Short-term load demand forecasting using nonlinear dynamic grey-black-box and kernel optimization models: A new generation learning algorithm

S. I. Abba*, Sagir Jibrin Kawu, Hamza Sabo MacCido, S. M. Lawan, Gafai Najashi, Abdullahi Yusuf Sada

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this study, new learning algorithms were employed viz: grey-black-box (GBB) and kernel optimization(K-SVR) for short-term load demand forecasting. The obtained data is randomly categorized into calibration and verification phases. The prediction accuracy of the algorithm is assessed using Correlation Coefficient (R), Coefficient of Determination (R2), Mean Square Error (MSE), and Root Mean Square Error (RMSE). The model combinations were achieved using the input variables feature selection approach. The GBB model has the highest goodness-of-fit (99%) and lowest prediction error (0.0). The results for both GBB, K-SVR models proved promising despite outstanding reliability of GBB model. The results show that the new learning algorithms are capable of forecasting load demand with simultaneous accuracy higher than 80%.

Original languageEnglish
Title of host publication2021 1st International Conference on Multidisciplinary Engineering and Applied Science, ICMEAS 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665434935
DOIs
StatePublished - 2021
Externally publishedYes

Publication series

Name2021 1st International Conference on Multidisciplinary Engineering and Applied Science, ICMEAS 2021

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

Keywords

  • Abuja
  • Black-Box
  • Grey-Box
  • Kernel optimization
  • Load Demand Forecasting

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Control and Optimization
  • Engineering (miscellaneous)

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