Variational Bayesian Network with Information Interpretability Filtering for Air Quality Forecasting
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Wang, Guoyang & Awad, Omar I. & Liu, Shiyu & Shuai, Shijin & Wang, Zhiming, 2020. "NOx emissions prediction based on mutual information and back propagation neural network using correlation quantitative analysis," Energy, Elsevier, vol. 198(C).
- Guangyuan Xing & Er-long Zhao & Chengyuan Zhang & Jing Wu & Giancarlo Consolo, 2021. "A Decomposition-Ensemble Approach with Denoising Strategy for PM2.5 Concentration Forecasting," Discrete Dynamics in Nature and Society, Hindawi, vol. 2021, pages 1-13, April.
- Xue-Bo Jin & Wei-Zhen Zheng & Jian-Lei Kong & Xiao-Yi Wang & Yu-Ting Bai & Ting-Li Su & Seng Lin, 2021. "Deep-Learning Forecasting Method for Electric Power Load via Attention-Based Encoder-Decoder with Bayesian Optimization," Energies, MDPI, vol. 14(6), pages 1-18, March.
- Jianxian Cai & Xun Dai & Li Hong & Zhitao Gao & Zhongchao Qiu, 2020. "An Air Quality Prediction Model Based on a Noise Reduction Self-Coding Deep Network," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-12, May.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Xin Xu & Cheng-Cai Yang & Yang Xiao & Jian-Lei Kong, 2023. "A Fine-Grained Recognition Neural Network with High-Order Feature Maps via Graph-Based Embedding for Natural Bird Diversity Conservation," IJERPH, MDPI, vol. 20(6), pages 1-20, March.
- Yu-Ting Bai & Wei Jia & Xue-Bo Jin & Ting-Li Su & Jian-Lei Kong & Zhi-Gang Shi, 2023. "Nonstationary Time Series Prediction Based on Deep Echo State Network Tuned by Bayesian Optimization," Mathematics, MDPI, vol. 11(6), pages 1-22, March.
- Jianlei Kong & Yang Xiao & Xuebo Jin & Yuanyuan Cai & Chao Ding & Yuting Bai, 2023. "LCA-Net: A Lightweight Cross-Stage Aggregated Neural Network for Fine-Grained Recognition of Crop Pests and Diseases," Agriculture, MDPI, vol. 13(11), pages 1-23, October.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Venkataramana Veeramsetty & Arjun Mohnot & Gaurav Singal & Surender Reddy Salkuti, 2021. "Short Term Active Power Load Prediction on A 33/11 kV Substation Using Regression Models," Energies, MDPI, vol. 14(11), pages 1-21, May.
- Xue-Bo Jin & Wen-Tao Gong & Jian-Lei Kong & Yu-Ting Bai & Ting-Li Su, 2022. "PFVAE: A Planar Flow-Based Variational Auto-Encoder Prediction Model for Time Series Data," Mathematics, MDPI, vol. 10(4), pages 1-17, February.
- Wang, Xinlin & Yao, Zhihao & Papaefthymiou, Marios, 2023. "A real-time electrical load forecasting and unsupervised anomaly detection framework," Applied Energy, Elsevier, vol. 330(PA).
- Vladimir Franki & Darin Majnarić & Alfredo Višković, 2023. "A Comprehensive Review of Artificial Intelligence (AI) Companies in the Power Sector," Energies, MDPI, vol. 16(3), pages 1-35, January.
- Dimitrios Kontogiannis & Dimitrios Bargiotas & Aspassia Daskalopulu & Lefteri H. Tsoukalas, 2021. "A Meta-Modeling Power Consumption Forecasting Approach Combining Client Similarity and Causality," Energies, MDPI, vol. 14(19), pages 1-19, September.
- Andi A. H. Lateko & Hong-Tzer Yang & Chao-Ming Huang, 2022. "Short-Term PV Power Forecasting Using a Regression-Based Ensemble Method," Energies, MDPI, vol. 15(11), pages 1-21, June.
- Krishna Prakash N. & Jai Govind Singh, 2023. "Electricity price forecasting using hybrid deep learned networks," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1750-1771, November.
