Reprint

Smart IoT System for Renewable Energy Resource

Edited by
November 2023
282 pages
  • ISBN978-3-0365-9182-7 (Hardback)
  • ISBN978-3-0365-9183-4 (PDF)

This book is a reprint of the Special Issue Smart IoT System for Renewable Energy Resource that was published in

Chemistry & Materials Science
Engineering
Environmental & Earth Sciences
Summary

Renewable energy resources are used as distributed generation (DG) units and installed near to where the energy is converted and consumed. Further, the integration of renewable energy sources at home is very important. IoT helps smart grids to support various network functions throughout the generation, distribution, and consumption of energy by incorporating IoT devices (such as sensors, actuators, and smart meters), as well as by providing connectivity, automation, and tracking for such devices. For these applications, the use of low-power long-range wireless networks (LPWAN) is fundamental to facilitate all the necessary tasks in the smart grids in City 4.0 and Industry 4.0. The integration of renewable energies (photovoltaic solar, wind energy, biomass energy, hydroelectric energy, and other sources) in smart grids implies the monitoring of households, cities, industries, and electric vehicles at all times. In this sense, the development of monitoring and control applications using mobile devices is a fundamental tool in this type of system, which complements all the possibilities offered by the IoT. Smart energy meters are used to allow for communication between consumers and utility command centers to exchange messages about electrical consumption. Thus, it is essential to have access from any location and instant access to information using mobile devices or computers.

Format
  • Hardback
License
© 2022 by the authors; CC BY-NC-ND license
Keywords
knowledge-based sensor; Internet of Things; high-concentration photovoltaic systems; sun tracker; buildings energy management; deep learning; energy consumption prediction; LSTM; autoencoder; load forecasting; smart sensors; smart meter; temporal data granularity; electric load profile; time slices; time series; advanced metering infrastructure; monitoring; data acquisition systems; renewable energy; multi-objective optimization; reactive power (RP) planning; hybrid algorithm; virus colony search; particle swarm optimization; LoRaWAN; smart irrigation systems; smart energy; IoT; renewable energy sources; photovoltaic energy; I-V curve; monitoring and data acquisition; microgrid; open-source; communication protocols; DC interrupting; digitization; remote control; electric energy measurement; miniature circuit breaker; smart meter; power meter; internet of things; load control; energy meter; smart socket; intelligent campus; smart building; internet of things platform; remote monitoring and control; classification; data anomalies; data imputation; energy consumption data; ensemble classifiers; machine learning; smart home data; smart meter data; tracebase dataset; home energy management system; smart home; Internet of Things; cloud infrastructure; distributed PV; energy management system; energy storage units; charging piles; smart grid; redundancy; IoT; Home Assistant; low-carbon island; Kinmen