Causality tests using basic and optimally-pruned extreme learning machines

Marwala, Lufuno; Twala, Bhekisipho

doi:10.1109/iccre.2017.7935064

Cited by 2 publications

(4 citation statements)

References 16 publications

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“…The technique is a single layer neural network that was proposed in the mid-2000s by Huang et al These researchers also demonstrated that this method could be utilized successfully in a variety of applications [55]. ELM has been used in many other applications by different researchers [32], [56], [57]. In [57] ELM was shown to perform better than traditional artificial neural networks (ANN), radial basis function neural networks (RBFNN) and back-propagation neural networks (BPNN) in most testing datasets, in market clearance price forecasting.…”

Section: Optimally Pruned Extreme Learning Machinesmentioning

confidence: 99%

“…where H * is matrix H s Moore-Penrose generalized inverse [32]. The ELM algorithm can be summarized as follows:…”

Section: Optimally Pruned Extreme Learning Machinesmentioning

confidence: 99%

“…The study of the application of AI in S.A. load forecasting is limited [9], [32]- [38]. Some of these studies are outdated and do not use recently developed techniques [34], [35].…”

Section: Introductionmentioning

confidence: 99%

“…ANFIS is thus used in this study as a comparison base for the performance of the techniques used in this study. Marwala et al introduced the recent state of the art extreme learning machines (ELM) and its improved version, optimally pruned extreme learning machine (OP-ELM) in SA load forecasting [32], [33]. Their studies focused on the country's total consumption and not on distribution level power networks.…”

Section: Introductionmentioning

confidence: 99%

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Improving Load Forecasting Process for a Power Distribution Network Using Hybrid AI and Deep Learning Algorithms

2019

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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-theart 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.INDEX TERMS Adaptive neuro-fuzzy inference systems, artificial intelligence, deep learning, distribution networks, extreme learning machines, load forecasting, recurrent neural networks, long short-term memory.

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Section: Optimally Pruned Extreme Learning Machinesmentioning

confidence: 99%

“…where H * is matrix H s Moore-Penrose generalized inverse [32]. The ELM algorithm can be summarized as follows:…”

Section: Optimally Pruned Extreme Learning Machinesmentioning

confidence: 99%

“…The study of the application of AI in S.A. load forecasting is limited [9], [32]- [38]. Some of these studies are outdated and do not use recently developed techniques [34], [35].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations