Articles liés à Empowering Prosumers: A Thesis on Machine Learning...
Empowering Prosumers: A Thesis on Machine Learning for Optimized Renewable Energy Management in Smart Grids - Couverture souple
Résultats de recherche pour Empowering Prosumers: A Thesis on Machine Learning...
Image d'archives
Empowering Prosumers: A Thesis on Machine Learning for Optimized Renewable Energy Management in Smart Grids
impression à la demandeVendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The increasing demand for energy coupled with the environmental concerns associated with fossil fuels necessitates a shift towards renewable energy sources. Solar, wind, geothermal, and hydro power offer clean and sustainable alternatives but come with inherent challenges. Unlike traditional, dispatchable power plants, renewable energy generation is intermittent and dependent on weather conditions. This variability poses significant challenges for grid operators in maintaining a stable and reliable power supply.Smart grids emerge as a solution by introducing two-way communication capabilities and distributed intelligence. They enable real-time monitoring and control of energy flows within the grid, facilitating the integration of renewable energy sources. Prosumers, a new category of grid participants who not only consume but also generate electricity through rooftop solar panels or micro-wind turbines, play a crucial role in smart grids. Their active participation can significantly impact overall grid efficiency and stability. 88 pp. Englisch. N° de réf. du vendeur 9783384242259
Acheter neuf
EUR 26,10
Expédition à EUR 23
Expédition depuis Allemagne vers Etats-Unis
Expédition depuis Allemagne vers Etats-Unis
Quantité disponible : 2 disponible(s)
Image fournie par le vendeur
Empowering Prosumers: A Thesis on Machine Learning for Optimized Renewable Energy Management in Smart Grids
impression à la demandeVendeur : moluna, Greven, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. The increasing demand for energy coupled with the environmental concerns associated with fossil fuels necessitates a shift towards renewable energy sources. Solar, wind, geothermal, and hydro power offer clean and sustainable alternatives but come with inhe. N° de réf. du vendeur 1684852400
Acheter neuf
EUR 26,10
Expédition à EUR 48,99
Expédition depuis Allemagne vers Etats-Unis
Expédition depuis Allemagne vers Etats-Unis
Quantité disponible : Plus de 20 disponibles
Image fournie par le vendeur
Empowering Prosumers: A Thesis on Machine Learning for Optimized Renewable Energy Management in Smart Grids
Edité par
Tredition, Tredition Mai 2024, 2024
ISBN 10 : 3384242254
ISBN 13 : 9783384242259
Neuf
Taschenbuch
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -The increasing demand for energy coupled with the environmental concerns associated with fossil fuels necessitates a shift towards renewable energy sources. Solar, wind, geothermal, and hydro power offer clean and sustainable alternatives but come with inherent challenges. Unlike traditional, dispatchable power plants, renewable energy generation is intermittent and dependent on weather conditions. This variability poses significant challenges for grid operators in maintaining a stable and reliable power supply.Smart grids emerge as a solution by introducing two-way communication capabilities and distributed intelligence. They enable real-time monitoring and control of energy flows within the grid, facilitating the integration of renewable energy sources. Prosumers, a new category of grid participants who not only consume but also generate electricity through rooftop solar panels or micro-wind turbines, play a crucial role in smart grids. Their active participation can significantly impact overall grid efficiency and stability.tredition, Heinz-Beusen-Stieg 5, 22926 Ahrensburg 88 pp. Englisch. N° de réf. du vendeur 9783384242259
Acheter neuf
EUR 26,10
Expédition à EUR 60
Expédition depuis Allemagne vers Etats-Unis
Expédition depuis Allemagne vers Etats-Unis
Quantité disponible : 1 disponible(s)
Image fournie par le vendeur
Empowering Prosumers: A Thesis on Machine Learning for Optimized Renewable Energy Management in Smart Grids
impression à la demandeVendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The increasing demand for energy coupled with the environmental concerns associated with fossil fuels necessitates a shift towards renewable energy sources. Solar, wind, geothermal, and hydro power offer clean and sustainable alternatives but come with inherent challenges. Unlike traditional, dispatchable power plants, renewable energy generation is intermittent and dependent on weather conditions. This variability poses significant challenges for grid operators in maintaining a stable and reliable power supply.Smart grids emerge as a solution by introducing two-way communication capabilities and distributed intelligence. They enable real-time monitoring and control of energy flows within the grid, facilitating the integration of renewable energy sources. Prosumers, a new category of grid participants who not only consume but also generate electricity through rooftop solar panels or micro-wind turbines, play a crucial role in smart grids. Their active participation can significantly impact overall grid efficiency and stability. N° de réf. du vendeur 9783384242259
Acheter neuf
EUR 26,10
Expédition à EUR 60,82
Expédition depuis Allemagne vers Etats-Unis
Expédition depuis Allemagne vers Etats-Unis
Quantité disponible : 1 disponible(s)
Image fournie par le vendeur
Empowering Prosumers: A Thesis on Machine Learning for Optimized Renewable Energy Management in Smart Grids
Vendeur : preigu, Osnabrück, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. Empowering Prosumers: A Thesis on Machine Learning for Optimized Renewable Energy Management in Smart Grids | Smith | Taschenbuch | Englisch | 2024 | tredition | EAN 9783384242259 | Verantwortliche Person für die EU: tredition GmbH, Heinz-Beusen-Stieg 5, 22926 Ahrensburg, support[at]tredition[dot]com | Anbieter: preigu. N° de réf. du vendeur 129333453
Acheter neuf
EUR 26,10
Expédition à EUR 70
Expédition depuis Allemagne vers Etats-Unis
Expédition depuis Allemagne vers Etats-Unis
Quantité disponible : 5 disponible(s)
Image d'archives
Empowering Prosumers: A Thesis on Machine Learning for Optimized Renewable Energy Management in Smart Grids
Edité par
tredition, 2024
ISBN 10 : 3384242254
ISBN 13 : 9783384242259
Ancien ou d'occasion
Couverture souple
Vendeur : Buchpark, Trebbin, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Etat : Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | The increasing demand for energy coupled with the environmental concerns associated with fossil fuels necessitates a shift towards renewable energy sources. Solar, wind, geothermal, and hydro power offer clean and sustainable alternatives but come with inherent challenges. Unlike traditional, dispatchable power plants, renewable energy generation is intermittent and dependent on weather conditions. This variability poses significant challenges for grid operators in maintaining a stable and reliable power supply.Smart grids emerge as a solution by introducing two-way communication capabilities and distributed intelligence. They enable real-time monitoring and control of energy flows within the grid, facilitating the integration of renewable energy sources. Prosumers, a new category of grid participants who not only consume but also generate electricity through rooftop solar panels or micro-wind turbines, play a crucial role in smart grids. Their active participation can significantly impact overall grid efficiency and stability. N° de réf. du vendeur 43044196/1
Acheter D'occasion
EUR 18,42
Expédition à EUR 105
Expédition depuis Allemagne vers Etats-Unis
Expédition depuis Allemagne vers Etats-Unis
Quantité disponible : 1 disponible(s)