Special Issue "Machine Learning for Energy and Industrial Datasets Forecasting: Volume II"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".

Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 396

Special Issue Editors

Prof. Dr. Kuo-Ping Lin
E-Mail Website
Guest Editor
Department of Industrial Engineering and Enterprise Information, Tunghai University, Taichung 40704, Taiwan
Interests: artificial intelligence; machine learning; e-commence; fuzzy system; neuro-fuzzy systems; system optimization
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Chien-Chih Wang
E-Mail Website
Guest Editor
Department of Industrial Engineering and Management, Ming Chi University of Technology, New Taipei City 24301, Taiwan
Interests: machine learning and AI applications; process quality control and engineering optimization; machine vision and inspection
Special Issues, Collections and Topics in MDPI journals
Dr. Chih-Hung Jen
E-Mail Website
Guest Editor
Department of Information Management, Lunghwa University of Science and Technology, Taoyuan City 333326, Taiwan
Interests: quality engineering; process control; machine learning and deep learning

Special Issue Information

Dear Colleagues,

Entitled “Machine Learning for Energy and Industrial Datasets Forecasting II”, this Special Issue aims to investigate the role of machine learning in energy and industrial datasets forecasting, which is an essential component of energy, manufacturing, and various industries in intelligent manufacturing. Energy and industrial datasets include loading forecast, electricity price forecast, wind power forecast, solar power forecast, demand forecast, production forecast, maintaining forecast, etc. Not only can accurate forecasting support investment profitability analysis and production planning and control forecasting, but it also enables smart strategies to be applied to price bidding and risk management, in addition to optimizing the grid operation. However, building a reliable forecasting solution has always remained a challenge. Machine learning is one of the techniques for energy and industrial datasets forecasting. With machine learning forecasting, processors learn from mining loads of energy and industrial data without human interference. Extrapolative analysis and algorithms include support vector machines (SVMs), least-square support vector machines (LSSVMs), recurrent neural networks (RNNs), Bayesian neural networks (BNNs), CART regression trees, Gaussian processes (GPs), generalized regression neural networks (GRNNs), multi-layer perceptron (MLP), deep learning (DL), etc. We seek new research contributions based on novel machine learning techniques for energy and industrial datasets forecasting.

Prof. Dr. Kuo-Ping Lin
Prof. Dr. Chien-Chih Wang
Dr. Chih-Hung Jen
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2300 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers

There is no accepted submissions to this special issue at this moment.
Back to TopTop