Data-Driven Energy Waste Minimization at Energy Distribution Networks

Babak Aslani ()
Additional contact information
Babak Aslani: George Mason University

A chapter in Handbook of Smart Energy Systems, 2023, pp 1413-1433 from Springer

Abstract: Abstract Energy is the essence of global economic and social development. However, the challenges from large-scale exploitation and utilization of energy resources in the last decades have seriously questioned the current strategies for energy distribution networks. Smart energy systems are the promising paradigm for providing affordable and sustainable energy systems. This strategic shift of perspective for the energy sector is becoming a popular direction for sustainable communities. Although the definition, implementation, and optimization of these intelligent systems have been studied extensively, efficient energy management has remained one of the most significant challenges in designing these networks. By adopting state-of-the-art technologies like smart sensors, wireless transmission, network communication, and cloud computing, large amounts of data are increasingly available in the energy sector. Moreover, big data technologies have become essential approaches to design sustainable systems, and thus, data-driven methods are becoming highly applicable to energy-related problems. In fact, computational intelligence algorithms can help tackle technical challenges associated with intelligent energy management in energy distribution networks. This chapter reviews the current trends in data-driven approaches to energy waste minimization in energy distribution networks. The focus will be on diverse data-driven techniques in the main application area, along with relevant implemented examples. The implemented data-driven algorithms will be classified into general classes, and the scope of the works and data sources will be investigated. The current challenges show the need to migrate from standard data-driven modeling to recently introduced AI 2.0 paradigm to address this challenging problem adequately.

Keywords: Smart energy systems; Data-driven analytics; Waste management; Clean energy; Artificial intelligence (AI) 2.0; Energy efficiency (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-97940-9_48

Ordering information: This item can be ordered from
http://www.springer.com/9783030979409

DOI: 10.1007/978-3-030-97940-9_48

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().