Total Electric Energy Consumption Research Articles

SIMULATING OPERATIONAL IMPROVEMENTS ON MINE COMPRESSED AIR SYSTEMS

Compressed air systems are one of the most significant energy users on mines, with a contribution of about 20 per cent to the total electrical energy consumption on a mine. Research has shown that these compressed air networks are systemically inefficient. Improving the efficiency of these systems would result in a significant reduction in energy costs. A simulation methodology was developed and investigations were conducted into compressed air systems. A compressed air system was subsequently modelled, and a proposed means of improvement was simulated, analysed, and quantified. A South African gold mine was used as a case study. The results showed that, by reducing air usage at refuge bays, an average power reduction of 0.9 megawatts could be achieved. The improvement in efficiency could potentially lead to R6.3 million in annual energy cost savings. The study shows how simulations can help to improve efficiency and profitability in the mining industry.

Prediction of domestic appliances usage based on electrical consumption

Forecasting or modeling the on-off times of domestic appliances has gained increasing attention in recent years. However, comparing currently published results is difficult due to the many different data-sets and performance measures employed. In this paper, we evaluate the performance of three increasingly sophisticated approaches within a common framework on three data-sets each spanning 2 years. The approaches forecast the future on-off times of the appliances for the next 24 h on an hourly basis, solely based on historic energy consumption data. The appliances investigated are driven by user behavior and consume a significant fraction of the household’s total electrical energy consumption. We find that for all algorithms the average area under curve (AUC) in the receiver operating characteristic (ROC) is in the range between 72% and 73%, i.e. indicating mediocre prediction quality. We conclude that historic consumption data alone is not sufficient for a good quality hourly forecast.

On the Climate Variability and Energy Demands for Indoor Human Comfort Levels in a Tropical-Coastal Urban Environment

The main objective of this study is to identify how climate variability and urbanization influence human comfort levels in tropical-coastal urban environments. San Juan Metro Metropolitan Area (SJMA) of the island of Puerto Rico was chosen as a reference point. A new human discomfort index (HDI) based on environmental enthalpy is defined to determine the energy required to maintain indoor human comfort levels. Regression analysis shows that both temperature and HDI are good indictors to predict total electrical energy consumption. Results showed that over the past 35 years, the average environmental enthalpy have increased, resulting in the increase of average HDI with clear bias due to urbanization. Local scale weather station data show a decreasing rate of maximum cooling per capita at –11.41 kW h/years and increasing of minimum cooling per capita of 10.64 kW h/years; however, for the whole Caribbean region, an increasing trend is observed for both minimum and maximum cooling per capita. To estimate human comfort levels under extreme heat wave events conditions, an event of 2014 was identified. The analysis is complemented by simulations from the weather forecasting system (WRF) at a resolution of 1 km, forced by data from the National Center for Environmental Prediction at 250 km spatial resolution. WRF model results were evaluated against observations showing good agreement for both temperature and relative humidity (RH) and improvements. It also shows that energy per capita in urban areas during a heat wave event can increase to 21% as compared to normal day.

Energy Saving Potential of a Combined Solar and Natural Gas-Assisted Vapor Absorption Building Cooling System

A building energy simulation study is carried out to analyze the performance of a triple-hybrid single-effect vapor absorption cooling system (VACS) operated by solar, natural gas, and auxiliary electricity-based cogeneration. A high capacity small office building subjected to different climatic conditions is considered. The system is designed to continuously maintain a specified building comfort level throughout the year under diverse environmental conditions. Simulations are done at different generator temperatures to investigate the performance in terms of total annual electric energy consumption, heating energy, and the coefficient of performance (COP). The performance of the present VACS is compared with the conventional compression-based system, which demonstrates the electric energy and cost saving potentials of the proposed VACS. Simulation outcomes are well-validated against benchmark data from national renewable energy laboratory and energy conservation building code. Interestingly, it is found that beyond a certain collector area, surplus energy savings can be acquired with the present triple-hybrid VACS as compared to the compression-based cooling. Results also show that COP of the simulated system is in line with experimental values available in the literature. Finally, recommendations are given to operate the complete system on solar and biomass resources, which provide encouraging opportunity for agriculture-based countries.

