Towards Short Term Electricity Load Forecasting Using Improved Support Vector Machine and Extreme Learning Machine

Waqas Ahmad, Nasir Ayub, Tariq Ali, Muhammad Irfan, Muhammad Awais, Muhammad Shiraz and Adam Glowacz
Additional contact information
Waqas Ahmad: Department of Computer Science, Federal Urdu University of Arts, Science and Technology, Islamabad 44000, Pakistan
Nasir Ayub: Department of Computer Science, Federal Urdu University of Arts, Science and Technology, Islamabad 44000, Pakistan
Tariq Ali: College of Engineering, Electrical Engineering Department, Najran University, Najran 61441, Saudi Arabia
Muhammad Irfan: College of Engineering, Electrical Engineering Department, Najran University, Najran 61441, Saudi Arabia
Muhammad Awais: School of Computing and Communications, Lancaster University, Bailrigg, Lancaster LA1 4YW, UK
Muhammad Shiraz: Department of Computer Science, Federal Urdu University of Arts, Science and Technology, Islamabad 44000, Pakistan
Adam Glowacz: Department of Automatic Control and Robotics, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Science and Technology, al. A. Mickiewicza 30, 30-059 Kraków, Poland

Energies, 2020, vol. 13, issue 11, 1-17

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.

Keywords: electricity load forecasting; smart grid; feature selection; Extreme Learning Machine; Genetic Algorithm; Support Vector Machine; Grid Search (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (18)

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