Customer active power consumption prediction for the next day based on historical profile

Energy consumption prediction application is one of the most important fieldsthat is artificially controlled with Artificial Intelligence technologies to maintainaccuracy for electricity market costs reduction. This work presents a way to buildand apply a model to each costumer in residential buildings. This model is built by using Long Short Term Memory (LSTM) networks to address a demonstration of time-series prediction problem and Deep Learning to take into consideration the historical consumption of customers and hourly load profiles in order to predict future consumption. Using this model, the most probable sequence of a certain industrial customer’s consumption levels for a coming day is predicted. In the case of residential customers, determining the particular period of the prediction in terms of either a year or a month would be helpful and more accurate due to changes in consumption according to the changes in temperature and weather conditions in general. Both of them are used together in this research work to make a wide or narrow prediction window.A test data set for a set of customers is used. Consumption readings for anycustomer in the test data set applying LSTM model are varying between minimum and maximum values of active power consumption. These values are always alternating during the day according to customer consumption behavior. This consumption variation leads to leveling all readings to be determined in a finite set and deterministic values. These levels could be then used in building the prediction model. Levels of consumption’s are modeling states in the transition matrix. Twenty five readings are recorded per day on each hour and cover leap years extra ones. Emission matrix is built using twenty five values numbered from one to twenty five and represent the observations. Calculating probabilities of being in each level (node) is also covered. Logistic Regression Algorithm is used to determine the most probable nodes for the next 25 hours in case of residential or industrial customers.Index Terms—Smart Grids, Load Forecasting, Consumption Prediction, Long Short Term Memory (LSTM), Logistic Regression Algorithm, Load Profile, Electrical Consumption.</p

Smart Electric Grids Three-Phase Automatic Load Balancing Applications using Genetic Algorithms

Smart power grid is going to be the future grid. Conventional, renewable and alternate sources incorporating for power generation[1]. Smart Electrical Grids require nowadays a large interest in the electrical load distribution balancing problem.  This problem is a well known for not having an optimal solution for large-scale systems, where the number of single phase consumers connected to three phase systems increases especially in very large-scale electrical distribution systems.  This paper presents a new control technique for an automatic circuit phase change as well as an optimisation approach using Genetic Algorithms (GA) used to enhance the solution of electrical load distribution balancing problem.  In the first part of the paper, the system under study is introduced, as well as the various solutions adopted.  In the second part of the paper, a GA formulation and implementation of the solution is presented.  The efficiency of the GA solution is also discussed

Influence of endotoxin induced fever on the pharmacokinetics of intramuscularly administered cefepime in rabbits

This study examined the effect of experimentally induced fever on the pharmacokinetics of cefepime (75 mg/kg BW) administered intramuscularly to six rabbits. The study was carried out in two consecutive phases separated by a two-week washout period. An infection was induced by an intravenous inoculation of 5 × 108 colony-forming units of Escherichia coli 24 h before the pharmacokinetic investigation. A quantitative microbiological assay was employed to measure the plasma cefepime concentrations using an agar-gel diffusion method with Bacillus subtilis ATCC 6633 as the test organism. Twenty-four hour after the injection, the rectal temperature in the infected animals increased by 1–. There was a significant reduction in the elimination half-life by 21.8% in the febrile rabbits compared to healthy animals. In addition, the infection significantly increased the peak plasma concentrations by 11.9%, the mean residence time by 19.9%, the area under the plasma-concentration-time curve by 53.6% and the area under the moment curve by 62.3%. In conclusion, the endotoxin-induced febrile state produced significant changes in the plasma levels as well as some of the pharmacokinetic variables of cefepime in rabbits

Evaluation of Oxfendazole, Praziquantel and Albendazole against Cystic Echinococcosis: A Randomized Clinical Trial in Naturally Infected Sheep

Cystic Echinococcosis (CE) is a near-cosmopolitan parasitic zoonosis that causes economic losses in many regions of the world. This parasitic infection can be regarded as an emerging or re-emerging disease causing considerable losses in livestock production. CE is produced by the larval cystic stage (hydatid) of the dog parasite Echinococcus granulosus. After infective eggs are ingested, cysts develop mainly in lungs and liver of humans and animals (sheep, cattle, pigs, horses, etc). Infected people may require surgery and/or Albendazole-based chemotherapy. In this study, we evaluated the effects of Oxfendazole alone (an antiparasitic drug used in animals), Oxfendazole plus Praziquantel, and Albendazole plus Praziquantel against hydatid cysts in sheep over 4 to 6 weeks of treatment. All of the treatments in this study were efficacious in killing the larval stages and, therefore, in minimizing the risk of a dog acquiring new infections (taenias). These treatment schemes can be added to control measures in animals and eventually could be used for the treatment of human infection. Further investigations on different schedules of monotherapy or combined chemotherapy are needed, as well as studies to evaluate the safety and efficacy of Oxfendazole in humans

Pharmacokinetics and Bioavailability of Moxifloxacin in Calves Following Different Routes of Administrations

Background: Moxifloxacin is a new fourth-generation 8-methoxy fluoroquinolone developed primarily for the treatment of community-acquired pneumonia and upper respiratory tract infections. The aim of the study was to investigate the plasma pharmacokinetics characteristic of moxifloxacin in calves, after intravenous, intramuscular and subcutaneous administration of a single dose. Meanwhile, plasma protein binding and bioavailability of moxifloxacin were also estimated. Methods: Plasma concentrations of moxifloxacin were measured using a modified HPLC method, and the extent of plasma protein binding was determined in vitro using ultrafiltration. Results: Following intravenous administration, the half life of elimination, the volume of distribution at steady state and the area under the curve were 3.29 h, 0.94 l/kg and 24.72 μg·h/ml, respectively. After intramuscular and subcutaneous administration of moxifloxacin at the same dose, the peak plasma concentrations were 2.41 and 2.20 μg/ml and were obtained at 1.54 and 1.59 h, respectively. The systemic bioavailabilities were 87.19 and 75.94%, respectively. The in vitro plasma protein binding of moxifloxacin in plasma of calves was 27%. Conclusion: A high peak plasma concentration, area under the curve, rapid absorption and bioavailability following intramuscular and subcutaneous administration characterize the pharmacokinetics of moxifloxacin in calves

CARBON NANOTUBES AND THEIR COMPOSITES: A REVIE

Carbon nanotubes have been the focus of considerable research. Numerous investigators have since reported remarkable physical and mechanical properties for this new form of carbon. From unique electronic properties and a thermal conductivity higher than diamond to mechanical properties where the stiffness, strength and resilience exceeds any current material, carbon nanotubes offer tremendous opportunities for the development of fundamentally new material systems. In particular, the exceptional mechanical properties of carbon nanotubes combined with their low density, offer scope for the development of nanotubes reinforced composite materials. The potential for nanocomposites reinforced with carbon tubes having extraordinary specific stiffness and strength represent tremendous opportunity for application in the 21st century. This paper provides an overview of recent advances reported in literature in composites research in the context of reinforcement with carbon nanotubes. Current state of research has indicated the potential of these nanocomposites but at the same time, has illustrated the significant challenges in processing and improving propertie