and electricity consumption by major importers. By the time the conditions are ensured for the majority of countries in the world to abandon coal as a source of electricity in order to stop air pollution, Russia will need to find opportunities for its alternative use, an example of which would be the creation and development of production of competitive products of the coal chemical industry.

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.

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