Averaging Ensembles Model for Forecasting of Short-term Load in Smart Grids

Dabeeruddin Syed, Shady S. Refaat, Haitham Abu-Rub, Othmane Bouhali, Ameema Zainab, Le Xie

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The traditional electric power grid is moving toward smart grid, and with the advent of smart grids, a lot of data is generated in high volumes, velocity and variety. This brings several challenges with real time processing to get meaningful information for enhancing the benefits of smart grid. Furthermore, the application of short-term load forecasting poses additional challenges of being highly uncertain and volatile due to different load profiles. This paper conducts a study on the demand side management and load forecasting in electric power grids with help of the historical data obtained from smart grid. Machine learning models are developed using deep learning to ensure significant improvement in forecast accuracy when compared to benchmark Auto-Regressive Integrated Moving Averages ARIMA analysis. The short-term load forecast data has been merged with weather data from Application Program Interface (API). The discussed system uses an autonomous feedback loop to consider the historical load values as features for training and testing. This paper also applies deep learning methods like pooling based recurrent neural networks which could solve the curse of dimensionality that usually exists with increasing layers in traditional neural network methods. The paper proposes an averaging regression ensembles model for short-term load forecast.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE International Conference on Big Data, Big Data 2019
EditorsChaitanya Baru, Jun Huan, Latifur Khan, Xiaohua Tony Hu, Ronay Ak, Yuanyuan Tian, Roger Barga, Carlo Zaniolo, Kisung Lee, Yanfang Fanny Ye
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2931-2938
Number of pages8
ISBN (Electronic)9781728108582
DOIs
Publication statusPublished - Dec 2019
Event2019 IEEE International Conference on Big Data, Big Data 2019 - Los Angeles, United States
Duration: 9 Dec 201912 Dec 2019

Publication series

NameProceedings - 2019 IEEE International Conference on Big Data, Big Data 2019

Conference

Conference2019 IEEE International Conference on Big Data, Big Data 2019
CountryUnited States
CityLos Angeles
Period9/12/1912/12/19

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Keywords

  • Load forecast
  • deep learning
  • long-short term memory
  • recurrent neural networks.
  • short-term

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Information Systems
  • Information Systems and Management

Cite this

Syed, D., Refaat, S. S., Abu-Rub, H., Bouhali, O., Zainab, A., & Xie, L. (2019). Averaging Ensembles Model for Forecasting of Short-term Load in Smart Grids. In C. Baru, J. Huan, L. Khan, X. T. Hu, R. Ak, Y. Tian, R. Barga, C. Zaniolo, K. Lee, & Y. F. Ye (Eds.), Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019 (pp. 2931-2938). [9006183] (Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData47090.2019.9006183