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Electricity load and price forecasting with influential factors in a deregulated power industry

Hassan,S, Khosravi,A, Jaafar,J and Raza,MQ 2014, Electricity load and price forecasting with influential factors in a deregulated power industry, in SOSE 2014 : The Socio-Technical Perspective : Proceedings of the 9th International Conference on System of Systems Engineering, IEEE, Piscataway, N.J., pp. 79-84, doi: 10.1109/SYSOSE.2014.6892467.

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Title Electricity load and price forecasting with influential factors in a deregulated power industry
Author(s) Hassan,S
Khosravi,AORCID iD for Khosravi,A orcid.org/0000-0001-6927-0744
Jaafar,J
Raza,MQ
Conference name System of Systems Engineering. Conference (2014 : Adelaide, South Australia)
Conference location Adelaide, South Australia
Conference dates 9-13 June 2014
Title of proceedings SOSE 2014 : The Socio-Technical Perspective : Proceedings of the 9th International Conference on System of Systems Engineering
Editor(s) [Unknown]
Publication date 2014
Conference series System of Systems Engineering. Conference
Start page 79
End page 84
Total pages 6
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) deregulated power industry
influential factors
load/price forecasting
neural networks
Summary With the emergence of smart power grid and distributed generation technologies in recent years, there is need to introduce new advanced models for forecasting. Electricity load and price forecasts are two primary factors needed in a deregulated power industry. The performances of the demand response programs are likely to be deteriorated in the absence of accurate load and price forecasting. Electricity generation companies, system operators, and consumers are highly reliant on the accuracy of the forecasting models. However, historical prices from the financial market, weekly price/load information, historical loads and day type are some of the explanatory factors that affect the accuracy of the forecasting. In this paper, a neural network (NN) model that considers different influential factors as feedback to the model is presented. This model is implemented with historical data from the ISO New England. It is observed during experiments that price forecasting is more complicated and hence less accurate than the load forecasting.
ISBN 9781479952274
Language eng
DOI 10.1109/SYSOSE.2014.6892467
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 850699 Energy Storage
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2014, Institute of Electrical and Electronics Engineers
Persistent URL http://hdl.handle.net/10536/DRO/DU:30070519

Document type: Conference Paper
Collection: Centre for Intelligent Systems Research
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