Simple estimation and energy‐aware migration models of virtual machines in a server cluster

SummaryIn order to realize green society, it is critical to reduce electric energy consumed by servers in clusters. In our previous studies, these types of algorithms are proposed to select an energy‐efficient server to perform an application process issued by a client. In this paper, we newly discuss a migration approach to reducing the electric energy consumption of servers where virtual machines with application processes migrate to more energy‐efficient servers. We propose a new algorithm called ISEAM2 to reduce the electric energy consumption of servers. Here, a pair of a host server and a virtual machine on the host server are first selected to perform a process issued by an application. In addition, a target virtual machine on a host server and a guest server to which the target virtual machine migrates are selected so that the electric energy consumption of the host and guest servers can be minimized. Thus, virtual machines with application processes migrate to more energy‐efficient servers. We also propose a simple way to estimate the termination time of every process on each server. In the evaluation, we show the total electric energy consumption and total active time of servers, and the average execution time of processes can be reduced in the ISEAM2 algorithm compared with other algorithms.

An energy-efficient model for fog computing in the Internet of Things (IoT)

A huge number of devices like sensors in addition to computers are interconnected in the IoT (Internet of Things). In the cloud computing model, sensor data is transmitted to servers in networks and processed on the servers in a cloud. Here, networks are congested and servers are overloaded due to heavy traffic from sensors. In order to reduce the delay time and network traffic and increase the performance of the system, data and processes are distributed to not only servers in a cloud but also fog nodes in fog computing models. While the traffic of servers in a cloud can be reduced, the total electric energy consumed by fog nodes increases to process sensor data. In this paper, we newly propose a tree-based fog computing (TBFC) model to distribute processes and data to servers and fog nodes so that the total electric energy consumption of nodes can be reduced in the IoT. In the evaluation, we show the total electric energy consumption of nodes in the TBFC model is smaller than the cloud computing model.

Energy consumption laxity-based quorum selection for distributed object-based systems

In object based systems, an object is an unit of computation resource. Distributed applications are composed of multiple objects. Objects in an application are replicated to multiple servers in order to increase reliability, availability, and performance. On the other hand, the large amount of electric energy is consumed in a system compared with non-replication systems since multiple replicas of each object are manipulated on multiple servers. In this paper, the energy consumption laxity-based quorum selection (ECLBQS) algorithm is proposed to construct a quorum for each method issued by a transaction so that the total electric energy consumption of servers to perform methods can be reduced in the quorum based locking protocol. The total electric energy consumption of servers, the average execution time of each transaction, and the number of aborted transactions are shown to be more reduced in the ECLBQS algorithm than the random algorithm in evaluation.

Conceptual Framework of Modelling for Malaysian Household Electrical Energy Consumption using Artificial Neural Network based on Techno-Socio Economic Approach

The residential sector was one of the contributors to the increase in the world energy consumption and CO2 emission due to the increase population, economic development, and improved living standard. Developing a reliable model of electrical energy consumption based on techno-socio economic factors was challenging since many assumptions need to be considered. Over the past decade, bottom-up approaches such as multi-linear regression, artificial neural network (ANN), and conditional demand analysis were used for developing mathematical models to investigate interrelated characteristics among techno-socio economic factors. However, the existing models mostly were focused on countries that had different socio-economic level and cultures from the developing countries of the Association of Southeast Asian Nations. Similar studies in that tropical region were very scarce and only limited for linear modelling under the conditions of techno-socio economic factors. In this study, we proposed ANN for developing a model of electrical energy consumption based on techno-socio economic factors for a tropical region, Malaysia. In order to develop the model, quantitative measurement and qualitative assessment were required. The quantitative measurement was based on the monitoring of total electrical energy consumption with a one-minute interval. In contrast, the qualitative assessment utilized a questionnaire survey to assess household characteristics based on techno-socio economic parameters. The objective of this paper was to propose a conceptual framework of the estimation model for household electrical energy consumption with the consideration of techno-socio economic factors using ANN.

Dynamic migration of virtual machines to reduce energy consumption in a cluster

Virtual machines are widely used to support applications with virtual service in server clusters. Here, a virtual machine can migrate from a host server to a guest server. In this paper, we consider a cluster where virtual machines are dynamically created and dropped depending on the number of processes. We propose a dynamic virtual machine migration (DVMM) algorithm to reduce the total electric energy consumption of servers. If an application issues a process to a cluster, the most energy-efficient host server is first selected and then the process is performed on a virtual machine of the server. Then, a virtual machine migrates from a host server to a guest server so that total electric energy consumption of servers can be reduced. In the evaluation, we show the total electric energy consumption and active time of servers and the average execution time of processes can be reduced in the DVMM algorithm.

