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Zohreh Kaheh
Tarbiat Modares University
Corresponding Author
ORCiD: https://orcid.org/0000-0002-8518-8545
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It stands to reason that developing more accurate forecasting methods is the pillar of building robust multi-energy systems. In this context, long-term forecasting is also indispensable to have a robust expansion planning program for modern power systems. While very short-term and short-term forecasting are usually represented with point estimation, this approach is highly unreliable in medium-term and long-term forecasting due to inherent uncertainty in predictors like weather variable in long terms. Accordingly, long-term forecasting is usually represented by probabilistic forecasting values which are based on probabilistic functions. In this paper, a self-organizing mixture network (SOMN) is developed to estimate the probability density function (PDF) of peak load in long-term horizons considering the most important drivers of seasonal similarity, population, GDP, and electricity price. The proposed methodology is applied to forecast the PDF of annual and seasonal peak load in Queensland Australia.
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View the published article at Journal of Big Data.
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Review #1 received
Received 19 Dec, 2020
Editorial decision: Accept
On 19 Dec, 2020
Review #2 received
Received 17 Dec, 2020
Reviewer #2 agreed
On 13 Dec, 2020
Reviewer #1 agreed
On 13 Dec, 2020
Reviewers invited
Invitations sent on 08 Dec, 2020
Editor assigned
On 06 Dec, 2020
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On 06 Dec, 2020
Editor invited
On 06 Dec, 2020
Version 1
Posted 17 Aug, 2020
No comments provided
Editorial decision: Major Revision
On 23 Sep, 2020
Review #1 received
Received 11 Sep, 2020
Review #2 received
Received 07 Sep, 2020
Review #3 received
Received 07 Sep, 2020
Reviewer #2 agreed
On 28 Aug, 2020
Reviewer #3 agreed
On 28 Aug, 2020
Reviewer #4 agreed
On 28 Aug, 2020
Reviewers invited
Invitations sent on 27 Aug, 2020
Reviewer #1 agreed
On 27 Aug, 2020
Editor assigned
On 14 Aug, 2020
Submission checks complete
On 13 Aug, 2020
Editor invited
On 13 Aug, 2020
First submitted
On 31 Jul, 2020
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Subject Areas
Energy Engineering
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See the published version of this preprint at Journal of Big Data.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research
Zohreh Kaheh
Tarbiat Modares University
Corresponding Author
ORCiD: https://orcid.org/0000-0002-8518-8545
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at Journal of Big Data.
No community comments so far
Review #1 received
Received 19 Dec, 2020
Editorial decision: Accept
On 19 Dec, 2020
Review #2 received
Received 17 Dec, 2020
Reviewer #2 agreed
On 13 Dec, 2020
Reviewer #1 agreed
On 13 Dec, 2020
Reviewers invited
Invitations sent on 08 Dec, 2020
Editor assigned
On 06 Dec, 2020
Submission checks complete
On 06 Dec, 2020
Editor invited
On 06 Dec, 2020
Version 1
Posted 17 Aug, 2020
No comments provided
Editorial decision: Major Revision
On 23 Sep, 2020
Review #1 received
Received 11 Sep, 2020
Review #2 received
Received 07 Sep, 2020
Review #3 received
Received 07 Sep, 2020
Reviewer #2 agreed
On 28 Aug, 2020
Reviewer #3 agreed
On 28 Aug, 2020
Reviewer #4 agreed
On 28 Aug, 2020
Reviewers invited
Invitations sent on 27 Aug, 2020
Reviewer #1 agreed
On 27 Aug, 2020
Editor assigned
On 14 Aug, 2020
Submission checks complete
On 13 Aug, 2020
Editor invited
On 13 Aug, 2020
First submitted
On 31 Jul, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Energy Engineering
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at Journal of Big Data.
It stands to reason that developing more accurate forecasting methods is the pillar of building robust multi-energy systems. In this context, long-term forecasting is also indispensable to have a robust expansion planning program for modern power systems. While very short-term and short-term forecasting are usually represented with point estimation, this approach is highly unreliable in medium-term and long-term forecasting due to inherent uncertainty in predictors like weather variable in long terms. Accordingly, long-term forecasting is usually represented by probabilistic forecasting values which are based on probabilistic functions. In this paper, a self-organizing mixture network (SOMN) is developed to estimate the probability density function (PDF) of peak load in long-term horizons considering the most important drivers of seasonal similarity, population, GDP, and electricity price. The proposed methodology is applied to forecast the PDF of annual and seasonal peak load in Queensland Australia.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
The full text of this article is available to read as a PDF.
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