Forecasting the Total South African Unplanned Capability Loss Factor Using an Ensemble of Deep Learning Techniques

Energies

Ali N. Hasan¹  Sibonelo Motepe²  Thokozani Shongwe² 

[1] Department of Electrical Engineering, Faculty of Engineering Science and Technology, Higher Colleges of Technology, Abu Dhabi 25026, United Arab Emirates;Department of Electrical and Electronic Engineering Technology, Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg 2092, South Africa;

关键词: deep learning;    forecasting;    power outages;    coal power plants;    recurrent neural networks;    ensemble techniques;   

DOI  :  10.3390/en15072546

来源: DOAJ

【 摘 要 】

Unplanned power plant failures have been seen to be a major cause of power shortages, and thus customer power cuts, in the South African power grid. These failures are measured as the unplanned capability loss factor (UCLF). The study of South Africa’s UCLF is almost non-existent. Parameters that affect the future UCLF are, thus, still not well understood, making it challenging to forecast when power shortages may be experienced. This paper presents a novel study of South African UCLF forecasting using state-of-the-art deep learning techniques. The study further introduces a novel deep learning ensemble South African UCLF forecasting system. The performance of three of the best recent forecasting techniques, namely, long short-term memory recurrent neural network (LSTM-RNN), deep belief network (DBN), and optimally pruned extreme learning machines (OP-ELM), as well as their aggregated ensembles, are investigated for South African UCLF forecasting. The impact of three key parameters (installed capacity, demand, and planned capability loss factor) on the future UCLF is investigated. The results showed that the exclusion of installed capacity in the LSTM-RNN, DBN, OP-ELM, and ensemble models doubled the UCLF forecasting error. It was also found that an ensemble model of two LSTM-RNN models achieved the lowest errors with a symmetric mean absolute percentage error (sMAPE) of 6.43%, mean absolute error (MAE) of 7.36%, and root-mean-square error (RMSE) of 9.21%. LSTM-RNN also achieved the lowest errors amongst the individual models.

【 授权许可】

Unknown