Arturo Peralta

Este trabajo analiza métodos y algoritmos de predicción del comportamiento de consumo eléctrico basados en redes neuronales, usando datos obtenidos de la infraestructura de medición avanzada (AMI) de una institución educativa. También, se ha realizado un contraste entre el uso de redes neuronales convencionales (ANN), redes neuronales basadas en wavelets (WNN) y los polinomios potenciales de grado uno (P1P). Se analiza la correlación de cada método de predicción, así como el comportamiento del error cuadrático medio (MSE) para finalmente establecer si existe un desbalance en el coste computacional a través del análisis de Big-O y el tiempo de ejecución. Los resultados cuantitativos del error MSE están por debajo del 0,05% para predicciones con ANN y usan un alto costo computacional. Para P1P se presentan errores alrededor del 1,2% mostrando como método de predicción de bajo consumo computacional pero aplicable de forma principal para un análisis a corto plazo. Este trabajo se da en respuesta a la necesidad de establecer una plataforma que permita aprovechar la estructura de medición inteligente, a través de la predicción de perfil de consumo eléctrico con el objetivo de elaborar una planificación de mantenimiento y gestión de la demanda eléctrica para reducir costos de operación desde el consumidor final hasta el gestor de la distribución de energía eléctrica. Para el análisis de las proyecciones sobre el perfil de carga eléctrica se consideran las características estadísticas del consumo para seleccionar los algoritmos de predicción según la cantidad de días a proyectar, usando los datos de cualquiera de los medidores inteligentes, que pueden ser monitoreados en una red eléctrica orientada a las Smart Grids.

Nowadays, growth in demand for bandwidth, due to new and future applications being implemented, for services provided from smart grids (SG), smart cities (SC) and internet of things (IoT), it has drawn attention of scientific community, on issues related to planning, and optimization of communication infrastructure resources, in addition is necessary comply with requirements such as scalability, coverage, security, flexibility, availability, delay and security. Another important point is how to find and analyze possible solutions that seek to minimize the costs involved by capital expenditure (CAPEX) and operational expenditure (OPEX), but where it is possible to measure the uncertainty coming from stochastic projections, in order to obtain the maximum benefit expected to give access to users Who benefits from the services provided by SG, SC and IoT, on the other hand, we must look for communications architectures that generate optimum topologies to meet demanded requirements and at the same time save energy, possible alternatives highlight the use of hybrid networks of optical fiber links combined with wireless links (Fiber-Wireless, FiWi). This chapter seeks to provide planning alternatives to network segments linking universal data aggregation point (UDAP) with base stations (BS), this segment joins wide area network (WAN) with metropolitan area network (MAN).

The large demand of bandwidth due to new and future applications from services being implemented such as smart grid (SG), smart cities (SC), and the Internet of Things has triggered a focus of interest from the scientific community on issues related to the planning and scalability of the communications infrastructure. In this paper, we present three new and important contributions regarding the deployment of FiWi networks that support users who benefit from the services provided by SG and SC. We first propose a planning model, and then formulate a mixed linear programming optimization model using multistage stochastic programming, and finally propose an algorithm, called MOA–FiWi, which has the capacity to handle medium and large instances of the problem and give feasible solutions for the optimization process, where scalability indices between 85%–95% are reached with respect to the projected stochastic values. The proposed model considers the scalability and capital and operating expense, as well as the uncertainty management in the different stages of time–space through multistage stochastic programming. The proposed model presents flexibility in decision making as the time stages progress. This allows for the planning of green fields, as well as the updating of networks that already have communication infrastructure.

In this paper, we consider the problem of enhancing the filtering capacity of single standard beamformers for real base-band signals. Unfortunately, the standard smart arrays are not able to effectively minimize the effects of the complex noise in the array. In order to solve this problem, we analyze a parallel adaptive beamforming system based on complex nonlinear adaptive filters. In this design, the smart array is used as a widely linear (WL) system in which the second order statistical information of the signals is exploited by using the covariance and pseudo-covariance matrices. The simulations results show this architecture has a lead filtering capability as well as an outstanding generation of the radiation patterns. Furthermore, this parallel hybrid array showed to have a better filtering capacity than the standard arrays LMS, NLMS and RLS. General equations for the optimal weight vector are derived for this augmented complex (AC) smart array or widely linear beamforming.

