Home > Research > Publications & Outputs > Towards short term electricity load forecasting...

Links

Text available via DOI:

View graph of relations

Towards short term electricity load forecasting using improved support vector machine and extreme learning machine

Research output: Contribution to journalJournal article

Published
  • W. Ahmad
  • N. Ayub
  • T. Ali
  • M. Irfan
  • M. Awais
  • M. Shiraz
  • A. Glowacz
Close
Article number2907
<mark>Journal publication date</mark>5/06/2020
<mark>Journal</mark>Energies
Issue number11
Volume13
Number of pages17
Publication StatusPublished
<mark>Original language</mark>English

Abstract

Forecasting the electricity load provides its future trends, consumption patterns and its usage. There is no proper strategy to monitor the energy consumption and generation; and high variation among them. Many strategies are used to overcome this problem. The correct selection of parameter values of a classifier is still an issue. Therefore, an optimization algorithm is applied with deep learning and machine learning techniques to select the optimized values for the classifier's hyperparameters. In this paper, a novel deep learning-based method is implemented for electricity load forecasting. A three-step model is also implemented, including feature selection using a hybrid feature selector (XGboost and decision tee), redundancy removal using feature extraction technique (Recursive Feature Elimination) and classification/forecasting using improved Support Vector Machine (SVM) and Extreme Learning Machine (ELM). The hyperparameters of ELM are tuned with a meta-heuristic algorithm, i.e., Genetic Algorithm (GA) and hyperparameters of SVM are tuned with the Grid Search Algorithm. The simulation results are shown in graphs and the values are shown in tabular form and they clearly show that our improved methods outperform State Of The Art (SOTA) methods in terms of accuracy and performance. The forecasting accuracy of Extreme Learning Machine based Genetic Algo (ELM-GA) and Support Vector Machine based Grid Search (SVM-GS) is 96.3% and 93.25%, respectively. The accuracy of our improved techniques, i.e., ELM-GA and SVM-GS is 10% and 7%, respectively, higher than the SOTA techniques. © 2020 by the authors.