System Identification-informed Transparent and Explainable Machine Learning with Application to Power Consumption Forecasting-Reference-Cited by-同舟云学术

System Identification-informed Transparent and Explainable Machine Learning with Application to Power Consumption Forecasting

Published:2023-07-19 Issue: Volume: Page:
ISSN:
Container-title:2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)
language:
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Author:

Wei Hua-Liang¹

Affiliation:

1. Centre for Signal Processing and Cpmplex Systems The University of Sheffield,Engineering Energy Institute; Centre of Machine Intelligence,Dept. Automatic Control and Systems,Sheffield,UK

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10252163/10252090/10252535.pdf?arnumber=10252535

Reference34 articles.

1. System Identification and Data‐Driven Forecasting of AE Index and Prediction Uncertainty Analysis Using a New Cloud‐NARX Model

2. Complex systems modelling for statistical forecasting of winter North Atlantic atmospheric variability: A new approach to North Atlantic seasonal forecasting

3. Comparison of Machine Learning Algorithms for the Power Consumption Prediction : - Case Study of Tetouan city –

4. Sparse, Interpretable and Transparent Predictive Model Identification for Healthcare Data Analysis

5. NARMAX Model as a Sparse, Interpretable and Transparent Machine Learning Approach for Big Medical and Healthcare Data Analysis

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