A dynamic model for energy-efficiently migrating a group of virtual machines from a host server to a guest server

In this paper, we newly propose a dynamic migration of a group of υ virtual machines [DMG(υ)] algorithm to reduce the total electric energy consumption of servers in a cluster. Here, virtual machines are dynamically resumed and suspended so that the number of processes on each virtual machine can be kept fewer. In addition, a group of υ (υ ≥ 1) virtual machines migrate from a host server to a guest server so that the total electric energy consumed by the servers can be reduced. In our previous studies, time to migrate each virtual machine is assumed to be zero. In reality, there is migration time every process is suspended on a virtual machine migrating even in the live manner. Hence, the more often a virtual machine migrates, the longer time it takes to perform processes on the virtual machine. We propose a model to estimate electric energy to be consumed by servers through considering the migration time of each virtual machine. By using the estimation model, we can decide on which virtual machines on a host server to which guest server so that the total electric energy consumption of the host and guest servers can be reduced. In the evaluation, we show the total electric energy consumption of servers can be reduced by the DMG(υ) algorithm compared with other algorithms.

Electricity Generation Potential Through Solar-Rice Husk Hybrid Power Plant in Nepal and Its Major Paddy Producing Districts

Nepal is an agricultural based country having huge potential of rice husk. Out of total electrical energy consumption in Nepal, 22%, 34.76% and 36.58% were imported from India in the years 2016, 2017 and 2018 respectively. Such import can be minimized by generating electricity from solar-rice husk hybrid system. Solar plants have drawbacks of intermittency in day/night cycle and reduced irradiation in winter and cloudy days. Biomass plants have problem associated with continuous supply of seasonal fuel. Those problems can be solved using hybrid system by maximizing the energy potential of these resources, increasing process efficiency, providing greater security of supply and reducing overall cost. The objective of this paper is to estimate electricity generation potential through solar- rice husk hybrid power plant in Nepal and its major paddy producing districts through existing data and forecasting the same for future potential. From existing data, the rice husk growth rate in Nepal was found to be 2.92% and on the basis of that growth rate, future potentials were forecasted. The 20 major paddy producing districts were taken into consideration as 71.15% of total paddy production of Nepal was found to be shared by those districts. The quantity of rice husk was found assuming 20% of paddy converted into rice husk in the mills. The rice husk consumption for cattle feeding was assumed 10% of total rice husk. The electric power from rice husk was calculated assuming 1.3 kg consumption of rice husk for per kWh generation of electricity through steam route and 1.86 kg of rice husk consumption for per kWh electricity generation through gasification route. The average increase rate of land for paddy production and the average increase rate of rice husk production were found to be 0.39% and 2.92% respectively. The electrical energy potentials of Nepal for the years 2015 and 2030 with solar-rice husk hybrid power plant through steam route were found as 100.67 MW and 155.02 MW respectively and through gasification route 70.36 MW and 108.35 MW respectively. The electrical potential for Jhapa district was found the highest values of 7.10 MW and 10.94 MW through steam route and 4.96 MW and 7.64 MW through gasification route for the years 2015 and 2030 respectively. Thus, through solar-rice husk hybrid power plant system, large amount of electricity can be generated locally in Nepal, which in turn can reduce energy import and enhance the better life of the people.

Simple algorithms for selecting an energy-efficient server in a cluster of servers

Electric energy consumed by computers, especially servers in scalable clusters has to be reduced to realise eco society. In this paper, we first propose a multi-level power consumption (MLPCM) model which gives how much electric power a server consumes to perform application processes. We also propose an multi-level computation (MLCM) model which shows the expected execution time of an application process performed on a server. By using the MLPCM and MLCM models, we propose a pair of simple locally energy-aware (SLEA) and simple globally energy-aware (SGEA) algorithms to select a host server to perform an application process issued by a client. In the SLEA algorithm, a host server is selected, which is expected to consume the minimum electric energy to perform the process. In the SGEA algorithm, a host server is selected, where the total expected electric energy consumption of not only the host server but also the other servers is minimum. In the evaluation, we show the total electric energy consumption and active time of servers and the average execution time of processes can be reduced in the SGEA algorithm compared with other algorithms.

Monitoring Penggunaan Daya listrik Sebagai Implementasi Internet of Things Berbasis Wireless Sensor Network

One implementation of the Internet of Things (IoT) conducted in this study to realize the system of monitoring and control of electrical energy usage-based Wireless Sensor Network (WSN). This research method is the design of wireless sensor nodes that can measure the electrical parameters of alternating current (AC) as effective voltage, effective current, active power, apparent power, power factor and total electrical energy consumption by using modules ESP8266 as a liaison with a Wi-Fi. Calculation of electrical parameters obtained from ATmega328P microcontroller ADC readings of a step-down transformer that is used as a voltage sensor and sensor SCT013 used as AC current sensors will be transmitted to the server over the network from a Wi-Fi Access Point (AP). ESP8266 modules are programmed using AT-Command proven to reliably measure can transmit data simultaneously with serial data format of the wireless sensor node to a server using TCP / IP protocol. Monitoring power consumption via the internet which are designed in the research, either through the Android application and web browser proven to be reliably able to show some electrical parameters with the same data than the data logger recaps taken from SD-Card installed in the wireless sensor node.

