Networked connection of people, data, process, and things. In other words, IoE extends IoT by including intelligent and robust communication between machines-to-people, machine-to-machine, people-to-machines and people-to-people.
Published in Chapter:
Monitoring, Predicting, and Optimizing Energy Consumptions: A Goal Toward Global Sustainability
Pedro J. S. Cardoso (Universidade do Algarve, Portugal & LARSyS, Institute for Systems and Robotics, Lisbon, Portugal),
Jânio Monteiro (Universidade do Algarve, Portugal & INESC-ID, Lisbon, Portugal), Cristiano Cabrita (Universidade do Algarve, Portugal & Centre of Intelligent Systems, IDMEC, Portugal), Jorge Semião (Universidade do Algarve, Portugal & INESC-ID, Lisbon, Portugal), Dario Medina Cruz (Universidade do Algarve, Portugal), Nelson Pinto (Universidade do Algarve, Portugal),
Célia M.Q. Ramos (Universidade do Algarve, Portugal & CIEO, Portugal), Luís M. R. Oliveira (Universidade do Algarve, Portugal & CISE, Portugal), and João M. F. Rodrigues (Universidade do Algarve, Portugal & LARSyS, Institute for Systems and Robotics, Lisbon, Portugal)
Copyright: © 2020
|Pages: 28
DOI: 10.4018/978-1-7998-2112-0.ch005
Abstract
Energy consumption and, consequently, the associated costs (e.g., environmental and monetary) concern most individuals, companies, and institutions. Platforms for the monitoring, predicting, and optimizing energy consumption are an important asset that can contribute to the awareness about the ongoing usage levels, but also to an effective reduction of these levels. A solution is to leave the decisions to smart system, supported for instance in machine learning and optimization algorithms. This chapter involves those aspects and the related fields with emphasis in the prediction of energy consumption to optimize its usage policies.