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Data mining with wavelet analysis are used to create a combined renewable energy prediction model
PUBLISHED March 22, 2023 (DOI: https://doi.org/10.54985/peeref.2303p7706702)
NOT PEER REVIEWED
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Authors
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Seemant Tiwari1
- Dept. of Electrical Engineering, Southern Taiwan University of Science and Technology,Taiwan
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Conference / event
- Asian Conference on Machine Learning, 2021, November 2021 (Singapore, Singapore)
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Poster summary
- In large distribution networks and distributed energy, wind energy is critical. Grid power balance relies heavily on accurate wind farm predictions. For effectively extraction characteristics using wind time series analysis, this poster offers a time - series data fuzzy c-means grouping technique and also a clusters selection algorithm. Clustering analysis is an effective approach for data processing which is often utilised. Cluster analysis is used to partition large datasets in subgroups based on similarity as differences. A wavelet decomposition is being used to split down wind energy output and to provide the MLNARx with more appropriate inputs. A comparison with well-known estimation methods reveals that the suggested estimation method outperforms them.
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Keywords
- Prediction, Wavelet, Neural network, Clustering
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Research areas
- Electrical Engineering
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References
- No data provided
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Funding
- No data provided
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Supplemental files
- No data provided
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Additional information
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- Competing interests
- No competing interests were disclosed.
- Data availability statement
- Data sharing not applicable to this poster as no datasets were generated or analyzed during the current study.
- Creative Commons license
- Copyright © 2023 Tiwari. This is an open access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Tiwari, S. Data mining with wavelet analysis are used to create a combined renewable energy prediction model [not peer reviewed]. Peeref 2023 (poster).
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