Conference proceeding
Using Deep Learning Techniques for South African Power Distribution Networks Load Forecasting
2019 INTERNATIONAL AEGEAN CONFERENCE ON ELECTRICAL MACHINES AND POWER ELECTRONICS (ACEMP) & 2019 INTERNATIONAL CONFERENCE ON OPTIMIZATION OF ELECTRICAL AND ELECTRONIC EQUIPMENT (OPTIM), pp 575-580
01 Jan 2019
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Load forecasting has many benefits for utilities. Artificial Intelligence (AI) has been seen to be effective in load forecasting. Deep Learning AI techniques have been found to perform better than traditional AI techniques. The study of deep learning techniques application in South African load forecasting is in its infancy. This paper presents a study of a South African distribution substation using two deep learning techniques, deep belief networks (DBN) and long short-term memory (LSTM), to address this. The impact of temperature and cleaning up the loading data is also studied. It was found that an LSTM model achieved the lowest errors with a symmetric mean absolute percentage error (sMAPE) of 3.3%, an), mean absolute error (MAE) of 4.6% and root mean square error (RMSE) of 5.5%. These errors were achieved with non-cleaned data with temperature not used as a variable for the training the model. Deep learning techniques can thus be used without weather parameters to forecast distribution substation data with low errors.
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1 citations in Scopus
Details
- Title
- Using Deep Learning Techniques for South African Power Distribution Networks Load Forecasting
- Creators
- Sibonelo Motepe - University of JohannesburgAli N. Hasan - University of JohannesburgBhekisipho Twala - University of South AfricaRiaan Stopforth - University of South Africa
- Publication Details
- 2019 INTERNATIONAL AEGEAN CONFERENCE ON ELECTRICAL MACHINES AND POWER ELECTRONICS (ACEMP) & 2019 INTERNATIONAL CONFERENCE ON OPTIMIZATION OF ELECTRICAL AND ELECTRONIC EQUIPMENT (OPTIM), pp 575-580
- Publisher
- IEEE
- Number of pages
- 6
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Engineering Leadership and Society/Engineering Technology
- Web of Science ID
- WOS:000535884900087
- Scopus ID
- 2-s2.0-85081634486
- Other Identifier
- 991022004768204721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Energy & Fuels
- Engineering, Electrical & Electronic