Load forecasting in electrical distribution: grid of medium voltage

Trabalho apresentado no 7th Advanced Doctoral Conference on Computing, Electrical and Industrial Systems (DoCEIS’16), 11-13 abril de 2016, Caparica, PortugalThe importance of forecasting has become more evident with the appearance of the open electricity market and the restructuring of the national energy sector. This paper presents a new approach to load forecasting in the medium voltage distribution network in Portugal. The forecast horizon is short term, from 24 hours up to a week. The forecast method is based on the combined use of a regression model and artificial neural networks (ANN). The study was done with the time series of telemetry data of the DSO (EDP Distribution) and climatic records from IPMA (Portuguese Institute of Sea and Atmosphere), applied for the urban area of Évora - one of the first Smart Cities in Portugal. The performance of the proposed methodology is illustrated by graphical results and evaluated with statistical indicators. The error (MAPE) was lower than 5%, meaning that chosen methodology clearly validate the feasibility of the test

EXAFS study of lead-free relaxor ferroelectric BaTi(1-x)Zr(x)O3 at the Zr K-edge

Extended X-ray absorption fine structure (EXAFS) experiments at the Zr K-edge were carried out on perovskite relaxor ferroelectrics BaTi(1-x)Zr(x)O3 (BTZ) (x = 0.25, 0.30, 0.35), and on BaZrO3 for comparison. Structural information up to 4.5 A around the Zr atoms is obtained, revealing that the local structure differs notably from the average Pm-3m cubic structure deduced from X-ray diffraction. In particular, our results show that the distance between Zr atoms and their first oxygen neighbors is independent of the Zr substitution rate x and equal to that measured in BaZrO3, while the X-ray cubic cell parameter increases linearly with x. Furthermore, we show that the Zr atoms tend to segregate in Zr-rich regions. We propose that the relaxor behavior in BTZ is linked to random elastic fields generated by this particular chemical arrangement, rather than to random electric fields as is the case in most relaxors.Comment: 13 pages, 12 figures, 4 tables. Submitted to Phys. Rev.

GA-ANN Short-Term Electricity Load Forecasting

This paper presents a methodology for short-term load forecasting based on genetic algorithm feature selection and artificial neural network modeling. A feed forward artificial neural network is used to model the 24-h ahead load based on past consumption, weather and stock index data. A genetic algorithm is used in order to find the best subset of variables for modeling. Three data sets of different geographical locations, encompassing areas of different dimensions with distinct load profiles are used in order to evaluate the methodology. The developed approach was found to generate models achieving a minimum mean average percentage error under 2 %. The feature selection algorithm was able to significantly reduce the number of used features and increase the accuracy of the models

Corneal Transduction by Intra-Stromal Injection of AAV Vectors In Vivo in the Mouse and Ex Vivo in Human Explants

The cornea is a transparent, avascular tissue that acts as the major refractive surface of the eye. Corneal transparency, assured by the inner stroma, is vital for this role. Disruption in stromal transparency can occur in some inherited or acquired diseases. As a consequence, light entering the eye is blocked or distorted, leading to decreased visual acuity. Possible treatment for restoring transparency could be via viral-based gene therapy. The stroma is particularly amenable to this strategy due to its immunoprivileged nature and low turnover rate. We assayed the potential of AAV vectors to transduce keratocytes following intra-stromal injection in vivo in the mouse cornea and ex vivo in human explants. In murine and human corneas, we transduced the entire stroma using a single injection, preferentially targeted keratocytes and achieved long-term gene transfer (up to 17 months in vivo in mice). Of the serotypes tested, AAV2/8 was the most promising for gene transfer in both mouse and man. Furthermore, transgene expression could be transiently increased following aggression to the cornea

The Cell Adhesion Molecule “CAR” and Sialic Acid on Human Erythrocytes Influence Adenovirus In Vivo Biodistribution

Although it has been known for 50 years that adenoviruses (Ads) interact with erythrocytes ex vivo, the molecular and structural basis for this interaction, which has been serendipitously exploited for diagnostic tests, is unknown. In this study, we characterized the interaction between erythrocytes and unrelated Ad serotypes, human 5 (HAd5) and 37 (HAd37), and canine 2 (CAV-2). While these serotypes agglutinate human erythrocytes, they use different receptors, have different tropisms and/or infect different species. Using molecular, biochemical, structural and transgenic animal-based analyses, we found that the primary erythrocyte interaction domain for HAd37 is its sialic acid binding site, while CAV-2 binding depends on at least three factors: electrostatic interactions, sialic acid binding and, unexpectedly, binding to the coxsackievirus and adenovirus receptor (CAR) on human erythrocytes. We show that the presence of CAR on erythrocytes leads to prolonged in vivo blood half-life and significantly reduced liver infection when a CAR-tropic Ad is injected intravenously. This study provides i) a molecular and structural rationale for Ad–erythrocyte interactions, ii) a basis to improve vector-mediated gene transfer and iii) a mechanism that may explain the biodistribution and pathogenic inconsistencies found between human and animal models