Short-term electrical energy demand prediction under heat island effects using emotional neural network integrated with genetic algorithm
Edited book (chapter)
Chapter Title | Short-term electrical energy demand prediction under heat island effects using emotional neural network integrated with genetic algorithm |
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Book Chapter Category | Edited book (chapter) |
ERA Publisher ID | 1821 |
Book Title | Predictive modelling for energy management and power systems engineering |
Authors | Karalasingham, Sagthitharan (Author), Deo, Ravinesh (Author) and Prasad, Ramendra (Author) |
Editors | Deo, Ravinesh, Samui, Pijush and Roy, Sanjiban Sekhar |
Page Range | 271-298 |
Chapter Number | 10 |
Number of Pages | 28 |
Year | 2021 |
Publisher | Elsevier |
Place of Publication | Amsterdam, Netherlands |
ISBN | 9780128177723 |
Web Address (URL) | https://www.elsevier.com/books/predictive-modelling-for-energy-management-and-power-systems-engineering/deo/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. |
Keywords | short-term electricity demand; prediction; forecasting |
ANZSRC Field of Research 2020 | 419999. Other environmental sciences not elsewhere classified |
469999. Other information and computing sciences not elsewhere classified | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | School of Sciences |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q5yq3/short-term-electrical-energy-demand-prediction-under-heat-island-effects-using-emotional-neural-network-integrated-with-genetic-algorithm
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