Power consumption analysis of electrical installations at healthcare facility

Producción CientíficaThis paper presents a methodology for power consumption estimation considering harmonic and interharmonic content and then it is compared to the power consumption estimation commonly done by commercial equipment based on the fundamental frequency, and how they can underestimate the power consumption considering power quality disturbances (PQD). For this purpose, data of electrical activity at the electrical distribution boards in a healthcare facility is acquired for a long time period with proprietary equipment. An analysis in the acquired current and voltage signals is done, in order to compare the power consumption centered in the fundamental frequency with the generalized definition of power consumption. The results obtained from the comparison in the power consumption estimation show differences between 4% and 10% of underestimated power consumption. Thus, it is demonstrated that the presence of harmonic and interharmonic content provokes a significant underestimation of power consumption using only the power consumption centered at the fundamental frequency.SEP-CONACYT, under grant 222453-2013FOMIX, under grant QRO-2014-C03-250269FOFIUAQ-FIN20161

Deskolo : un outil de supervision énergétique pour les parcs informatiques

Session "Démo"National audienceCe projet national, financé par Systematic, a pour vocation de proposer de nouveaux outils logiciels pour la gestion énergétique informatique. En effet, la problématique de la gestion et de l'économie s'avère indispensable à grande échelle, en particulier donc pour des parcs informatiques de centaines d'ordinateurs individuels. Les différentes problématiques abordées dans le cadre de ce projet s'articulent d'une part autour de la caractérisation de la consommation, dans un objectif de supervision, et d'autre part la gestion des machines, afin de réduire la consommation, en particulier pendant les phases d'inactivité des machines, durant la nuit ou les week-ends. La première problématique abordée dans ce projet concerne la caractérisation de la consommation énergétique de chaque machine. En effet, afin de pouvoir estimer la consommation globale du parc uniquement, il est nécessaire de connaître la consommation de chaque machine. Le moyen le plus simple et le plus précis consiste à mesurer à la prise la puissance consommé puis de remonter l'ensemble des mesures à un serveur de données. Cependant cette solution nécessite d'installer un capteur supplémentaire et de gérer l'ensemble du matériel, ce qui rajoute une contrainte physique à la gestion du parc, tout en impliquant un surcoût non négligeable. La solution retenue ici consiste à construire un modèle grâce à des outils d'apprentissage statistique, afin de pouvoir prédire la consommation de chaque machine en utilisant des paramètres facilement accessibles tels que la puissance du processeur, l'activité, etc. L'avantage réside tout d'abord dans l'embarquement très simple du logiciel qui peut tourner en tâche de fond sans impacter l'activité courante. De plus, l'utilisateur peut avoir directement accès à sa consommation et peut estimer la puissance consommée dans sa globalité. L'utilisation de méthodes statistiques permet également d'apprendre différentes fonctions de prédiction selon les modèles de machines disponibles sur le parc. Il est ainsi possible de prendre en considération le vieillissement du matériel et de mettre à jour les modèles utilisés. La méthode utilisée a été développée dans l'article édité sur le site suivant : http://www.wallix.org/2011/08/02/deskolo-project-modeling-the-power-consumption/ L'autre problématique concerne la collecte des données au travers de l'ensemble du parc. Grâce à des outils développés par les partenaires dans le cadre de la supervision et la sécurisation de parc, les données peuvent être centralisées sur le serveur de supervision, tout en conservant l'anonymat puisque seule la puissance électrique est stockée. En fonction des paramètres d'utilisation définis par chaque utilisateur, les ordinateurs peuvent ensuite être systématiquement allumés et éteints chaque jour afin d'optimiser la consommation globale. Différents outils ont été développés durant ce projet, tout d'abord des composants spécifiques pour la supervision, afin de pouvoir communiquer avec chaque machine du réseau, puis des interfaces utilisables localement par chaque utilisateur afin d'avoir une vue de sa consommation, comme l'illustrent les figures ci-dessous. Deux versions ont été développées pour les plateformes Windows et Linux

