Short-term electrical energy demand prediction under heat island effects using emotional neural network integrated with genetic algorithm

Karalasingham, Sagthitharan and Deo, Ravinesh ORCID: https://orcid.org/0000-0002-2290-6749 and Prasad, Ramendra (2021) Short-term electrical energy demand prediction under heat island effects using emotional neural network integrated with genetic algorithm. In: Predictive modelling for energy management and power systems engineering. Elsevier, Amsterdam, Netherlands, pp. 271-298. ISBN 978-0-12-817772-3


Abstract

The aim of this project is to develop a data-driven model to predict short-term electricity demand, day-ahead, incorporating granular climate data capturing the effects of urban-scale climatic phenomena such as UHI. In particular the development of energy demand prediction models which take into account climatic variability in space and time, while being computationally efficient will be of practical use for the players in generating near-real-time predictions for the electricity market and policy planners. The models evaluated against UHI-affected sites provide an important tool in capturing the shifts in electricity consumption, thereby influencing the application of electricity demand modeling toward the study of energy efficiency at the urban scale contributing to the design of energy-efficient cities.


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Item Type: Book Chapter (Commonwealth Reporting Category B)
Refereed: Yes
Item Status: Live Archive
Additional Information: Permanent restricted access to Published version + Front Matter in accordance with the copyright policy of the publisher.
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sep 2019 -)
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sep 2019 -)
Date Deposited: 06 Oct 2020 06:05
Last Modified: 06 Oct 2020 06:15
Uncontrolled Keywords: short-term electricity demand; prediction; forecasting
Fields of Research (2008): 05 Environmental Sciences > 0599 Other Environmental Sciences > 059999 Environmental Sciences not elsewhere classified
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080108 Neural, Evolutionary and Fuzzy Computation
Socio-Economic Objectives (2008): E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
URI: http://eprints.usq.edu.au/id/eprint/39836

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