A Pruning Algorithm Based on Relevancy Index of Hidden Neurons Outputs

Abid, Slim; Chtourou, Mohamed; Djemel, Mohamed

doi:10.48084/etasr.704

Cited by 2 publications

(1 citation statement)

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“…The best point in this region in terms of reduced MAE and improved accuracy was 630 neurons, so it was selected as the optimum number of neurons. Single hidden layered architecture is insufficient as a network with a smaller number of layers or neurons often fails to extract details from the training data [20]. Thus, 6 different architectures were tested comprising of 2, 4, 6, 8, 10, and 12 hidden GRU layers.…”

Section: B the E2e ML Modelmentioning

confidence: 99%

An End-to-End Machine Learning based Unified Architecture for Non-Intrusive Load Monitoring

Wali

Haq

Kazmi

et al. 2021

Eng. Technol. Appl. Sci. Res.

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Non-Intrusive Load Monitoring (NILM) or load disaggregation aims to analyze power consumption by decomposing the energy measured at the aggregate level into constituent appliances level. The conventional load disaggregation framework consists of signal processing and machine learning-based pipelined architectures, respectively for explicit feature extraction and decision making. Manual feature selection in such load disaggregation frameworks leads to biased decisions that eventually reduce system performance. This paper presents an efficient End-to-End (E2E) approach-based unified architecture using Gated Recurrent Units (GRU) for NILM. The proposed approach eliminates explicit feature engineering and has a unified classification and prediction model for appliance power. This eventually reduces the computational cost and enhances response time. The performance of the proposed system is compared with conventional algorithms' with the use of recall, precision, accuracy, F1 score, the relative error in total energy and Mean Absolute Error (MAE). These evaluation metrics are calculated on the power consumption of top priority appliances of Reference Energy Disaggregation Dataset (REDD). The proposed architecture with an overall accuracy of 91.2 and MAE of 25.23 outperforms conventional methods for all electrical appliances. It has been showcased through a series of experiments that feature extraction and event-based approaches for NILM can readily be replaced with E2E deep learning techniques allowing simpler and cost-efficient implementation pathways.

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Section: B the E2e ML Modelmentioning

confidence: 99%