Effective load forecasting for large power consuming industrial customers using long short-term memory recurrent neural networks

Article type: Research Article

Authors: Motepe, Sibonelo^{a; *} | Hasan, Ali N.^a | Twala, Bhekisipho^b | Stopforth, Riaan^c

Affiliations: [a] Faculty of Engineering and The Built Environment, University of Johannesburg, Johannesburg, South Africa | [b] Faculty of Engineering and The Built Environment, Durban University of Technology, Durban, South Africa | [c] Stopforth Mechatronics Robotics Research Lab, School of Engineering, University of Kwa-Zulu Natal, Durban, South Africa

Correspondence: [*] Corresponding author. Sibonelo Motepe, Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg, South Africa. E-mail: [email protected].; E-mail: [email protected].

Abstract: The study of South African distribution (Dx.) network’s load forecasting using recent and state of the art AI (machine learning, deep learning and ensemble deep learning) techniques, is limited. The impact of weather parameters on load forecasting performance of AI techniques in forecasting South African large power users is not well understood. This paper proposes a novel distribution network load forecasting system. The paper further introduces deep learning and ensemble deep learning techniques in forecasting the power consumption of large South African power users. The paper introduces these techniques through an investigation of their performance against that off state of the art machine learning techniques, ANFIS and OP-ELM. The impact of temperature on the performance of these techniques is also investigated. This investigation was conducted on three case studies, with three different industrial large power consumer loads. LSTM-RNN proved to be a more efficient load forecasting technique for the proposed load forecasting system, achieving the lowest load forecasting error in all three case studies. Ensembles of LSTM were found to overall achieve lower errors than the individual techniques’ models. This improvement was less than 1%. The inclusion of temperature was found to generally improve the load forecasting performance of ML and DL techniques’ models.

Keywords: Distribution networks, load forecasting, deep learning, machine learning, LSTM-RNN

DOI: 10.3233/JIFS-190658

Journal: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 6, pp. 8219-8235, 2019