Big Data Analytics for Short and Medium-Term Electricity Load Forecasting Using an AI Techniques Ensembler

Nasir Ayub, Muhammad Irfan, Muhammad Awais, Usman Ali, Tariq Ali, Mohammed Hamdi, Abdullah Alghamdi and Fazal Muhammad
Additional contact information
Nasir Ayub: Department of Computer Science, Federal Urdu University of Arts, Science and Technology, Islamabad 44000, Pakistan
Muhammad Irfan: Electrical Engineering Department, College of Engineering, Najran University, Najran 61441, Saudi Arabia
Muhammad Awais: School of Computing and Communications, Lancaster University, Bailrigg, Lancaster LA1 4YW, UK
Usman Ali: Department of Computing, RIPHAH University Faisalabad, Faisalabad 38000, Pakistan
Tariq Ali: Electrical Engineering Department, College of Engineering, Najran University, Najran 61441, Saudi Arabia
Mohammed Hamdi: College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia
Abdullah Alghamdi: College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia
Fazal Muhammad: Department of Electrical Engineering, City University of Science & Information Technology Peshawar, Peshawar 25000, Pakistan

Energies, 2020, vol. 13, issue 19, 1-21

Abstract: Electrical load forecasting provides knowledge about future consumption and generation of electricity. There is a high level of fluctuation behavior between energy generation and consumption. Sometimes, the energy demand of the consumer becomes higher than the energy already generated, and vice versa. Electricity load forecasting provides a monitoring framework for future energy generation, consumption, and making a balance between them. In this paper, we propose a framework, in which deep learning and supervised machine learning techniques are implemented for electricity-load forecasting. A three-step model is proposed, which includes: feature selection, extraction, and classification. The hybrid of Random Forest (RF) and Extreme Gradient Boosting (XGB) is used to calculate features’ importance. The average feature importance of hybrid techniques selects the most relevant and high importance features in the feature selection method. The Recursive Feature Elimination (RFE) method is used to eliminate the irrelevant features in the feature extraction method. The load forecasting is performed with Support Vector Machines (SVM) and a hybrid of Gated Recurrent Units (GRU) and Convolutional Neural Networks (CNN). The meta-heuristic algorithms, i.e., Grey Wolf Optimization (GWO) and Earth Worm Optimization (EWO) are applied to tune the hyper-parameters of SVM and CNN-GRU, respectively. The accuracy of our enhanced techniques CNN-GRU-EWO and SVM-GWO is 96.33% and 90.67%, respectively. Our proposed techniques CNN-GRU-EWO and SVM-GWO perform 7% and 3% better than the State-Of-The-Art (SOTA). In the end, a comparison with SOTA techniques is performed to show the improvement of the proposed techniques. This comparison showed that the proposed technique performs well and results in the lowest performance error rates and highest accuracy rates as compared to other techniques.

Keywords: load forecasting; optimization techniques; deep learning; big data analytics (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 (10)

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