Rodrigo Porteiro

• Researcher at Universidad de la República de Uruguay

This article presents a study to characterize the electricity consumption in residential buildings in Uruguay. Understanding residential electricity consumption is a relevant concept to identify factors that influence electricity usage, and allows developing specific and custom energy efficiency policies. The study focuses on two home appliances: a...

This article presents an approach applying computational intelligence for detecting the use of air conditioners in households. The main objective is determining the intensive use of air conditioning with a high level of confidence. Unsupervised (-means) and supervised (Artificial Neural Networks) approaches are developed for classifying consumers f...

This article presents an unsupervised machine learning approach for the problem of detecting use of air conditioning in households, during the summer. This is a relevant problem in the context of the modern smart grid approach under the paradigm of smart cities. The proposed methodology applies data analysis, a thermal inertial model for estimating...

En el presente trabajo se propone el uso de redes neuronales artificiales para la resolución del problema de flujos de carga. El estudio del flujo de carga de la red eléctrica constituye una herramienta fundamental para la operación y la planificación de un sistema eléctrico. En términos matemáticos el problema de flujos de carga se resuelve a trav...

Featured Application The methodology described in this article is applicable to design proper management strategies for demand response in smart electricity grids to fairly select water heaters to intervene while guaranteeing the lower discomfort of users. Abstract Demand-response techniques are crucial for providing a proper quality of service un...

Energy demand management is an important technique for smart grids, under the paradigm of smart cities. Direct control of devices is useful for demand management, but it has the disadvantage of affecting user comfort. This article presents an approach for defining an index to estimate the discomfort associated with an active demand management consi...

This article presents the application of parallel computing for building different computational intelligence models applied to the forecast of the hourly electricity demand of the following day. The short-term forecast of electricity demand is a crucial problem to define the dispatch of generators. In turn, it is necessary to define demand respons...

This article presents electricity demand forecasting models for industrial and residential facilities, developed using ensemble machine learning strategies. Short term electricity demand forecasting is beneficial for both consumers and suppliers, as it allows improving energy efficiency policies and the rational use of resources. Computational inte...

Forecasting the day-ahead electricity load is beneficial for both suppliers and consumers. The reduction of electricity waste and the rational dispatch of electric generator units can be significantly improved with accurate load forecasts. This article is focused on studying and developing computational intelligence techniques for electricity load...

During the day of October 7-8, the congress has invited experts from the sector of Smart Cities to organize a sectoral debate. This debate will be composed of prestigious companies in the sector, Public Administration, as well as specialized consultants. The aim is to give a business point of view around Smart Cities. CYTED is the Ibero-American Pr...

In nowadays energy markets, suppliers are encouraged to model the electricity consumption behavior of their customers in order to improve the quality of service and provide better products with lower investment and operating costs. New load models to support power system are required to mitigate scalability issues, especially considering the increa...