Energy Consumption Load Research Articles

BOAT: A Block-Streaming App Execution Scheme for Lightweight IoT Devices

The contradiction between the limited capability of lightweight Internet-of-Things (IoT) devices and ever-increasing user demands is a fundamental and challenging problem in the era of IoT. One of the most important challenges is that lightweight IoT devices are generally embedded with fixed applications on resource-constrained hardware, and cannot enable on-demand service provisioning. In this paper, we propose BOAT, a block-streaming application execution scheme based on transparent computing (TC), which can remotely retrieve the necessary parts of traditional applications from edge servers on demand and run them on IoT devices locally. Specifically, we first exploit TC to build a scalable IoT system, where the applications of lightweight IoT devices are stored in edge servers or cloud but can be dynamically loaded on IoT devices in a block-streaming way. Moreover, we present a code partition approach to split the codes of a whole service into numerous functional blocks at the edge side. Such that, IoT devices only need to load necessary blocks of an application to obtain the requested services without loading the whole application codes. A lightweight I/O virtualization mechanism and a fine-grained relocation technology are then developed to support the block-streaming service loading. Our experimental results on a lightweight wearable device demonstrate that the proposed scheme can efficiently achieve flexible service provisioning with improved scalability and reduced service loading delay and energy consumption.

Development of building energy saving advisory: A data mining approach

Occupants’ behavior and their interaction with home appliances are crucial for assessing building energy consumption. This study proposes a new methodology for monitoring the energy consumed in building end-use loads to build an advisory system. The built system alerts occupants to take certain measures (prioritized recommendations) to reduce energy consumption of end-use loads. The quantification of potential savings is also provided upon following said measures. The proposed methodology is also capable of evaluating the energy savings performed by the occupants. The system works based on the analysis of historical data generated by occupants using data mining techniques to output highly feasible recommendations. For demonstration purposes, the methodology was tested on the real dataset of a building in Japan. The dataset includes detailed energy consumption of end-use loads, categorized as hot water supply, lighting, kitchen, refrigerator, entertainment & information, housework & sanitary, and others. Results suggest that the developed models are accurate, and that it is possible to save up to 21% of total energy consumption by only changing occupants’ energy use habits.

A comparison of resource allocation process in grid and cloud technologies

Grid Computing and Cloud Computing are two different technologies that have emerged to validate the long-held dream of computing as utilities which led to an important revolution in IT industry. These technologies came with several challenges in terms of middleware, programming model, resources management and business models. These challenges are seriously considered by Distributed System research. Resources allocation is a key challenge in both technologies as it causes the possible resource wastage and service degradation. This paper is addressing a comprehensive study of the resources allocation processes in both technologies. It provides the researchers with an in-depth understanding of all resources allocation related aspects and associative challenges, including: load balancing, performance, energy consumption, scheduling algorithms, resources consolidation and migration. The comparison also contributes an informal definition of the Cloud resource allocation process. Resources in the Cloud are being shared by all users in a time and space sharing manner, in contrast to dedicated resources that governed by a queuing system in Grid resource management. Cloud Resource allocation suffers from extra challenges abbreviated by achieving good load balancing and making right consolidation decision.

Rancang Bangun Smart Energy Meter Berbasis UNO dan Raspberry Pi

Smart energy meter (SEM) is developed to the transparency goal of energy consumption by consumers for tariff strategy. The tariff strategy is designed based on the type of consumer load that per-kWh energy price difference for linear and nonlinear loads. Energy conscious awareness is built by displaying all load power consumption information from consumer loads such as voltage, current, power factor, load properties, load type, power, volt-ampere distortion, power factor distortion, THD, current and voltage waveforms, and harmonic frequency spectrum. The SEM is designed using ZMCT103C and ZMPT101B for current and voltage sensors, respectively. The SEM security function uses the SW420 vibrating sensor, open/close bolt sensor on the chasing and electronic lock. The results show that error measurement of the voltage sensor, current sensor, and power factor are 0.8%, 1.5%, and 1.0% respectively. The SEM security works well as the information on maintenance and criminal actions are informed on screen and buzzer sound for criminal acts. The tariff strategy for linear and nonlinear loads is used to calculate the energy cost per-kWh. The details of load energy consumption are stored in a database.

