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Applied Sciences
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Machine Learning for Energy and Industrial Datasets Forecasting: Volume II
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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 (20 April 2023) | Viewed by 4014

Special Issue Editors

Prof. Dr. Kuo-Ping Lin

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Guest Editor
Department of Industrial Engineering and Enterprise Information, Tunghai University, Taichung, Taiwan
Interests: heuristic algorithm; artificial intelligence; system dynamics; reliability engineering; intelligent decision system
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 2400 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 (2 papers)

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Research

20 pages, 5189 KiB  
Article
A Hybrid Grey Wolf Optimization Algorithm Using Robust Learning Mechanism for Large Scale Economic Load Dispatch with Vale-Point Effect
by Tzu-Ching Tai, Chen-Cheng Lee and Cheng-Chien Kuo
Appl. Sci. 2023, 13(4), 2727; https://0-doi-org.brum.beds.ac.uk/10.3390/app13042727 - 20 Feb 2023
Cited by 7 | Viewed by 1621
Abstract
This paper proposes a new hybrid algorithm for grey wolf optimization (GWO) integrated with a robust learning mechanism to solve the large-scale economic load dispatch (ELD) problem. The robust learning grey wolf optimization (RLGWO) algorithm imitates the hunting behavior and social hierarchy of [...] Read more.
This paper proposes a new hybrid algorithm for grey wolf optimization (GWO) integrated with a robust learning mechanism to solve the large-scale economic load dispatch (ELD) problem. The robust learning grey wolf optimization (RLGWO) algorithm imitates the hunting behavior and social hierarchy of grey wolves in nature and is reinforced by robust tolerance-based adjust searching direction and opposite-based learning. This technique could effectively prevent search agents from being trapped in local optima and also generate potential candidates to obtain a feasible solution. Several constraints of power generators, such as generation limits, local demand, valve-point loading effect, and transmission losses, are considered in practical operation. Five test systems are used to evaluate the effectiveness and robustness of the proposed algorithm in solving the ELD problem. The simulation results clearly reveal the superiority and feasibility of RLGWO to find better solutions in terms of fuel cost and computational efficiency when compared with the previous literature. Full article
(This article belongs to the Special Issue Machine Learning for Energy and Industrial Datasets Forecasting: Volume II)
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25 pages, 9364 KiB  
Article
Short-Term Load Forecasting of the Greek Electricity System
by George Stamatellos and Tassos Stamatelos
Appl. Sci. 2023, 13(4), 2719; https://0-doi-org.brum.beds.ac.uk/10.3390/app13042719 - 20 Feb 2023
Cited by 3 | Viewed by 1569
Abstract
Short-term load forecasting is an essential instrument in power system planning, operation, and control. It is involved in the scheduling of capacity dispatch, system reliability analysis, and maintenance planning for turbines and generators. Despite the high level of development of advanced types of [...] Read more.
Short-term load forecasting is an essential instrument in power system planning, operation, and control. It is involved in the scheduling of capacity dispatch, system reliability analysis, and maintenance planning for turbines and generators. Despite the high level of development of advanced types of machine learning models in commercial codes and platforms, the prediction accuracy needs further improvement, especially in certain short, problematic time periods. To this end, this paper employs public domain electric load data and typical climatic data to make 24-hour-ahead hourly electricity load forecasts of the Greek system based on two types of robust, standard feed-forward artificial neural networks. The accuracy and stability of the prediction performance are measured by means of the modeling error values. The current prediction accuracy levels of mean absolute percentage error, mean value μ = 2.61% with σ = 0.33% of the Greek system operator for 2022, attained with noon correction, are closely matched with a simple feed-forward artificial neural network, attaining mean value μ = 3.66% with σ = 0.30% with true 24-hour-ahead prediction. Specific instances of prediction failure in cases of unexpectedly high or low energy demand are analyzed and discussed. The role of the structure and quality of input data of the training datasets is demonstrated to be the most critical factor in further increasing the accuracy and reliability of forecasting. Full article
(This article belongs to the Special Issue Machine Learning for Energy and Industrial Datasets Forecasting: Volume II)
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