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Processes
Special Issues
Artificial Intelligence and Model Predictive Control for Renewable Energy
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Artificial Intelligence and Model Predictive Control for Renewable Energy

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Energy Systems".

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 5080

Special Issue Editors

Dr. Shaohua Wu

E-Mail Website
Guest Editor
School of Energy and Power Engineering, Dalian University of Technology, Dalian 116024, China
Interests: AI for energy; model predictive control; population balance modelling
Dr. Han Xu

E-Mail Website
Guest Editor
School of Energy and Power Engineering, Dalian University of Technology, Dalian 116024, China
Interests: artificial intelligence; smart energy; chemical kinetics; model predictive control
Dr. Xiang Sun

E-Mail Website
Guest Editor
Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan 430071, China
Interests: numerical simulation of deep water geo-environment; geotechnical modeling in offshore geotechnical engineering; physics-informed neural network method in energy engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the last several decades, the countries worldwide have devoted enormous efforts to developing renewable energies in line with the targets of the Paris Agreement and Sustainable Development Goals. Renewable energy is produced using natural resources that are abundant and able to be constantly renewed, including the sun, wind, water and trees. It can offer substantial cost savings compared with grid-supplied energy and enables businesses to reduce emissions, enhance sustainability credentials and reduce exposure to future price volatility. Unfortunately, the renewable industry suffers a poor yield and high cost. There is a lack of highly efficient production and conversion devices for the renewable fuels. The reaction mechanisms for the renewable fuels are far from being fully understood, to which model and simulation could contribute significantly. Among various modelling techniques, Artificial Intelligence (AI) is the most promising one. AI on everyone's lips right now. It is the fastest growing branch of the high-tech industry and has gained relevance in a wide variety of sectors including material synthesis, medical science, auto pilot as well as energy system design. It has a great potential for the future design of the energy system. Typical areas of application are renewable fuel mechanism construction, highly efficient system design, smart grid or the sector coupling of electricity, heat and transport. Prerequisites for an increased use of AI in the energy system are the digitalization of the energy sector and a correspondingly large set of data that is evaluable. AI helps make the energy industry more efficient and secure by analyzing and evaluating the data volumes.

This special issue will focus on publishing original research works about AI for Renewable Energy. We target specifically the development of novel AI algorithms and their applications in renewable energy industries, incluidng new energy system, energy materials, energy conversion devices and energy chemistry. It also welcomes the review papers covering the state-of-the-art developments of AI technologies for renewable energy system modelling and optimization.

Topics of interest for this Special Issue include but are not limited to:

  • AI for renewable fuel synthesis
  • Data-driven modelling of renewable energy device
  • Advanced optimization technique for energy system
  • Model predictive control of energy system
  • Hybrid AI and physical model for renewable energy related problems
  • Internet of things and digital twining technologies for renewable energy systems
  • Data science in renewable energy
  • Environmental impact assessment based on big data technique

Dr. Shaohua Wu
Dr. Han Xu
Dr. Xiang Sun
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. Processes is an international peer-reviewed open access monthly 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.

Keywords

  • renewable energy
  • artificial intelligence
  • optimization
  • model predictive control
  • fuel synthesis
  • data-driven modelling technique

Published Papers (1 paper)

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Research

28 pages, 7040 KiB  
Article
Machine Learning-Based Method for Predicting Compressive Strength of Concrete
by Daihong Li, Zhili Tang, Qian Kang, Xiaoyu Zhang and Youhua Li
Processes 2023, 11(2), 390; https://doi.org/10.3390/pr11020390 - 27 Jan 2023
Cited by 16 | Viewed by 4554
Abstract
Accurate prediction of the compressive strength of concrete is of great significance to construction quality and progress. In order to understand the current research status in the concrete compressive strength prediction field, a bibliometric analysis of the relevant literature published in this field [...] Read more.
Accurate prediction of the compressive strength of concrete is of great significance to construction quality and progress. In order to understand the current research status in the concrete compressive strength prediction field, a bibliometric analysis of the relevant literature published in this field in the last decade was conducted first. The 3135 journal articles published from 2012 to 2021 in the Web of Science core database were used as the database, and the knowledge map was drawn with the help of the visualisation software CiteSpace 6.1R2 to analyse the field at the macro level in terms of spatial and temporal distribution, hotspot distribution and evolutionary trends, respectively. Afterwards, we go into the detail and divide concrete compressive strength prediction methods into two categories: traditional and machine-learning methods, and introduce the typical methods of each. In addition, a boosting-based ensemble machine-learning algorithm, namely the gradient boosting regression tree (GBRT) algorithm, is proposed for predicting the compressive strength of concrete. 1030 sets of concrete compressive strength test data were collected as the dataset, of which 60% were used to train the model, 20% to validate the model and 20% to test the trained model. The coefficient of determination (R2) of the GBRT model was 0.92, the mean square error (MSE) was 22.09 MPa, and the root mean square error (RMSE) was 4.7 MPa, which is an excellent prediction accuracy compared to prediction models constructed by other machine-learning algorithms. In addition, a five-fold cross-validation analysis was carried out, and the eight input variables were analyzed for their characteristic importance. Full article
(This article belongs to the Special Issue Artificial Intelligence and Model Predictive Control for Renewable Energy)
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