Integrated scalable system for smart energy management

The planet's reserves are encountering vital challenges and suffer inequitable consumption. The outcomes of the prostration of natural reserves have started affecting every single organism on the globe. Energy is a critical key factor in this aspect because a considerable part of the destruction is triggered by utilising the planet reserves to produce power in diverse forms. The increasing environmental awareness in humans' minds, and the rapid development of smart concepts, home automation technologies in both hardware and software fields, played an essential role in speeding up the progress to apply smart energy management which is needed to revert the situation to its appropriate track by focusing on two main divisions: firstly, producing clean and renewable energy and secondly, reducing the loss of the total generated energy. This research will concentrate on the second approach by proposing, implementing and evaluating a contemporary integrated, scalable, smart energy management framework that assists in reducing the energy consumption in the household sector, covering a range of single households till huge communities and big organisations with thousands of appliances. A number of correspondent strategies and policies which utilise a set of observed and predicted system entities are applied to keep meetings the most relevant quality attributes such as integrability, scalability, interoperability and availability. IoT concepts are applied in this context to connect conventional household appliances to a farm of microservices that implement predictive analytics techniques to reduce energy consumption by applying two main strategies; appliance substitution based on the energy consumption and creating automatic schedules to run appliances based on predictions. A case study is presented on two sample appliances within the household to illustrate the framework validity and deliver percentage figures of the saved energy. Additionally, the framework offers a number of possibilities to provide relevant third parties such as local energy providers, apparatuses' manufacturers, or pertinent government offices with various appliances’ operational behaviours under real-life conditions

Program and Abstracts Celebration of Student Scholarship, 2013

Program and Abstracts from the Celebration of Student Scholarship on April 24, 2013

Program and Abstracts Celebration of Student Scholarship, 2013

Program and Abstracts from the Celebration of Student Scholarship on April 24, 2013

Program and Abstracts Celebration of Student Scholarship, 2014

Program and Abstracts from the Celebration of Student Scholarship on April 23, 2014

An Integrated Method for Information and Communication Technology (ICT) Supported Energy Efficiency Evaluation and Optimization in Manufacturing: Knowledge-based Approach and Energy Performance Indicators (EnPI) to Support Evaluation and Optimization of Energy Efficiency

This thesis develops a holistic evaluation and optimization of energy efficiency in manufacturing. The innovation of this thesis consists in the integrated method applying an expressive and adaptive ontology knowledge base capable of learning, and sector-independent, straightforward energy performance indicator for evaluating different processes and units within a company. This thesis also develops a hyper-heuristics-based energy-optimized and flexible production scheduling

Renewable Energies for Sustainable Development

In the current scenario in which climate change dominates our lives and in which we all need to combat and drastically reduce the emission of greenhouse gases, renewable energies play key roles as present and future energy sources. Renewable energies vary across a wide range, and therefore, there are related studies for each type of energy. This Special Issue is composed of studies integrating the latest research innovations and knowledge focused on all types of renewable energy: onshore and offshore wind, photovoltaic, solar, biomass, geothermal, waves, tides, hydro, etc. Authors were invited submit review and research papers focused on energy resource estimation, all types of TRL converters, civil infrastructure, electrical connection, environmental studies, licensing and development of facilities, construction, operation and maintenance, mechanical and structural analysis, new materials for these facilities, etc. Analyses of a combination of several renewable energies as well as storage systems to progress the development of these sustainable energies were welcomed

Advanced Process Monitoring for Industry 4.0

This book reports recent advances on Process Monitoring (PM) to cope with the many challenges raised by the new production systems, sensors and “extreme data” conditions that emerged with Industry 4.0. Concepts such as digital-twins and deep learning are brought to the PM arena, pushing forward the capabilities of existing methodologies to handle more complex scenarios. The evolution of classical paradigms such as Latent Variable modeling, Six Sigma and FMEA are also covered. Applications span a wide range of domains such as microelectronics, semiconductors, chemicals, materials, agriculture, as well as the monitoring of rotating equipment, combustion systems and membrane separation processes

Personality Identification from Social Media Using Deep Learning: A Review

Social media helps in sharing of ideas and information among people scattered around the world and thus helps in creating communities, groups, and virtual networks. Identification of personality is significant in many types of applications such as in detecting the mental state or character of a person, predicting job satisfaction, professional and personal relationship success, in recommendation systems. Personality is also an important factor to determine individual variation in thoughts, feelings, and conduct systems. According to the survey of Global social media research in 2018, approximately 3.196 billion social media users are in worldwide. The numbers are estimated to grow rapidly further with the use of mobile smart devices and advancement in technology. Support vector machine (SVM), Naive Bayes (NB), Multilayer perceptron neural network, and convolutional neural network (CNN) are some of the machine learning techniques used for personality identification in the literature review. This paper presents various studies conducted in identifying the personality of social media users with the help of machine learning approaches and the recent studies that targeted to predict the personality of online social media (OSM) users are reviewed