A Review of Classification Problems and Algorithms in Renewable Energy Applications

Classification problems and their corresponding solving approaches constitute one of the fields of machine learning. The application of classification schemes in Renewable Energy (RE) has gained significant attention in the last few years, contributing to the deployment, management and optimization of RE systems. The main objective of this paper is to review the most important classification algorithms applied to RE problems, including both classical and novel algorithms. The paper also provides a comprehensive literature review and discussion on different classification techniques in specific RE problems, including wind speed/power prediction, fault diagnosis in RE systems, power quality disturbance classification and other applications in alternative RE systems. In this way, the paper describes classification techniques and metrics applied to RE problems, thus being useful both for researchers dealing with this kind of problem and for practitioners of the field

Recommended System for Optimizing Battery Energy Management with Floating Car Data

Atualmente, os veículos pesados que transportam mercadoria sensível à temperatura utilizam sistemas de refrigeração ruidosos e com elevado consumo de combustível. Para combater estas desvantagens, está a ser instalado um sistema capaz de recuperar e produzir energia elétrica durante as travagens e a partir de painéis fotovoltaicos. Esta energia é armazenada num conjunto de baterias para, posteriormente, alimentar o sistema frigorífico em modo elétrico. Adicionalmente, estão a ser recolhidos dados em tempo real sobre o comportamento do veículo e do sistema.Tendo em conta que toda a energia disponível durante a condução está condicionada por diversas variáveis de operação, é fulcral extrair conhecimento a partir da análise dos dados recolhidos, identificando padrões que possam otimizar a produção e gestão da energia preditivamente. Este processo de extração de conhecimento inclui seleção e avaliação dos dados a recolher, construção do modelo preditivo do sistema e estudo da sua aplicação. Assim sendo, num dado momento, tendo em conta não só as métricas recolhidas da viagem atual, mas também de dados históricos de um dado percurso, será possível ao sistema de gestão de energia instalado no camião decidir qual a melhor ação a tomar de forma a otimizar a energia produzida sem causar stress ao sistema.Nowadays, heavy vehicles that transport temperature-sensitive goods, generally use a fuel-needy dedicated diesel engine. Towards solving this problem, an energy management system (EMS) capable of producing energy on-board of the vehicle is being developed. This recovery is possible due to the regenerative braking (RB) functionality, which consists in converting kinetic energy to electrical energy during a slowdown. The recovered energy is then stored in a set of batteries that supplies the refrigeration system when needed, allowing it to run in electrical mode. Using data retrieved from the vehicle's operation and this management system, an opportunity towards intelligently using the regenerative braking functionality emerges. By introducing an intelligence layer on the energy management system, a decision on applying the RB functionality could be made based on the trip's energetic potential. This decision will optimize the battery usage and reduce the load and wear on the EMS components.In order to calculate the energetic potential of a certain route, an estimation of the road is needed. This document presents context information and different approaches towards this end. In the modeling approach recommended and implemented, a route is divided in several spatial segments and each segment is categorized among three pre-defined classes. A classification model is used to predict traffic historical data as input. By using this modeling approach based on travel times, information on traffic flow and intersection queues are incorporated and by calculating the most likely sequence of states, a estimation of the road ahead is made.Using the information of the modeled path, when the RB systems detects a situation where the functionality can be applied, a decision will be made by weighting the energetic potential of the path ahead and the energy need. When the algorithm sees fit, a higher torque may be applied to the generator, which will result in a larger quantity of energy recovered. Since this causes stress to the system, this functionality needs a robust intelligence layer

Ocean Remote Sensing with Synthetic Aperture Radar

The ocean covers approximately 71% of the Earth’s surface, 90% of the biosphere and contains 97% of Earth’s water. The Synthetic Aperture Radar (SAR) can image the ocean surface in all weather conditions and day or night. SAR remote sensing on ocean and coastal monitoring has become a research hotspot in geoscience and remote sensing. This book—Progress in SAR Oceanography—provides an update of the current state of the science on ocean remote sensing with SAR. Overall, the book presents a variety of marine applications, such as, oceanic surface and internal waves, wind, bathymetry, oil spill, coastline and intertidal zone classification, ship and other man-made objects’ detection, as well as remotely sensed data assimilation. The book is aimed at a wide audience, ranging from graduate students, university teachers and working scientists to policy makers and managers. Efforts have been made to highlight general principles as well as the state-of-the-art technologies in the field of SAR Oceanography

Industrial Applications: New Solutions for the New Era

This book reprints articles from the Special Issue "Industrial Applications: New Solutions for the New Age" published online in the open-access journal Machines (ISSN 2075-1702). This book consists of twelve published articles. This special edition belongs to the "Mechatronic and Intelligent Machines" section