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

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

Automobile compression composite elliptic spring

An automotive suspension system is designed to provide both safety and comfort for the occupants. When a vehicle encounters a road surface irregularity, the tire deforms and the suspension displaces. Some of the energy caused by the disturbance is dissipated in the tire, while in the old design, some energy is dissipated in the shock absorber and the remainder of the energy is stored in the coil spring. In this paper, Finite element models were developed to optimize the material and geometry of the composite elliptical spring based on the spring rate, log life and shear stress. The influence of ellipticity ratio on performance of woven roving wrapped composite elliptical springs has been investigated both experimentally and numerically, this study demonstrated that composites elliptical spring can be used for light and heavy trucks and meet the requirements, together with substantial weight saving. The results showed that the ellipticity ratio significantly influenced the design parameters. Composite elliptic spring with ellipticity ratios of a/b 2 displayed the optimum spring model

Role of some complexing agents during electrodeposition of tellurium

205-210<span style="font-size:11.0pt;line-height: 115%;font-family:Calibri;mso-fareast-font-family:" times="" new="" roman";mso-bidi-font-family:="" "times="" roman";mso-ansi-language:en-us;mso-fareast-language:en-us;="" mso-bidi-language:ar-sa"="" lang="EN-US">he uses of electrodeposition processes for the production of tellurium as a pure metal from different baths containing tellurous acid with some suitable additives have been investigated. The quantity and quality of the deposited metal are found to be dependent on the type of the baths used. The effects of different parameters such as kind and concentration of acids, current density, temperature, electrode type (platinum and graphite) and metal to complexing agent concentration ratio on the cathodic efficiency and on the quality of deposit have been discussed. The interfering effect of some cations and anions has also been studied. Spectrophotometric, AAS and X-ray analyses revealed the purity of separated deposit to be 99.9%. An analytical application for preconcentration and separation of tellurium from its natural ores and alloys using the proposed electrolytic method is found to be satisfactory.</span