Journal article
Improving Load Forecasting Process for a Power Distribution Network Using Hybrid AI and Deep Learning Algorithms
IEEE access, v 7, pp 82584-82598
2019
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Load forecasting is useful for various applications, including maintenance planning. The study of load forecasting using recent state-of-the-art hybrid artificial intelligence (AI) and deep learning (DL) techniques is limited in South Africa (SA) and South African power distribution networks. This paper proposes a novel hybrid AI and DL South African distribution network load forecasting system. The system comprises of modules that handle the collection of the loading data from the field, analysis of data integrity using fuzzy logic, data preprocessing, consolidation of the loading and the temperature data, and load forecasting. The load forecasting results are then used to inform maintenance planning. The load forecasting is conducted using a hybrid AI/DL load forecasting module. A novel comparative study of recent state-of-the-art AI techniques is also presented to determine the best technique to deploy in this module when forecasting South African power redistributing customers' loads. The impact of the inclusion of weather parameters and loading data clean up on the load forecasting performance of a hybrid AI technique, optimally pruned extreme learning machines (OP-ELM), and a deep learning technique, long short-term memory (LSTM), is also investigated. These techniques are compared with each other and also with a commonly used powerful hybrid AI technique, adaptive neuro-fuzzy inference system (ANFIS). LSTM was found to achieve higher load forecasting accuracies than ANFIS and OP-ELM in forecasting the two distribution customers' loads in this paper. Only the LSTM models' performance improved with the inclusion of temperature in their development.
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Details
- Title
- Improving Load Forecasting Process for a Power Distribution Network Using Hybrid AI and Deep Learning Algorithms
- Creators
- Sibonelo Motepe - University of JohannesburgAli N. Hasan - University of JohannesburgRiaan Stopforth - University of Zululand
- Publication Details
- IEEE access, v 7, pp 82584-82598
- Publisher
- IEEE
- Number of pages
- 15
- Grant note
- Eskom Tertiary Education Support Programme (TESP) Department of Science and Technology (DST) Robotics Strategy of South Africa (ROSSA) Programme National Research Foundation (10.13039/100011512)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Engineering Leadership and Society/Engineering Technology
- Web of Science ID
- WOS:000475354900001
- Scopus ID
- 2-s2.0-85068640058
- Other Identifier
- 991022004631604721
UN Sustainable Development Goals (SDGs)
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Information Systems
- Engineering, Electrical & Electronic
- Telecommunications