Tightening the loop on the circular economy: Coupled distributed recycling and manufacturing with recyclebot and RepRap 3-D printing

A promising method of enhancing the circular economy is distributed plastic recycling. In this study plastic waste is upcycled into 3-D printing filament with a recyclebot, which is an open source waste plastic extruder. The recyclebot is combined with an open source self-replicating rapid prototyper (RepRap) 3-D printer, to enable post-consumer ABS plastic filament from computer waste to be further upcycled into valuable consumer products pre-designed in the digital commons. The total electrical energy consumption for the combined process is monitored and an economic evaluation is completed. The coupled distributed recycling and manufacturing method for complex products reduces embodied energy by half, while reducing the cost of consumer products to pennies. This economic benefit provides an incentive for consumers to both home recycle and home manufacture, which tightens the loop on the circular economy by eliminating waste associated from transportation and retail. It is clear from the results that waste plastic can be significantly upcycled at the individual level using this commons-based approach. This tightening of the loop of the circular economy benefits the environment and sustainability as well as the economic stability of consumers/prosumers.

The impact of air conditioning system upgrade on energy use and comfort in low income housing

Abstract: The ‘Beat The Heat!’ project was a project of the Australian Federal Governments ‘Low Income Energy Efficiency Program’ (LIEEP). This project aimed to enhance the resilience and improve the comfort and wellbeing of Adelaide households during South Australias long, hot summers. The installation of ceiling insulation and new air-conditioners in the main living areas of low-income households were two of the key interventions made through this project. Total electrical energy consumption of each household and the air-conditioner were monitored following these interventions and compared with electricity bills before the installation, during matched summer periods. Indoor temperatures of the main living area were also monitored, following the intervention, to assess the improvement in thermal comfort. Significant differences between the energy use before and after household interventions, along with associated household perceptions, are discussed in this paper. Specifically, the impact on energy consumption of factors such as household income, the source of cooling energy prior to the intervention and differences in climate and thermal comfort are explored. For houses where an old, inefficient and ineffective air-conditioner was replaced by a new, efficient split system (3.23

Quantifying electrical energy savings in offices through installing daylight responsive control systems in hot climates

Lighting is considered as one of the most important issues in reducing energy consumption of a building. It is estimated that electrical lighting consumes 25–40% of the total electrical energy in a typical commercial building in the United States. Over the last three decades, there has been a growing concern about reducing energy consumption associated with artificial lightings. Daylighting could be considered as a cost-effective alternative to artificial lighting which not only reduces the demands for electrical energy, but also provides occupants with a pleasant, attractive, and a healthy indoor environment. Through installing sensors and controllers, daylighting is able to reduce and even eliminates the use of artificial lighting needed to deliver sufficient illuminance levels in an office. Present study is a simulation based research that investigates the impacts of various types of daylighting controllers on enhancing total and lighting electrical energy consumption of office buildings located in hot climates. Effects of Dimming (5%, 10%, and 20% light), On/Off, and Stepped control systems are evaluated in this study. E-Quest is used as the energy simulation tool to calculate and compare electrical and lighting energy consumption. In order to assess the effects of daylight control systems in humid and arid hot climates, Miami, Phoenix, and Houston, located in ASHRAE 90.1 climate zones of 1A, 2B, and 2A respectively, have been chosen as three locations for the prototype building. The prototype building is a four-story open office building measuring 18m wide×36m long×15m high oriented along east-west axis. The window to wall ratio of 20, 40, 60 and 90% in all directions are assessed. Windows consist of horizontal shading in all façades as well as blinds in those of East and West. Results of this study demonstrate that in all studied cities installing daylighting controllers in office buildings significantly reduces electrical energy consumption of the building particularly that of lighting.