Physarum-inspired multi-parameter adaptive routing protocol for coal mine hybrid wireless mesh networks

Hybrid wireless mesh networks are suitable for complex environment communication in coal mine. Mesh clients with application service and routing function in hybrid wireless mesh networks can form a highly robust hybrid network with mesh routers. The processes of nutrient flux transfer and path choice in Physarum network are similar to data transmission and routing selection in hybrid wireless mesh networks. In this article, we use Physarum-inspired autonomic optimization model to design a Physarum-inspired multi-parameter adaptive routing protocol to improve the service quality of coal mine hybrid wireless mesh networks. Physarum-inspired multi-parameter adaptive routing protocol has achieved distributed routing decision by drawing the hybrid wireless mesh network parameters into Poisson’s equation of Physarum-inspired autonomic optimization model to measure the quality of link and implements two adjustment strategies to make the protocol more adaptive. The resource-dependent adjustment, which considers the irreversible energy consumption and recoverable buffer occupation, makes the energy consumption problem prominent when there is a lack of energy. The position-dependent adjustment makes routing decision efficient according to the load of different positions, which is caused by many-to-one data transmission model in coal mine. Based on NS2, simulation experiments are performed to evaluate the performance of Physarum-inspired multi-parameter adaptive routing protocol, and the results are compared with those of ad hoc on-demand distance vector, HOPNET, ANT-DSR, and Physarum-inspired routing protocols. The experimental results show that the route path selected by Physarum-inspired multi-parameter adaptive routing protocol is better than those selected by the other four protocols in the performance of average end-to-end delay and delivery ratio. The balance of energy consumption and network load is achieved and the network lifetime is effectively prolonged when using Physarum-inspired multi-parameter adaptive routing protocol.

Optimal Demand-Side Management for Smart Micro Grid with Storage

In this paper, an autonomous and distributive demand-side management based on Bayesian game theory is developed and applied among users in a grid connected micro-grid with storage. To derive that strategy, an energy consumption of shiftable loads belonging to a given user is modelled as a noncooperative three-player game of incomplete information, in which each user plays against the storage unit and an opponent gathering all the other users in the micro-grid. Each player is assumed to be endowed with statistical information about its behavior and that of its opponents so that he can take actions maximizing his expected utility. Results of the proposed strategy evaluated by simulating, under MATLAB environment, a connected micro-grid with storage device evidence its efficacy when employed to manage the charging of electric vehicles.

Case study of effective current estimation in main circuit of tram vehicle

Currently the trams are experiencing the renaissance and more and more Polish cities decide to renew the fleet. New vehicles are usually characterized by high transport capacity, low floor, climate comfort, crash resistance, low energy consumption and low axle loads. Such requirements can only be achieved through optimization in many areas of the vehicle's construction. One of such tasks is the optimal selection of components of the main circuit installation and traction needs. The paper is oriented on the issue of engineering modelling of the effective values of currents flowing in the main circuit and the tramway traction needs circuits. Due to the non-periodic nature of the vehicle traffic loads in urban traffic, the optimal selection of the performance parameters helps in better selection of conductor cross sections, filters and safeguards. A better selection of these components means a reduction of its weight, which is extremely important for meet basic requirements of modern tram vehicles. The paper is intended to gather the experience of authors in this field of knowledge and compares several modelling strategies that the authors use during designing tram vehicles. The elaboration summarizes the results obtained using different modelling strategies, and shows correlation between effective value and selection of components.

Trend appraisal of Nigeria's energy sector and its implication on living standards

Utility capacity and energy consumption in Nigeria over a 10-year period 2005-2014 were analysed. Improvement was recorded from 2010 through 2014 for yearly and monthly peak load demand and energy consumption. The coming on stream of some NIPP plants, reduction in vandalism of gas infrastructure following the presidential amnesty to Niger Delta militant in August 2009, the launching of power sector road map in 2010 which saw to the eventual privatisation of power generation and distribution and mean monthly reservoir inflow for Kainji Dam respectively were seen to influence the yearly and monthly energy distribution. By regression technique, per capita electricity consumption (EC/capita) was used to predict to a reasonable degree of accuracy the living standard indicators-infrastructure availability and purchasing power parity. The implication of this therefore is that for there to be a positive shift in the economic and social well-being of the populace attention must be given to the provision of functional energy infrastructure.

