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Solar energy. --- Hydraulic energy --- Wind power generation --- Energies renouvelables --- Belgique --- Wallonie

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Astrophysics --- verbrandingsmotoren --- stralingenergiebenuttende machines --- Relation between energy and economics --- Internal combustion engines --- Énergie éolienne. --- Vents. --- Winds --- Énergie éolienne --- Wind power --- Facteur lié au site --- site factors --- Météorologie --- Meteorology --- Anémomètre --- Anemometers --- France --- Énergie éolienne. --- Wind power generation --- Wind velocity --- Loi de weibull

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697 --- Heating, ventilation and air conditioning of buildings --- 697 Heating, ventilation and air conditioning of buildings --- Architecture and climate --- Architecture and solar radiation --- Solar architecture --- Solar radiation and architecture --- Sun protection in architecture --- Solar radiation --- Architecture --- Climate and architecture --- Climatology --- Climatic factors --- Influence of climate --- Buildings. --- Climate --- Heat pumps. --- Heating. --- Power resources. --- Health. --- Health --- Climate. --- Air circulation --- Air intakes --- Comfort --- Consumption --- Experimentation --- Heat loss --- Heat recovery --- Humidity --- Microclimatology --- Shelters --- Solar power generation --- Temperature measurement --- Thermal conductivity --- Thermal environments --- Ventilation --- Wind power generation --- Windows

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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

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

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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.

Technology: general issues --- History of engineering & technology --- 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