The use of statistical methods to analyze electric-energy consumption by budget institutions

The possibilities of applying statistical methods to analyze the consumption of electric energy by budget organizations and control energy efficiency are studied. Statistical methods are used to consider the influence of the season and climatic conditions on consumption of electric energy, which allowed a simulation model to be created of electric-energy consumption for illumination and other needs according to data of devices considering only the total consumption of electric energy by all groups of consumers. The change in monthly consumption of electric energy within a year is a stochastic value depending on the duration of light in a day and other factors. The statistical characteristics of the monthly consumption of electric energy by individual buildings of Chuvash State University are determined. Dispersion analysis shows that distribution of the values of the monthly electric-energy consumptions by most buildings corresponds to the normal law. The correlation between the monthly electric-energy consumption and duration of light in a day permitted one to carry out a regression analysis and determine the actual average power of the lighting system and actual average power of the rest of the energy consumption of each facility. The results of the statistical analysis make it possible to compile the electric-energy consumption balance, predict electric-energy consumption by the different group of consumers, estimate the efficiency of energy-saving measures, and obtain the data necessary to fill in energy declarations and energy-performance certificates.

RESIDUAL BIOMASSES IN THE MICRO-REGION OF DOURADOS (MS): ASSESSMENT AND AVAILABILITY FOR ENERGY IN AGRICULTURE THERMAL CONVERSION

ABSTRACT This research presents results of the assessment of solid biofuels (residual biomasses from agricultural crops) in the state of Mato Grosso do Sul, in the period 2007-2012, for energy in agricultural uses. It is pointed out the available quantity, its geographic location at micro and meso regions and energy conversion potential. The methodology is based on a survey on municipal agricultural production of the Brazilian Institute for Geography and Statistics (quantity and local availability, per year) followed by determination of the amounts of agricultural assessed residues, and then applying equations from the literature to estimate the amount of energy (J) and power (kW) obtained from the thermal conversion of residual biomasses. Results are presented for three residual biomasses from agricultural crops (corn cobs, rice husk and sugarcane bagasse) with cartograms for all micro regions at the state of Mato Grosso do Sul, graphics for quantification in the cities where crops production occurs and a table for total energy obtained by conversion processes. For the whole state of MS, Dourados micro region was identified as the most promising for energy in agriculture with three main cities (Dourados, Rio Brilhante and Maracaju), by solid biofuel availability to provide about 11% of the total electrical energy consumption in 2014.

Integrated energy simulation of a deep level mine cooling system through a combination of forward and first-principle models applied to system-side parameters

Mine cooling systems typically account for around 25% of the total electrical energy consumption of deep level mines. Numerous energy saving strategies have been implemented, with various levels of success. Most of the previously implemented strategies involved load shifting, energy efficiency and demand reduction. To achieve this, some levels of control are often introduced to control chilled water consumption, water and air mass flow rates as well as scheduling cooling system operation in order to shift the load primarily to off-peak periods.To support and sustain further reduction of energy consumption and optimisation of savings, a simulation model of the integrated cooling system represented by smooth and continuous equations is needed to assist the optimisation computation. This study focused on developing such an integrated mine cooling system simulation model to mimic the thermal hydraulic behaviour along with the energy consumption of the complete mine cooling system. This was achieved by coupling various models representing the major cooling system components such as chillers, cooling towers, pumps and fans. Although various cooling system energy simulations were considered before, very few quantify the simulation accuracy on a component basis; furthermore, only limited cases were found where the entire system was considered. For these cases not all components were always based on explicit models which would eliminate the requirement for iterative computation which deter optimisation applications.The simulation model was used to predict the energy consumption of the integrated cooling system of a deep level gold mine in South Africa. The simulation model obtained an average error when compared to the mine’s system data of 3.5% for a selected dataset and 2.5% for another dataset one month later. The successful energy simulation of an integrated mine cooling system would allow for a holistic view on the total power consumption as one parameter or any combination of parameters of the system changes.

Ultralow-energy electro-absorption modulator consisting of InGaAsP-embedded photonic-crystal waveguide

Towards realizing highly integrable low-energy optical modulators, the small device capacitance (C) as well as the low driving voltage (Vpp) is demanded for suppressing the charging energy during the dynamic operation. Although an electro-absorption modulator (EAM) has great potential in reducing them, the additional energy associated with the photocurrent flow will limit the lower-bound of the consumption energy. In this work, a broadband EAM based on an InGaAsP-embedded photonic crystal waveguide is demonstrated, revealing a high modulation bit rate of up to 56 Gbit/s. The air-bridge structure and a device length of 100 μm or less result in a small C ≤ 13 fF while operating with Vpp < 1 V. Particularly, the operation in low reverse voltage for a p-i-n junction, that is, −0.2 V as the minimum value in this study, works effective for the reduction of energy involving the photocurrent. This results in the total electrical energy consumption of <2 fJ/bit, which is lower than that of any waveguide EAMs.