Energy audit and optimal power supply for a commercial building in Nigeria

Energy is indispensable to human existence, and its need is on the increase daily due to technological advancement. In recent times, there is sporadic increase in the development of modern buildings, and these buildings require a cost-effective and sustainable source of energy. Nigeria and some other African countries have major energy shortage issues due to the insufficient power generation and distribution facilities. This energy poverty creates a challenge for the growing population, and as such, it is vital to ensure that the energy supplied to a building is duly optimized and delivered cost effectively. In this study, an energy audit of an eight-floor multipurpose business complex was performed to determine the nature and type of loads within the building. Based on the load profile and energy consumption over a 10-year period, three alternative energy sources (national grid, diesel generator and photovoltaic [PV] system) were considered using various load-sharing ratios. The result reveals that though PV solar system is a renewable energy source that reduces the production of greenhouse gases generated by burning diesel, its continuous application in low–middle-income country like Nigeria may be challenged by the initial start-up capital which raises the cost of its unit energy.

Compressive Data Gathering Based on Even Clustering for Wireless Sensor Networks

Compressive data gathering (CDG) based on compressed sensing (CS) theory for wireless sensor networks (WSNs) greatly reduces the amount of data transmitted compared with the traditional acquisition method that each node forwards the collected data directly to the next node. CDG combined with sparse random projection can further reduce the amount of data and thus prolong the lifetime of the WSN. The method of randomly selecting projection nodes as cluster heads to collect the weighted sum of sensor nodes outperforms the non-CS (without using CS) and hybrid-CS (applying CS only to relay nodes that are overloaded) schemes in decreasing the communication cost and distributing the energy consumption loads. However, the random selection of projection nodes causes the overall energy consumption of the network to be unstable and unbalanced. In this paper, we propose two compressive data gathering methods of balanced projection nodes. For WSN with uniform distribution of nodes, an even clustering method based on spatial locations is proposed to distribute the projection nodes evenly and balance the network energy consumption. For WSN with unevenly distributed nodes, an even clustering method based on node density is proposed, taking into account the location and density of nodes together, balancing the network energy and prolonging the network lifetime. The simulation results show that compared with the random projection node method and the random walk method, our proposed methods have better network connectivity and more significantly increased overall network lifetime.

Stochastic assessment of distributed generation hosting capacity and energy efficiency in active distribution networks

Active network management (ANM) aims to increase the capacity of variable distributed generation (DG), which can be connected to existing distribution networks. In this study, it is proposed to simultaneously consider the efficient use of energy resources when high shares of DG are procured through the ANM approach. To that end, a multi-period and multiobjective optimisation algorithm, based on the linearised optimal power flow, is formulated. The algorithm seeks to maximise the installed capacity of DG while minimising the energy losses and consumption of voltage-dependent loads. The objectives are optimised considering the coordinated operation of voltage regulators and on-load tap changers, and the management of DG generation curtailment and reactive power compensation from DG. Additionally, the effects of load and generation uncertainties are addressed through a two-stage stochastic programming formulation of the multiobjective problem. The result is a set of non-inferior solutions, which allows exploring the degree of conflict among the objectives. The proposed approach was tested on two IEEE test feeders and the solutions show a significant improvement in the system's energy efficiency with a low impact on the amount of connected DG.

Load balancing for mitigating hotspot problem in wireless sensor network based on enhanced diversity pollen

ABSTRACTThis paper proposes a load balancing to mitigate the hot spot problem in wireless sensor network (WSN), based on enhancing diversity pollens in Flower pollination algorithm (FPA). The hotspot problem in the WSN is spots near base station (BS) that consume more energy and drain out energy more quickly than other nodes farther from the BS. The spots near BS are hotter than other places due to the heavy traffic from the cluster members and other cluster heads (CH) for relaying data to BS. Enhancing diversity pollens for FPA is one of the solutions to deal smoothly with trapping local extrema for solving the hotspot problem. To evaluate the proposed algorithm, we firstly use a set of benchmark functions to test performance quality, and secondly, we deal with the load balancing problem in WSN. The results compared with some metaheuristic approaches and other related clustering algorithms demonstrate that the proposed method performs better than the others regarding various performance metrics such as the load balancing, execution time, energy consumption, and convergence rate.

