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Applications of nature-inspired computing in renewable energy systems
\"This book discusses the latest research on nature-inspired computing approaches applied to the design and development of renewable energy systems and provides new solutions to the renewable energy domain such as microgrids, wind power, and artificial neural networks\"-- Provided by publisher.
Book
Blockchain-based peer-to-peer transactions in energy systems
Blockchain is a nascent technology with the potential to disrupt the operational and business models of many industries. The enormous successes recorded by solutions underpinned by blockchain have attracted much attention to it. Beginning from the emergence of Bitcoin in the financial sector, blockchain has continued to impact other industries. It finds application in healthcare, supply chain, energy among other sectors. The technology has emerged as a viable technology for enabling decentralized peer-to-peer transactions. With the high adoption of renewable energy sources, the modern power system has become decentralized thereby paving the way for the integration of blockchain-based solutions. In the energy industry, blockchain can be employed to facilitate decentralised peer-to-peer energy transactions between power grid entities, energy storage sharing among others.
eBook
Introduction to renewable power systems and the environment with R
This textbook introduces the fundamentals of renewable electrical power systems examining their direct relationships with the environment. It covers conventional power systems and opportunities for increased efficiencies and friendlier environmental interactions. While presenting state-of-the-art technology, the author uses a practical interdisciplinary approach explaining electrical, thermodynamics, and environmental topics within every chapter. This approach allows students to feel comfortable moving across these disciplines. The added value are the examples of software programs using open source systems which serve as learning tools for the concepts and techniques described in the book-- Provided by publisher.
Book
Advances of Machine Learning in Clean Energy and the Transportation Industry
This book presents the latest research in the field of machine learning, discussing the real-world application problems associated with new innovative renewable energy methodologies as well as cutting edge technologies in the transport industry. The requirements and demands of problem solving have been increasing exponentially, and new artificial intelligence and machine learning technologies have reduced the scope of data coverage worldwide. Recent advances in data technology (DT) have contributed to reducing the gaps in the coverage of domains around the globe. Attention to clean energy in recent decades has been growing exponentially. This is mainly due to a decrease in the cost of both installed capacity of converters and a decrease in the cost of generated energy. Such successes were achieved thanks to the improvement of modern technologies for the production of converters, an increase in the efficiency of using incoming energy, optimization of the operation of converters and analysis of data obtained during the operation of systems with the possibility of planning production. The use of clean energy plays an important role in the transportation industry, where technologies are also being improved from year to year - the transportation industry is growing, and machinery and systems are becoming more autonomous and robotic, where it is no longer possible to do without complex intelligent computing, machine learning optimization, planning and working with large amounts of data. The book is a valuable reference work for researchers in the fields of renewable energy, computer science and engineering with a particular focus on machine learning and intelligent optimization as well as for postgraduates, managers, economists and decision makers, policy makers, government officials, industrialists and practicing scientists and engineers as well compassionate global decision makers. Topics include: Machine learning, Quantum Optimization, Modern Technology in Transport Industry, Innovative Technologies in Transport Education, Systems Based on Renewable Energy Conversion, Business Process Models and Applications in Renewable Energy, Clean Energy, and Climate Change.
eBook
Energías Renovables
La cantidad de energía que una sociedad consume y la eficiencia con la que la transforma y utiliza constituyen hoy en día criterios que permiten diagnosticar su grado de desarrollo. Se puede afirmar que el incremento en el nivel de desarrollo de una nación se encuentra asociado, en general, a un mayor consumo energético y a una mayor capacidad.
eBook
IoT, Machine Learning and Blockchain Technologies for Renewable Energy and Modern Hybrid Power Systems
This edited book comprises chapters that describe the IoT, machine learning, and blockchain technologies for renewable energy and modern hybrid power systems with simulation examples and case studies.
After reading this book, users will understand recent technologies such as IoT, machine learning techniques, and blockchain technologies and the application of these technologies to renewable energy resources and modern hybrid power systems through simulation examples and case studies.
