A Hybrid Smart Neural Network Model for Short-Term Prediction of Energy Consumption

Kianoush Nokhbeh Dehghan, Seyed Habib A. Rahamti () and Soulmaz Rahman Mohammadpour
Additional contact information
Kianoush Nokhbeh Dehghan: Islamic Azad University
Seyed Habib A. Rahamti: Islamic Azad University
Soulmaz Rahman Mohammadpour: Islamic Azad University

A chapter in Handbook of Smart Energy Systems, 2023, pp 1975-1985 from Springer

Abstract: Abstract Nowadays, energy management is a crucial issue for the governments all over the world. Different scientific tools are implemented by the practitioners to empower managers to make their dynamic decisions. Data analysis and data mining provide smart and practical classes tools for mentioned decision-making process. Meanwhile, a chief obstacle is short-term prediction of various energy resources, just like other short-term prediction areas such as weather or earthquake prediction. In this chapter, the adaptive neural fuzzy inference system (ANFIS) and its combination with genetic algorithm (GA) have been proposed for short-term prediction of energy consumption. In the proposed method, first, a classical ANFIS model is conducted by the back-propagation approach. Then, GA is used for the optimal design of fuzzy systems of Takagi Suguno type. In fact, the weights of the classical model are adjusted in the data-training section by GA. The implementing data set is real data collected from the Office of Energy Efficiency at Natural Resources Canada (NRCan). The data set considers electricity, natural gas, diesel fuel oil, heavy fuel oil, still gas and petroleum coke, and liquefied petroleum gas (LPG) terms of energy. The results of the research prove the efficiency of the developing method as an empowering and practical tool for short-term prediction of energy consumption.

Keywords: Energy consumption; Prediction; Adaptive neural fuzzy inference system; Genetic algorithm; Fuzzy inference system (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_123

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

DOI: 10.1007/978-3-030-97940-9_123

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 ().