Ibero-American Congress Smart Cities 

About: Ibero-American Congress Smart Cities is an academic conference. The conference publishes majorly in the area(s): Photovoltaic system & Renewable energy. Over the lifetime, 60 publications have been published by the conference receiving 215 citations.

Cloud Computing for Smart Energy Management (CC-SEM Project)

Abstract: This paper describes the Cloud Computing for Smart Energy Management (CC-SEM) project, a research effort focused on building an integrated platform for smart monitoring, controlling, and planning energy consumption and generation in urban scenarios. The project integrates cutting-edge technologies (Big Data analysis, computational intelligence, Internet of Things, High Performance Computing and Cloud Computing), specific hardware for energy monitoring/controlling built within the project and explores their communication. The proposed platform considers the point of view of both citizens and administrators, providing a set of tools for controlling home devices (for end users), planning/simulating scenarios of energy generation (for energy companies and administrators), and shows some advances in communication infrastructure for transmitting the generated data.

Assessing the Environmental Impact of Car Restrictions Policies: Madrid Central Case

Abstract: With the increase of population living in urban areas, many transportation-related problems have grown very rapidly. Pollution causes many inhabitants health problems. A major concern for the International Community is pollution, which causes many inhabitants health problems. Accordingly, and under the risk of fines, countries are required to reduce noise and air pollutants. As a way to do so, road restrictions policies are applied in urban areas. Evaluating objectively the benefits of this type of measures is important to asses their real impact. In this work, we analyze the application of Madrid Central (MC), which is a set of road traffic limitation measures applied in the downtown of Madrid (Spain), by using smart city tools. According to our results, MC significantly reduces the nitrogen dioxide (\(NO_2\)) concentration in the air and the levels of noise in Madrid, while not arising any border effect.

Household Energy Disaggregation Based on Pattern Consumption Similarities

Abstract: Non-intrusive load monitoring allows breaking down the aggregated household consumption into a detailed consumption per appliance, without installing extra hardware, apart of a smart meter. Breakdown information is very useful for both users and electric companies, to provide an accurate characterization of energy consumption, avoid peaks, and elaborate special tariffs to reduce the cost of the electricity bill. This article presents an approach for energy consumption disaggregation in residential households, based on detecting similar patterns of recorded consumption from labeled datasets. The proposed algorithm is evaluated using four different instances of the problem, which use synthetically generated data based on real energy consumption. Each generated dataset normalize the consumption values of the appliances to create complex scenarios. The nilmtk framework is used to process the results and to perform a comparison with two built-in algorithms provided by the framework, based on combinatorial optimization and factorial hidden Markov model. The proposed algorithm was able to achieve accurate results, despite the presence of ambiguity between the consumption of different appliances or the difference of consumption between training appliances and test appliances.

Short term load forecasting of industrial electricity using machine learning

Abstract: 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 forecasting. Several models are developed to forecast the electricity load of the next hour using real data from an industrial pole in Spain. Feature selection and feature extraction are performed to reduce overfitting and therefore achieve better models, reducing the training time of the developed methods. The best of the implemented models is optimized using grid search strategies on hyperparameter space. Then, twenty four different instances of the optimal model are trained to forecast the next twenty four hours. Considering the computational complexity of the applied techniques, they are developed and evaluated on the computational platform of the National Supercomputing Center (Cluster-UY), Uruguay. Standard performance metrics are applied to evaluate the proposed models. The main results indicate that the best model based on ExtraTreesRegressor obtained has a mean absolute percentage error of 2.55% on day ahead hourly forecast which is a promising result.

Urban Data Analysis for the Public Transportation System of Montevideo, Uruguay

Abstract: This article presents a study of the public transportation system in Montevideo, Uruguay, following a data science approach. More than 20 million records from the Intelligent Transportation System (ITS) are analyzed in order to characterize mobility in the city. Several useful pieces of information are obtained through data analysis, related to tickets sold, patterns of smart card utilization, most used bus lines and stops, and socioeconomic insights about passengers behavior. Practical case studies are also presented: anomaly detection in space and time, and a study of potential safety hazards due to reckless driving. The work reported in this article constitutes one of the first steps towards using data from the ITS in Montevideo to understand mobility in the city.