This edited book comprises chapters that describe the IoT, machine learning, and blockchain technologies for renewable energy and modern hybrid power systems with simulation examples and case studies.
eBook
Prognostics and Health Management of Engineering Systems
This book introduces the methods for predicting the future behavior of a system's health and the remaining useful life to determine an appropriate maintenance schedule. The authors introduce the history, industrial applications, algorithms, and benefits and challenges of PHM (Prognostics and Health Management) to help readers understand this highly interdisciplinary engineering approach that incorporates sensing technologies, physics of failure, machine learning, modern statistics, and reliability engineering. It is ideal for beginners because it introduces various prognostics algorithms and explains their attributes, pros and cons in terms of model definition, model parameter estimation, and ability to handle noise and bias in data, allowing readers to select the appropriate methods for their fields of application.Among the many topics discussed in-depth are:- Prognostics tutorials using least-squares- Bayesian inference and parameter estimation- Physics-based prognostics algorithms including nonlinear least squares, Bayesian method, and particle filter- Data-driven prognostics algorithms including Gaussian process regression and neural network- Comparison of different prognostics algorithms The authors also present several applications of prognostics in practical engineering systems, including wear in a revolute joint, fatigue crack growth in a panel, prognostics using accelerated life test data, fatigue damage in bearings, and more. Prognostics tutorials with a Matlab code using simple examples are provided, along with a companion website that presents Matlab programs for different algorithms as well as measurement data. Each chapter contains a comprehensive set of exercise problems, some of which require Matlab programs, making this an ideal book for graduate students in mechanical, civil, aerospace, electrical, and industrial engineering and engineering mechanics, as well as researchers and maintenance engineers in the above fields.
eBook
Towards a standardization of biomethane potential tests
Production of biogas from different organic materials is a most interesting source of renewable energy. The biomethane potential (BMP) of these materials has to be determined to get insight in design parameters for anaerobic digesters. Although several norms and guidelines for BMP tests exist, inter-laboratory tests regularly still show high variability of BMPs for the same substrate. A workshop was held in June 2015, in Leysin, Switzerland, with over 40 attendees from 30 laboratories around the world, to agree on common solutions to the conundrum of inconsistent BMP test results. This paper presents the consensus of the intense roundtable discussions and cross-comparison of methodologies used in respective laboratories. Compulsory elements for the validation of BMP results were defined. They include the minimal number of replicates, the request to carry out blank and positive control assays, a criterion for the test duration, details on BMP calculation, and last but not least criteria for rejection of the BMP tests. Finally, recommendations on items that strongly influence the outcome of BMP tests such as inoculum characteristics, substrate preparation, test setup, and data analysis are presented to increase the probability of obtaining validated and reproducible results.
Journal Article
Analyzing the environmental Kuznets curve for the EU countries: the role of ecological footprint
A great majority of the environmental Kuznets curve (EKC) literature use CO
2
emissions to proxy for environmental degradation. However, this is an important shortage in application of the EKC concept because environmental degradation cannot be captured by CO
2
emissions only. By using a broader proxy, ecological footprint, this study aims to investigate the presence of environmental Kuznets curve hypothesis for the EU countries. The annual data from 1980 to 2013 is examined with second generation panel data methodologies which take into account the cross-sectional dependence among countries. The results show that there is U-shaped relationship between the real income and ecological footprint. In addition, non-renewable energy increases the environmental degradation while renewable energy and trade openness decrease the environmental degradation in the EU countries. Policy implications are further discussed.
Journal Article
Testing the EKC hypothesis for ten US states: an application of heterogeneous panel estimation method
This study aims to test the EKC (
Environmental Kuznets Curve
) hypothesis for the ten states, having the highest levels of carbon dioxide emissions in the USA, through the independent variables of real GDP, population, and renewable energy and fossil energy consumptions. To this aim, the panel estimation method with cross-sectional dependence is applied to data from 1980 to 2015. The empirical findings of the study indicate that the EKC (inverted U-shaped) hypothesis is valid only for Florida, Illinois, Michigan, New York, and Ohio. Interestingly, the negative impacts of fossil energy consumption on CO
2
emission levels in Texas are not detected statistically although this state is the leading
oil
-producing state. Furthermore, the positive impacts of renewable energy consumption in Florida, officially known as “
Sun
shine State”, are considerably low when compared with the other states.
Journal Article