- Thibaut Th'eate & Antonio Sutera & Damien Ernst, 2023. "Matching of Everyday Power Supply and Demand with Dynamic Pricing: Problem Formalisation and Conceptual Analysis," Papers 2301.11587, arXiv.org.
- Yuan Huang & Junhao Yu & Xiaohong Dai & Zheng Huang & Yuanyuan Li, 2022. "Air-Quality Prediction Based on the EMD–IPSO–LSTM Combination Model," Sustainability, MDPI, vol. 14(9), pages 1-18, April.
- Wang, Zhihong & Luo, Kangwei & Yu, Hongsen & Feng, Kai & Ding, Hang, 2024. "NOx Emission prediction of heavy-duty diesel vehicles based on Bayesian optimization -Gated Recurrent Unit algorithm," Energy, Elsevier, vol. 292(C).
- Xue-Bo Jin & Wei-Zhen Zheng & Jian-Lei Kong & Xiao-Yi Wang & Min Zuo & Qing-Chuan Zhang & Seng Lin, 2021. "Deep-Learning Temporal Predictor via Bidirectional Self-Attentive Encoder–Decoder Framework for IOT-Based Environmental Sensing in Intelligent Greenhouse," Agriculture, MDPI, vol. 11(8), pages 1-25, August.
- Wang, Jianxing & Guo, Lili & Zhang, Chengying & Song, Lei & Duan, Jiangyong & Duan, Liqiang, 2020. "Thermal power forecasting of solar power tower system by combining mechanism modeling and deep learning method," Energy, Elsevier, vol. 208(C).
- Ali Asghar Heidari & Mehdi Akhoondzadeh & Huiling Chen, 2022. "A Wavelet PM2.5 Prediction System Using Optimized Kernel Extreme Learning with Boruta-XGBoost Feature Selection," Mathematics, MDPI, vol. 10(19), pages 1-35, September.
- Wenbing Chang & Xu Chen & Zhao He & Shenghan Zhou, 2023. "A Prediction Hybrid Framework for Air Quality Integrated with W-BiLSTM(PSO)-GRU and XGBoost Methods," Sustainability, MDPI, vol. 15(22), pages 1-24, November.
- Jia, Xiongjie & Sang, Yichen & Li, Yanjun & Du, Wei & Zhang, Guolei, 2022. "Short-term forecasting for supercharged boiler safety performance based on advanced data-driven modelling framework," Energy, Elsevier, vol. 239(PE).
- Xudong Pang & Xiangchen Lu & Hao Ding & Josep M. Guerrero, 2022. "Construction of Smart Grid Load Forecast Model by Edge Computing," Energies, MDPI, vol. 15(9), pages 1-16, April.
- Shahzad Aslam & Nasir Ayub & Umer Farooq & Muhammad Junaid Alvi & Fahad R. Albogamy & Gul Rukh & Syed Irtaza Haider & Ahmad Taher Azar & Rasool Bukhsh, 2021. "Towards Electric Price and Load Forecasting Using CNN-Based Ensembler in Smart Grid," Sustainability, MDPI, vol. 13(22), pages 1-28, November.
- Krzysztof Lalik & Filip Wątorek, 2021. "Predictive Maintenance Neural Control Algorithm for Defect Detection of the Power Plants Rotating Machines Using Augmented Reality Goggles," Energies, MDPI, vol. 14(22), pages 1-18, November.
- Tiago Pinto, 2023. "Artificial Intelligence as a Booster of Future Power Systems," Energies, MDPI, vol. 16(5), pages 1-4, February.
- Wellcome Peujio Jiotsop-Foze & Adrián Hernández-del-Valle & Francisco Venegas-MartÃnez, 2024. "Electrical Load Forecasting to Plan the Increase in Renewable Energy Sources and Electricity Demand: a CNN-QR-RTCF and Deep Learning Approach," International Journal of Energy Economics and Policy, Econjournals, vol. 14(4), pages 186-194, July.
More about this item
Keywords
multiple factors; time series forecasting; deep learning; interpretability; data filtering; variational Bayesian;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:11:y:2023:i:4:p:837-:d:1060152. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.