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Incremental Learning Model for Load Forecasting without Training Sample

by Charnon Chupong, Boonyang Plangklang*

Faculty of Engineering, Rajamangala University of Technology Thanyaburi, Pathum Thani, 12110, Thailand

* Corresponding Author: Boonyang Plangklang. Email: email

Computers, Materials & Continua 2022, 72(3), 5415-5427. https://doi.org/10.32604/cmc.2022.028416

Abstract

This article presents hourly load forecasting by using an incremental learning model called Online Sequential Extreme Learning Machine (OS-ELM), which can learn and adapt automatically according to new arrival input. However, the use of OS-ELM requires a sufficient amount of initial training sample data, which makes OS-ELM inoperable if sufficiently accurate sample data cannot be obtained. To solve this problem, a synthesis of the initial training sample is proposed. The synthesis of the initial sample is achieved by taking the first data received at the start of working and adding random noises to that data to create new and sufficient samples. Then the synthesis samples are used to initial train the OS-ELM. This proposed method is compared with Fully Online Extreme Learning Machine (FOS-ELM), which is an incremental learning model that also does not require the initial training samples. Both the proposed method and FOS-ELM are used for hourly load forecasting from the Hourly Energy Consumption dataset. Experiments have shown that the proposed method with a wide range of noise levels, can forecast hourly load more accurately than the FOS-ELM.

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APA Style
Chupong, C., Plangklang, B. (2022). Incremental learning model for load forecasting without training sample. Computers, Materials & Continua, 72(3), 5415-5427. https://doi.org/10.32604/cmc.2022.028416
Vancouver Style
Chupong C, Plangklang B. Incremental learning model for load forecasting without training sample. Comput Mater Contin. 2022;72(3):5415-5427 https://doi.org/10.32604/cmc.2022.028416
IEEE Style
C. Chupong and B. Plangklang, “Incremental Learning Model for Load Forecasting without Training Sample,” Comput. Mater. Contin., vol. 72, no. 3, pp. 5415-5427, 2022. https://doi.org/10.32604/cmc.2022.028416



cc Copyright © 2022 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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