Multiple wavelet convolutional neural network for short-term load forecasting
- Submitting institution
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Glasgow Caledonian University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 42261929
- Type
- D - Journal article
- DOI
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10.1109/JIOT.2020.3026733
- Title of journal
- IEEE Internet of Things Journal
- Article number
- -
- First page
- N/A
- Volume
- N/A
- Issue
- -
- ISSN
- 2327-4662
- Open access status
- Compliant
- Month of publication
- September
- Year of publication
- 2020
- URL
-
-
- Supplementary information
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-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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4
- Research group(s)
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-
- Citation count
- -
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper proposes to make use of different types of wavelets to reconstruct features for CNN, and ensembles multiple models to improve prediction performance. This method has been used in Load Forecasting in Smart Grid. This work enhanced the continuous international collaboration between GCU and CSU (Central South University, China), in terms of expanding the collaboration domain to the new dimension of cutting edge Deep learning and IoT. This work led to 1 recently submitted follow-up paper.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -