Machine Learning techniques for energy consumption forecasting in Smart Cities scenarios

Over the past few years, the number of sensors spread across cities has significantly increased. This led to an exponential growth in data volume, which can only be treated with Big Data techniques. Having such a large amount of generated data, turns possible to apply machine learning techniques more accurately, with the goal of making data predictions over time, finding anomalies, performing classification, among other tasks. This article aims to show the application of machine learning techniques, using a variant of the Recurrent Neural Networks, the Long Short-Term Memory (LSTM), in order to predict city\u27s energy consumptions for the near future. This forecast will support municipal entities decisions, helping them to improve the managing of energy consumptions and budgets

Modelos de Machine Learning na gestão de consumos de energia

Ao longo destes últimos anos, a quantidade de sensores espalhados pelas cidades tem aumentado significativamente, o que, por consequência, leva a um incremento no volume de dados, originando o Big Data. Muitos desses sensores foram colocados em candeeiros e em contadores de energia elétrica, permitindo, tanto ao utilizador, como ao município, verificar os respetivos consumos, muitas vezes em tempo real, fazendo da cidade uma cidade inteligente. Com essa grande quantidade de dados gerada, seria possível aplicar técnicas de machine learning, com o objetivo de fazer previsões de dados no tempo, encontrar anomalias, efetuar algumas estatísticas e retirar informações úteis. Isto permite que o município consiga ir em conta aos seus objetivos, tornando a cidade numa cidade sustentável, melhorando, assim, a qualidade de vida dos seus cidadãos. Em suma, com este trabalho pretende-se criar modelos de machine learning, utilizando bibliotecas de código aberto (e. g. TensorFlow, Keras) sobre dados reais de energia elétrica de uma cidade, com o objetivo de prever os consumos para os próximos tempos, de forma a que o município tenha uma melhor tomada de decisão. Aliada a esta previsão, pretende-se, também, criar uma REST API que disponibilize essas previsões numa ferramenta de business intelligence, para que o município possa ter uma melhor visão das mesmas. No geral, com a previsão dos consumos, será possível resolver o problema, que é mútuo não só a municípios, mas também a outras entidades, da verificação da gestão do orçamento em relação à energia e indo em conta às suas expectativas.ABSTRACT: Over the past few years, the number of sensors spread across cities has significantly increased, which, consequently, leads to an increase in data volume, giving rise to Big Data. Many of these sensors were placed in street lamps and electricity meters, allowing both the user and municipal entities to check their consumptions, often in real time, making the city a smart city. With this large amount of generated data, it would be possible to apply machine learning techniques, with the objective of making data predictions over time, finding anomalies, performing some statistics and finding useful information. This allows the municipal entity to reach its objectives, making the city a sustainable city, improving the quality of its citizens life. In short, this work intends to create machine learning models, using open source libraries (e. g. TensorFlow, Keras) on real electric energy data from a city, in order to predict consumption data for the next times, so that the municipality has better decision-making. Allied to this forecast, it is also intended to create a REST API that makes these forecasts available in a business intelligence tool, so that the municipality can have a better view of them. In general, with the consumption forecasts, it will be possible to solve the problem, which is mutual not only for municipal entities, but also for other entities, of verifying the management of the budget in relation to energy and over-reaching their expectations

Neural aspects in soccer kicks: a case study about kinemetrics techniques adaptation for assessment and cognitive training

In sports, Kinemetric procedures are widely used for athletic gesture analysis, but are rarely employed considering cognitive aspects of the movement. The present work consists a case study that proposes the implementation of a kinemetric method that can also consider higher order cognitive processes during soccer dead-ball kicks, such as the calculation of the spatial dimension, motors engrams establishment, movement trends and the precision variability . The images obtained of motor variation around the preestablished target were analysed with Dartfish Connect software and with statistical procedures. In results, we considered the spatial distribution of the kicks around the target and the identification of spatial biases in motor programs. The findings were shared with athletes and coaches to provide greater awareness of the related details to individual performance. In conclusion, we saw that the methodological adjustment for kinemetric assessment of kick cognition can be useful for the coaches choose the most efficient soccer kicker and for identifying better strategies to stimuli personal training

A vegetação arbórea do Parque Estadual do Morro do Diabo, município de Teodoro Sampaio, Estado de São Paulo

Fez-se o levantamento florístico da vegetação arbórea da floresta mesófila do Parque Estadual do Morro do Diabo, Município de Teodoro Sampaio, Estado de São Paulo (22º30'S, 52º20'W) pelos métodos de quadrantes e parcelas, incluindo as árvores com diâmetro à altura do peito igual ou superior a 10 cm. A utilização dos dois métodos deu-se em função das características fisionômicas, solo e drenagem dos locais amostrados. O método de quadrante envolveu 462 pontos com intervalos de 30 m., e o método de parcelas ca. 26.900 m² (ca. 2,7 ha.). Os parâmetros fitossociológicos serão abordados em futuros trabalhos. Os dados florísticos subsidiaram o reassentamento dos grupos faunísticos, em especial o Leontopithecus chrysopygus Mikan, 1823 (mico-leão-preto), dentro das áreas remanescentes. O "check list" inclui também algumas espécies coletadas aleatoriamente em outras áreas do Parque. Constatou-se nas áreas de amostragem e adjacentes 113 espécies, 95 gêneros e 42 famílias, das quais 6 contribuíram com 56% das espécies levantadas. As famílias mais representativas no Parque, envolvendo todos os locais de coleta são: Leguminosae (13 Faboideae, 6 Caesalpinioideae e 6 Mimosoideae), Rutaceae 11, Meliaceae 8, Lauraceae 7, Euphorbiaceae 7 e Myrtaceae 6. A listagem das espécies revela que o Parque Estadual do Morro do Diabo apresenta uma vegetação de grande heterogeneidade florística.<br>In a mesophyll forest at the State Park of "Morro do Diabo", in the municipality of Teodoro Sampaio, State of São Paulo (22º30'S, 52º20'W) the floristic composition was surveyed. The point centered quarter and quadrant methods were used to survey trees with a diameter equal or greater than 10 cm at breast height. These methods were used as a function of soil features, drainage and physionomical characteristics of the place where the samples were taken. The quarter method sampled 462 points with intervals of 30 m, and the other one had an area of approximately 26.900 m². The phytosociological parameters will be reported in a future paper. The floristic data were important to the resettlement of launistic groups, particulary Leontopithecus chrysopygus Mikan, 1823 ("mico-leão-preto") within remaining areas. The check list also included some species colected in other areas of the Park. There were verified in the sampling areas and surrondings, 113 species, 95 genera and 42 families of trees. Six (6) families contributed with 56% of the total num ber of species. The most representative families in the Park were the following: Leguminosae 25 (13 Faboideae, 6 Caesalpinioideae and 6 Mimosoideae), Rutaceae 11, Meliaceae 8, Lauraceae 7, Euphorbiaceae 7 and Myrtaceae 6. The checklist showed that the State Park of "Morro do Diabo" has a great floristic heterogeneity