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

• Published in: Energies (Basel) Vol. 13; no. 11; p. 2907
• Main Authors: Ahmad, Waqas, Ayub, Nasir, Ali, Tariq, Irfan, Muhammad, Awais, Muhammad, Shiraz, Muhammad, Glowacz, Adam
• Format: Journal Article
• Language: English
• Published: MDPI AG 01-06-2020
• Subjects: electricity load forecasting; Extreme Learning Machine; feature selection; Genetic Algorithm; smart grid; Support Vector Machine
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en13112907

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.