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san miguel de tucumán, argentina
$10 USD / hora
Atualmente, está 6:01 AM aqui
Ingressou em janeiro 22, 2014
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Victor Adrian J.

@vajimenez87

5,0 (1 avaliação)
1,5
1,5
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$10 USD / hora
Bandeira do(a) ARGENTINA
san miguel de tucumán, argentina
$10 USD / hora
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100%
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100%
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Python Programmer - Full Stack Developer

Computer Engineer. Recently, I obtained a PhD degree in Engineering, mention in Information Systems. I am currently a researcher and member of the research group GITIA. My interests are: Python, Data Engineering, Artificial Intelligence, Evolutionary Algorithms, and Web Development.

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5,0
$55,00 USD
He is the best person to reach out for efficient and timely work. New here but got loads of experience. Keep up the good work!
Engineering
Algorithm
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Artificial Intelligence
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Bandeira do(a) M P.
@nepalmp
há 3 meses

Experiência

Professor

Universidad Tecnológica Nacional
abr. 2014 - Atual
Teaching Genetic Algorithms and Heuristic Optimization in the Information Systems Engineering career.

Researcher

Grupo de Investigación en Tecnologías Informáticas Avanzadas
abr. 2013 - Atual
My current work focuses on intelligent data analysis applied to the management and optimization of energy.

Educação

PhD in Engineering, mention in Information Systems

Universidad Tecnológica Nacional, Argentina 2013 - 2019
(6 anos)

Computer Engineer

Universidad Nacional de Tucumán, Argentina 2005 - 2012
(7 anos)

Publicações

Optimal Allocation of Phase-Switching Devices for Dynamic Load Balancing

Electrical Engineering (Springer)
This paper presents a new method to determine the minimum number of devices that should be installed and find their optimal allocation. The method is based on Genetic Algorithms, requiring less than 50% of penetration of smart meters to fix the imbalance problem. A new control algorithm for the switching devices is also proposed. It was designed to reduce the load imbalance while maintaining the number of changes per customer to a minimum, reducing device degradation.

Neural Network for Estimating Daily Global Solar Radiation Using Temperature, Humidity and Pressure

Smart Grid and Renewable Energy
In this paper, we propose a method for estimating daily global solar radiation, combining empirical models and artificial neural networks. The model uses temperature, relative humidity and atmospheric pressure as the only climatic input variables. Also, this method is compared with linear regression to verify that the data have nonlinear components. The models are adjusted and validated using data from five meteorological stations in the province of Tucumán, Argentina.

Adjustment of Model Parameters to Estimate Distribution Transformers Remaining Lifespan

Smart Grid and Renewable Energy
In this work we propose a method to adjust the parameters of a thermal model using Genetic Algorithms, comparing the estimation values of top-oil temperature with measurements from 315 kVA distribution transformers, located in the province of Tucumán, Argentina. The results show that, despite limited data availability, the adjusted model is suitable to implement a transformer monitoring system.

An Artificial Immune Network for Distributed Demand-Side Management in Smart Grids

Information Sciences
In this work we present a Distributed Demand-Side Management system based on the Artificial Immune Network algorithm. It implements an intelligent, distributed and autonomous control of the customer’s Air Conditioning devices in order to meet the desired demand. The system is particularly adapted to tackle the Peak Load problem that appears in Tropical and Subtropical climates due to the use of thousands of these devices at the same time.

Phase identification and substation detection using data analysis on limited electricity consumption

Electric Power Systems Research
This paper presents a new method for phase identification and substation detection based on a correlation analysis of the variations of load consumption. Our method achieves accurate results with much fewer samples than the previous works, which is effective even if there are a low percentage of smart meters installed and missing data. We use statistical tests to avoid getting erroneous results from non-significant correlations values.

A New Data-Driven Method Based on Niching Genetic Algorithms for Phase and Substation Identification

Electric Power Systems Research
This work presents a new method for Phase Identification and Transformer Substation Detection for single-phase customers. Unlike previous approaches, we address the problem through a heuristic optimization, using an Evolutionary Algorithm based on Deterministic Crowding and correlation analysis on load measurements. The algorithm was designed to work with low penetration of smart meters and missing data, obtaining better results in shorter periods.

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