In this paper, we propose a hybrid wireless mesh network infrastructure which connects the smart meters of each consumer with the data aggregation points (DAP). We suppose a set of smart meters that need to send information, and receive information from a central office on electrical enterprises, employing the meter data management system (MDMS), and so forming the advanced metering infrastructure (AMI) stage of smart grids. We consider a multi-hop system, where information is routed through several nodes which act as DAP. Wireless mesh networks are known to extend coverage and increase deployment efficiency, so they could be an alternative for the connection between Home Area Network (HAN) and the Neighborhood Area Network (NAN). However, the NAN data must be send through wider area cabled networks to Metropolitan Area Network (MAN), and based on the WDM-PON architecture. We consider a novel planning model which simultaneously solves both architectures and analyses the overall performance based on average packet delay.

This paper presents an optimal deployment model on Universal Data Aggregation Points (UDAP); therefore, ideal for receive information of Vehicular Ad-Hoc Networks (VANET), and for the Advanced Metering Infrastructure (AMI), based on FiWi networks. The optimal dimensioning and scalability of fiber optic networks, and the candidates sites for each UDAP are considered in this work, so that the cost for infrastructure of communications will be acceptable, but considering reliability, efficiency, and with dynamic maintenance for long term. On the other hand, is possible to agglutinate the data of VANET and AMI using wireless networks in each UDAP; furthermore, we propose generate a link between UDAP with the minimum deployment of fiber optic links througth an ILP model and an algorithm named Clustering Location Deployment Allocation Cost-CLDAC. For this work, each UDAP considered different technologies of communications such as: WiFi, IEEE 802.15.4g, WiMax. Thus, this situation enables additional services to VANET and AMI are considering in the future, due to the exponential increase of wireless sensors used in different applications.

En este documento, presentamos el modelado de un canal de fibra óptica mediante la resolución de la Ecuación No Lineal de Schrödinger (NLSE). Se presentan las dos formas de solución para la NLSE: la forma analítica y la forma numérica empleando el método SSF (Split–Step Fourier Transform). En la simulación se consideran efectos lineales como la dispersión cromática y los efectos no lineales. Uno de los efectos no lineal es el efecto Kerr, del que se derivan los efectos de auto modulación fase (Self Phase Modulation, SPM) y modulación de fase cruzada (Cross Phase Modulation, XPM). Los métodos de solución son empleados para simular y visualizar los efectos de propagación a través de la fibra óptica. Se analizan los efectos de propagación para un escenario de red de acceso óptica con fibra mono–modo estándar (Single Mode Fiber, SMF), con longitudes de fibra de 20 y 40 km y tasas de bits entre 1,25 y 100 Gbps. De otro lado, son presentados los fenómenos no lineales como dispersión estimulada de Raman (Stimulated Raman Scattering, SRS) y dispersión estimulada de Brillouin (Stimulated Brillouin Scattering, SBS). Se presentan las ecuaciones para modelar SRS. Se presentan resultados de simulación de la amplificación Raman en un escenario seleccionado.

With the increasing amount of information that is demanding by fixed and mobile users through the multiservice networks, the deployment of the optical segment in the access network is a critical issue, with impact in the cost and performance of the overall network. While TDM-Passive optical network (PON) is standardized, hybrid WDM/TDM-PON is now clearly seen as the next generation trend in the multiservice access. In this work we propose a dimensioning scheme for the deployment of multiservice hybrid WDM/TDM-PONs, using a heuristic method.

Resumen El mundo actual requiere cada vez más el estar conectados con la sociedad de la Información y el Conocimiento, esto demanda que en los hogares, lugares de turismo, trabajos, universidades, etcétera, se cuente con una conexión a Internet de Alta Velocidad, que además sea segura y confiable. En este documento se presenta las características más importantes de la tecnología BPL (Broadband Power Line) o PLC (Power Line Comunication), la cual permite brindar el servicio de Internet de Alta Velocidad, mediante la utilización de las Redes de Distribución de Energía Eléctrica, tanto a nivel de Media como de Baja Tensión, donde se debe considerar los equipos necesarios para realizar la conexión y de esta manera poder enviar datos sobre las líneas de energía eléctrica.