Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Solar energy. --- Direct energy conversion --- Wind power generation

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Effective smart grid operation requires rapid decisions in a data-rich, but information-limited, environment. In this context, grid sensor data-streaming cannot provide the system operators with the necessary information to act on in the time frames necessary to minimize the impact of the disturbances. Even if there are fast models that can convert the data into information, the smart grid operator must deal with the challenge of not having a full understanding of the context of the information, and, therefore, the information content cannot be used with any high degree of confidence. To address this issue, data mining has been recognized as the most promising enabling technology for improving decision-making processes, providing the right information at the right moment to the right decision-maker. This Special Issue is focused on emerging methodologies for data mining in smart grids. In this area, it addresses many relevant topics, ranging from methods for uncertainty management, to advanced dispatching. This Special Issue not only focuses on methodological breakthroughs and roadmaps in implementing the methodology, but also presents the much-needed sharing of the best practices. Topics include, but are not limited to, the following:  Fuzziness in smart grids computing  Emerging techniques for renewable energy forecasting  Robust and proactive solution of optimal smart grids operation  Fuzzy-based smart grids monitoring and control frameworks  Granular computing for uncertainty management in smart grids  Self-organizing and decentralized paradigms for information processing

Information technology industries --- voltage regulation --- smart grid --- decentralized control architecture --- multi-agent systems --- t-SNE algorithm --- numerical weather prediction --- data preprocessing --- data visualization --- wind power generation --- partial discharge --- gas insulated switchgear --- case-based reasoning --- data matching --- variational autoencoder --- DSHW --- TBATS --- NN-AR --- time-series clustering --- decentral smart grid control (DSGC) --- interpretable and accurate DSGC-stability prediction --- data mining --- computational intelligence --- fuzzy rule-based classifiers --- multi-objective evolutionary optimization --- power systems resilience --- dynamic Bayesian network --- Markov model --- probabilistic modeling --- resilience models

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Effective smart grid operation requires rapid decisions in a data-rich, but information-limited, environment. In this context, grid sensor data-streaming cannot provide the system operators with the necessary information to act on in the time frames necessary to minimize the impact of the disturbances. Even if there are fast models that can convert the data into information, the smart grid operator must deal with the challenge of not having a full understanding of the context of the information, and, therefore, the information content cannot be used with any high degree of confidence. To address this issue, data mining has been recognized as the most promising enabling technology for improving decision-making processes, providing the right information at the right moment to the right decision-maker. This Special Issue is focused on emerging methodologies for data mining in smart grids. In this area, it addresses many relevant topics, ranging from methods for uncertainty management, to advanced dispatching. This Special Issue not only focuses on methodological breakthroughs and roadmaps in implementing the methodology, but also presents the much-needed sharing of the best practices. Topics include, but are not limited to, the following:  Fuzziness in smart grids computing  Emerging techniques for renewable energy forecasting  Robust and proactive solution of optimal smart grids operation  Fuzzy-based smart grids monitoring and control frameworks  Granular computing for uncertainty management in smart grids  Self-organizing and decentralized paradigms for information processing

Information technology industries --- voltage regulation --- smart grid --- decentralized control architecture --- multi-agent systems --- t-SNE algorithm --- numerical weather prediction --- data preprocessing --- data visualization --- wind power generation --- partial discharge --- gas insulated switchgear --- case-based reasoning --- data matching --- variational autoencoder --- DSHW --- TBATS --- NN-AR --- time-series clustering --- decentral smart grid control (DSGC) --- interpretable and accurate DSGC-stability prediction --- data mining --- computational intelligence --- fuzzy rule-based classifiers --- multi-objective evolutionary optimization --- power systems resilience --- dynamic Bayesian network --- Markov model --- probabilistic modeling --- resilience models

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Climate change mitigation and adaptation are key challenges of the 21st century. These challenges include global energy consumption and dependence on fossil fuels, which are addressed in global energy policies. About two-thirds of global greenhouse gas emissions are linked to the burning of fossil fuels used for heating, electricity, transport, and industry. Therefore, the world is looking for the most reliable, cost-effective, and environmentally friendly energy sources coupled with energy saving, which is a clean and low-cost solution to the growing demand for energy. As a clear example of this, cities are integrating renewable energies into their smart city plans. This book aims to advance the contribution of the use of renewable energies and energy saving in order to achieve a more sustainable world.

BIPV window --- WWR --- overall energy --- tilt angle --- visual comfort --- energy saving --- semi-arid --- wind power generation --- artificial neural networks --- chargeability factor --- reactive power capacity --- wind speed and demand curves --- energy management systems --- multi-objective function --- optimal set-points --- stochastic optimization --- wind farm operation --- expert survey --- renewable energy --- biogas --- biomethane --- biogas plant --- business model --- political support system --- building performance --- value co-creation --- value add --- maintenance management --- hospital buildings --- optimal power flow --- power flow --- optimization algorithms --- DC networks --- electrical energy --- optimization --- willingness to pay --- minigrids --- rural electrification --- Ghana --- hospital building maintenance --- critical success factor --- value-based practices --- importance-performance matrix analysis --- renewable energy sources --- non-conventional renewable energy sources --- RES --- NCRES --- electric power system --- information environment --- n/a

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

The book contains the research contributions belonging to the Special Issue "Numerical Simulation of Wind Turbines", published in 2020-2021. They consist of 15 original research papers and 1 editorial. Different topics are discussed, from innovative design solutions for large and small wind turbine to control, from advanced simulation techniques to noise prediction. The variety of methods used in the research contributions testifies the need for a holistic approach to the design and simulation of modern wind turbines and will be able to stimulate the interest of the wind energy community.

Technology: general issues --- large-scale wind turbine balde --- computational aeroacoustics --- sound source detection --- low Mach number turbulent flows --- NACA0012 airfoil --- fluid-structure interaction --- wind turbine --- atmospheric boundary layer --- composite materials --- gusts --- wind energy --- actuator line method --- wind turbine simulation --- regularization kernel --- small wind turbine (SWT) --- computational fluid dynamics (CFD) --- composites --- fluid-structure interaction (FSI) --- VAWT --- gurney flap --- CFD --- RBF --- power augmentation --- Darrieus --- turbulence --- experiments --- turbine wake --- turbine size --- large-eddy simulation --- actuator surface model --- wind turbine wake --- actuator disk model --- dynamic mode decomposition --- coherent structures --- wake meandering --- vertical axis wind turbine (VAWT) --- Savonius turbine --- deformable blades --- power coefficient --- blade load --- uncertainty quantification --- blade damage --- AEP --- winglet --- computational fluid dynamics (CFD), wind energy --- renewable energy --- rotor blade --- tip vortices --- aerodynamics --- ansys fluent --- savonius turbine --- icewind turbine --- static torque --- three-dimensional simulation --- Delayed DES --- H-Darrieus --- micro wind power generation --- wind turbine control --- load mitigation --- individual pitch control --- lifting line free vortex wake --- vortex methods --- pitch --- stall --- engineering codes