Techno-Economic and dynamic analysis of low velocity wind turbines for rural electrification in agricultural area of Ratchaburi Province, Thailand

This paper presents the analysis of potential wind speed of electrical power generating using for agriculture in Ratchaburi province, Thailand. The total area is 1,900 square kilometers. First of all, the agriculture electrical load (AEL) data was investigated from all farming districts in Ratchaburi. Subsequently, the load data was analyzed and classified by the load power and energy consumption at individual district. The wind turbine generator (WTG) at capacity rate of 200w, 500w, 1,000w, and 2,000w were adopted to implement for the AEL in each area at wind speed range of 3 to 6 m/s. This paper shows the approach based on the wind speed at individual district to determine the capacity of WTG using the capacitor factor (CF) and the cost of energy (COE) in baht per unit under different WTG value rates. Ten locations for wind station installations are practical investigated. Results show that for instance, the Damnoen Sa-duak (DN-04) one of WTG candidate site is identically significant for economic investment of installing rated WTG. The results of COE are important to determine whether a wind site is good or not.

Offline first-fit decreasing height scheduling of power loads

In this paper, we consider the problem of scheduling energy consumption loads in the setting of smart electric grids. Each load is characterized as a “job” by a start (arrival) time and a deadline by which a certain amount of electric energy must be delivered to the load. A job may be preemptable, i. e. it can be interrupted or non-preemptable. Specifically, we focus on scheduling a mixture of preemptable and non-preemptable jobs with the same arrival time and deadline with the goal of minimizing the peak power. We study and modify the first-fit decreasing height algorithm of the strip packing problem for this purpose. We prove its asymptotic performance bound: $$1.7 OPT + 1$$ and its tightness. The heuristic results in at most one preemption per job, and it can be implemented with $$O(n \log n + nq)$$ time complexity where q is the number of $$non-preemptable$$ jobs.

Deconstructing the Energy Consumption of the Mobile Page Load

Modeling the energy consumption of applications on mobile devices is an important topic that has received much attention in recent years. However, there has been very little research on modeling the energy consumption of the mobile Web. This is primarily due to the short-lived yet complex page load process that makes it infeasible to rely on coarse-grained resource monitoring for accurate power estimation. We present RECON, a modeling approach that accurately estimates the energy consumption of any Web page load and deconstructs it into the energy contributions of individual page load activities. Our key intuition is to leverage low-level application semantics in addition to coarse-grained resource utilizations for modeling the page load energy consumption. By exploiting fine-grained information about the individual activities that make up the page load, RECON enables fast and accurate energy estimations without requiring complex models. Experiments across 80 Web pages and under four different optimizations show that RECON can estimate the energy consumption for a Web page load with an average error of less than 7%. Importantly, RECON helps to analyze and explain the energy effects of an optimization on the individual components of Web page loads.

Experimental investigation on operating performance of digital variable multiple air conditioning system

The objective of this paper was to study operating performance of digital variable multiple (DVM) air conditioning (AC) system by experiments. Variable refrigerant flow performance, part load performance, defrosting performance, and energy consumption model of DVM AC system were analyzed by experimental data. The results indicate that variable refrigerant volume characteristic and excellent part load performance are beneficial to seasonal energy saving operation of DVM AC system. Seasonal energy consumption of DVM AC system is able to be calculated based on hourly energy consumption model and defrosting energy consumption model obtained by experimental data regression. Under defrosting condition, indoor unit which contributes the most to defrosting is the one nearest to outdoor unit and its corresponding indoor thermal environment is adversely affected. Thus, it is suggested to take this factor into account when configuring the capacity of the indoor unit which is nearest to outdoor unit.

