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AI and IOT in renewable energy
~
Shaw, Rabindra Nath.
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AI and IOT in renewable energy
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
AI and IOT in renewable energy/ edited by Rabindra Nath Shaw ... [et al.].
其他作者:
Shaw, Rabindra Nath.
出版者:
Singapore :Springer Singapore : : 2021.,
面頁冊數:
xii, 109 p. :ill., digital ;24 cm.
內容註:
A Day Ahead Power Output Forecasting of three PV Systems using Regression, Machine Learning and Deep Learning Techniques -- Internet of Things and Internet of Drones in the Renewable Energy Infrastructure Towards Energy Optimization -- Reinforcement Learning Algorithm to Reduce Energy Consumption in Electric Vehicles -- Spotted Hyena Optimization (SHO) Algorithm based Novel Control Approach for Buck DC-DC Converter Fed PMBLDC Motor -- Simulation and Performance Analysis of Standalone Photovoltaic System with Boost Converter Under Irradiation and Temperature -- Analysis of Variation in Locational Marginal Pricing under Influence of Stochastic Wind Generation -- Optimal Integration of Plug-In Electric Vehicles Within a Distribution Network Using Genetic Algorithm -- Frequency Control of 5kW Self-Excited Induction Generator Using Gravitational Search Algorithm and Genetic Algorithm -- Cloud Based Real-time Vibration and Temperature Monitoring System for Wind Turbine -- Smart Solar-Powered Smart Agricultural Monitoring System Using Internet of Things Devices.
Contained By:
Springer Nature eBook
標題:
Artificial intelligence. -
電子資源:
https://doi.org/10.1007/978-981-16-1011-0
ISBN:
9789811610110
AI and IOT in renewable energy
AI and IOT in renewable energy
[electronic resource] /edited by Rabindra Nath Shaw ... [et al.]. - Singapore :Springer Singapore :2021. - xii, 109 p. :ill., digital ;24 cm. - Studies in infrastructure and control,2730-6453. - Studies in infrastructure and control..
A Day Ahead Power Output Forecasting of three PV Systems using Regression, Machine Learning and Deep Learning Techniques -- Internet of Things and Internet of Drones in the Renewable Energy Infrastructure Towards Energy Optimization -- Reinforcement Learning Algorithm to Reduce Energy Consumption in Electric Vehicles -- Spotted Hyena Optimization (SHO) Algorithm based Novel Control Approach for Buck DC-DC Converter Fed PMBLDC Motor -- Simulation and Performance Analysis of Standalone Photovoltaic System with Boost Converter Under Irradiation and Temperature -- Analysis of Variation in Locational Marginal Pricing under Influence of Stochastic Wind Generation -- Optimal Integration of Plug-In Electric Vehicles Within a Distribution Network Using Genetic Algorithm -- Frequency Control of 5kW Self-Excited Induction Generator Using Gravitational Search Algorithm and Genetic Algorithm -- Cloud Based Real-time Vibration and Temperature Monitoring System for Wind Turbine -- Smart Solar-Powered Smart Agricultural Monitoring System Using Internet of Things Devices.
This book presents the latest research on applications of artificial intelligence and the Internet of Things in renewable energy systems. Advanced renewable energy systems must necessarily involve the latest technology like artificial intelligence and Internet of Things to develop low cost, smart and efficient solutions. Intelligence allows the system to optimize the power, thereby making it a power efficient system; whereas, Internet of Things makes the system independent of wire and flexibility in operation. As a result, intelligent and IOT paradigms are finding increasing applications in the study of renewable energy systems. This book presents advanced applications of artificial intelligence and the internet of things in renewable energy systems development. It covers such topics as solar energy systems, electric vehicles etc. In all these areas applications of artificial intelligence methods such as artificial neural networks, genetic algorithms, fuzzy logic and a combination of the above, called hybrid systems, are included. The book is intended for a wide audience ranging from the undergraduate level up to the research academic and industrial communities engaged in the study and performance prediction of renewable energy systems.
ISBN: 9789811610110
Standard No.: 10.1007/978-981-16-1011-0doiSubjects--Topical Terms:
516317
Artificial intelligence.
LC Class. No.: Q335 / .A5 2021
Dewey Class. No.: 006.3
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A Day Ahead Power Output Forecasting of three PV Systems using Regression, Machine Learning and Deep Learning Techniques -- Internet of Things and Internet of Drones in the Renewable Energy Infrastructure Towards Energy Optimization -- Reinforcement Learning Algorithm to Reduce Energy Consumption in Electric Vehicles -- Spotted Hyena Optimization (SHO) Algorithm based Novel Control Approach for Buck DC-DC Converter Fed PMBLDC Motor -- Simulation and Performance Analysis of Standalone Photovoltaic System with Boost Converter Under Irradiation and Temperature -- Analysis of Variation in Locational Marginal Pricing under Influence of Stochastic Wind Generation -- Optimal Integration of Plug-In Electric Vehicles Within a Distribution Network Using Genetic Algorithm -- Frequency Control of 5kW Self-Excited Induction Generator Using Gravitational Search Algorithm and Genetic Algorithm -- Cloud Based Real-time Vibration and Temperature Monitoring System for Wind Turbine -- Smart Solar-Powered Smart Agricultural Monitoring System Using Internet of Things Devices.
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This book presents the latest research on applications of artificial intelligence and the Internet of Things in renewable energy systems. Advanced renewable energy systems must necessarily involve the latest technology like artificial intelligence and Internet of Things to develop low cost, smart and efficient solutions. Intelligence allows the system to optimize the power, thereby making it a power efficient system; whereas, Internet of Things makes the system independent of wire and flexibility in operation. As a result, intelligent and IOT paradigms are finding increasing applications in the study of renewable energy systems. This book presents advanced applications of artificial intelligence and the internet of things in renewable energy systems development. It covers such topics as solar energy systems, electric vehicles etc. In all these areas applications of artificial intelligence methods such as artificial neural networks, genetic algorithms, fuzzy logic and a combination of the above, called hybrid systems, are included. The book is intended for a wide audience ranging from the undergraduate level up to the research academic and industrial communities engaged in the study and performance prediction of renewable energy systems.
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