Power Consumption and Energy Estimation in Smartphones

A developer needs to evaluate software performance metrics such as power consumption at an early stage of design phase to make a device or a software efficient especially in real-time embedded systems. Constructing performance models and evaluation techniques of a given system requires a significant effort. This paper presents a framework to bridge between a Functional Modeling Approach such as FSM, UML etc. and an Analytical (Mathematical) Modeling Approach such as Hierarchical Performance Modeling (HPM) as a technique to find the expected average power consumption for different layers of abstractions. A Hierarchical Generic FSM “HGFSM” is developed to be used in order to estimate the expected average power. A case study is presented to illustrate the concepts of how the framework is used to estimate the average power and energy produced

Energy Efficiency in the ICT - Profiling Power Consumption in Desktop Computer Systems

Energy awareness in the ICT has become an important issue. Focusing on software, recent work suggested the existence of a relationship between power consumption, software configuration and usage patterns in computer systems. The aim of this work was collecting and analysing power consumption data of general-purpose computer systems, simulating common usage scenarios, in order to extract a power consumption profile for each scenario. We selected two desktop systems of different generations as test machines. Meanwhile, we developed 11 usage scenarios, and conducted several test runs of them, collecting power consumption data by means of a power meter. Our analysis resulted in an estimation of a power consumption value for each scenario and software application used, obtaining that each single scenario introduced an overhead from 2 to 11 Watts, which corresponds to a percentage increase that can reach up to 20% on recent and more powerful systems. We determined that software and its usage patterns impact consistently on the power consumption of computer systems. Further work will be devoted to evaluate how power consumption is affected by the usage of specific system resource

Profiling Power Consumption on Mobile Devices

The proliferation of mobile devices, and the migration of the information access paradigm to mobile platforms, motivate studies of power consumption behaviors with the purpose of increasing the device battery life. The aim of this work is to profile the power consumption of a Samsung Galaxy I7500 and a Samsung Nexus S, in order to understand how such feature has evolved over the years. We performed two experiments: the first one measures consumption for a set of usage scenarios, which represent common daily user activities, while the second one analyzes a context-aware application with a known source code. The first experiment shows that the most recent device in terms of OS and hardware components shows significantly lower consumption than the least recent one. The second experiment shows that the impact of different configurations of the same application causes a different power consumption behavior on both smartphones. Our results show that hardware improvements and energy-aware software applications greatly impact the energy efficiency of mobile device

Reducing Power Consumption in Backbone Networks

Abstract—According to several studies, the power consumption of the Internet accounts for up to 10 % of the worldwide energy consumption, and several initiatives are being put into place to reduce the power consumption of the ICT sector in general. To this goal, we propose a novel approach to switch off network nodes and links while still guaranteeing full connectivity and maximum link utilization. After showing that the problem falls in the class of capacitated multi-commodity flow problems, and therefore it is NP-complete, we propose some heuristic algorithms to solve it. Simulation results in a realistic scenario show that it is possible to reduce the number of links and nodes currently used by up to 30 % and 50 % respectively during off-peak hours, while offering the same service quality

Purchasing Power, Fruits Vegetables Consumption, Nutrition Status Among Elementary School Student

Food purchasing power is usually defined as a household\u27s economic ability to obtain food which is determined by measuring the income allocated for food purchase, the price of food consumed, and the number of family members. More than 50% of the sub-districts in South Central Timor are vulnerable to food consumption due to their low purchasing power, thus causing low fruits and vegetables consumption. To analyze the correlation between purchasing power, fruits and vegetables consumption, with nutrition status of elementary school students. Analytic observational using a cross sectional design. Sample size of 108 students was achieved using simple random sampling method. Independent variables are purchasing power, fruits and vegetables consumption. Dependent variable is nutrition status. Fruits and vegetables consumption data was collected using food frequency, purchasing power data was collected using questionnaires, and nutrition status was collected by calculating IMT/U. The statistics tests used were chi square test. The mean number of fruits and vegetables consumption of the elementary students was 0,36±0,483 and the mean number of purchasing power was 2,80±0,405. Bivariate study test results show a significant correlation between purchasing power and nutrition status (p=0,039) and a significant correlation between fruits and vegetables consumption and nutrition status (p=0,000). There is a correlation between purchasing power, fruits and vegetables consumption, and nutrition status in elementary school students