Experimental Results on a Wireless Wattmeter Device for the Integration in Home Energy Management Systems

This paper presents a home area network (HAN)-based domestic load energy consumption monitoring prototype device as part of an advanced metering system (AMS). This device can be placed on individual loads or configured to measure several loads as a whole. The wireless communication infrastructure is supported on IEEE 805.12.04 radios that run a ZigBee stack. Data acquisition concerning load energy transit is processed in real time and the main electrical parameters are then transmitted through a RF link to a wireless terminal unit, which works as a data logger and as a human-machine interface. Voltage and current sensing are implemented using Hall effect principle-based transducers, while C code is developed on two 16/32-bit microcontroller units (MCUs). The main features and design options are then thoroughly discussed. The main contribution of this paper is that the proposed metering system measures the reactive energy component through the Hilbert transform for low cost measuring device systems.

Potential of Utilizing Different Natural Cooling Sources to Reduce the Building Cooling Load and Cooling Energy Consumption: A Case Study in Urumqi

Generally, Central Asia is typical for regions with strong solar radiation and various natural cooling sources. The heat gain from the building envelope accounts for a large part of the cooling load there. Thus, the pipe-embedded envelope is receiving attention as a semi-active system of utilizing natural energy for cooling. In this study, the performance of the pipe-embedded envelope used in Urumqi is numerically investigated. The energy saving potential regarding evaporative cooling and a ground-source heat exchanger (GSHE) is evaluated over a complete summer. The results show that the built-in pipes can reduce 80% of the solar heat gain through windows, with an effectiveness of around 60%. External windows rather than internal windows should be insulated because the air cavity is cool. With respect to the pipe-embedded wall, it becomes a radiant cooling panel absorbing the heat from the room, with an effectiveness around 83%. The seasonal cooling energy is decreased by 25%–50% in a typical office with a pipe-embedded envelope. Offices with a large window-to-wall ratio are acceptable because natural cooling is employed. GSHE performs the best among the selected sources. The effectiveness of evaporative cooling is also satisfactory, with an energy saving rate of 27%. Overall, the pipe-embedded system is suitable for climatic regions like Urumqi.

Investigation on Passive Energy-saving Technologies of Demonstration Houses in Taohai Pasture

With the construction of new villages in China, energy-saving technologies for rural buildings have been attracted extensive attention. According to the characteristic of severe cold climate in Hailar, Inner Mongolia, and the structure of rural residential buildings in Taohai Pasture and the energy use customs in these houses, the energy efficiencies of passive energy-saving technologies which are applied to demonstration houses in Taohai Pasture are analyzed. Based on BIM, the calculation models of three types of houses, including the demonstration house applied passive energy-saving technologies, the traditional house and the energy-saving standard model based on local rural residential energy-saving design standard, are established. Then energy simulation software is used to calculate dynamic heating loads of these houses. Furthermore some indexes such as heating load and annual energy consumption per unit building area are compared. The calculated results show that the heating load of the demonstration house in Taohai Pasture is decreased obviously through the passive energy-saving strategies of the thermal insulation of building envelopes, the effective setting of a solar room and the reasonable distribution of indoor functional zones. Compared to the annual heating loads of the traditional house and the energy-saving standard model, that of the demonstration house is reduced about 74.8 % and 28.9%, respectively. The investment increase of the envelope of the demonstration house can be recovered within 11.4 years through the reduction of annual energy use for heating.

Quantity and electricity consumption of plug load equipment on a university campus

The percent of energy consumed by plug load equipment in commercial buildings is on the rise. Research conducted in the past has included surveying plug load equipment, measuring plug load electricity consumption and equipment operating patterns, and studying plug load reduction solutions in office buildings, but plug load energy use across other building types is poorly understood. A university campus, which houses many building types, presents a unique opportunity to understand plug load profiles across building types. In this study, an equipment inventory was performed in 220 buildings on Stanford University’s campus, totaling 8,901,911 ft2 of building space and encompassing lab buildings, office buildings, recreation facilities, public space, and service buildings. Within these buildings, 110,529 pieces of plug load equipment were recorded. Energy consumption estimates were developed from published values and used to evaluate the aggregate plug load energy consumption of this equipment by equipment type and by building type. In total, it is estimated that the plug loads from these buildings consume nearly 50 million kWh per year and comprise 32% of the electricity consumption of the buildings surveyed. This data can be used to better target energy conservation efforts throughout multiple sectors.