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Advances in AI-Based (AI+) Energy and Resource Research
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Advances in AI-Based (AI+) Energy and Resource Research

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 April 2023) | Viewed by 4551

Special Issue Editors

Prof. Dr. Xiaoping Ma

E-Mail Website
Guest Editor
Artificial Intelligence Research Institute, China University of Mining and Technology, Xuzhou 221000, China
Interests: control theory; network control; AI+
Special Issues, Collections and Topics in MDPI journals
Dr. Difei Zhao

E-Mail Website
Guest Editor
Artificial Intelligence Research Institute, China University of Mining and Technology, Xuzhou 221000, China
Interests: unconventional oil and gas geology; AI+ energy technology; sedimentary petrology
Special Issues, Collections and Topics in MDPI journals
Dr. Qinxia Wang

E-Mail Website
Guest Editor
Artificial Intelligence Research Institute, China University of Mining and Technology, Xuzhou 221000, China
Interests: multi-modality image processing; fault detection; deep learning

Special Issue Information

Dear Colleagues,

Presently, global energy and resources research is driven by many new technologies. Combinations of artificial intelligence and other disciplines in the field of energy and resources research has greatly promoted its progress. In this context, it is of great significance to take an up-to-date survey of the new advances in AI-based (AI+) energy and resource research.

Areas relevant to advances in AI-based (AI+) energy and resource research include, but are not limited to, new understandings of intelligent mining, mathematical geology, big data for energy and resources, and AI-based applied technologies.

This Special Issue will publish high-quality, original research papers from the following fields: intelligent mining; AI-based geological surveys; new applications of AI+ technologies; intelligent equipment; macro research on energy and resource systems; energy security; energy- and resource-related simulation studies.

Prof. Dr. Xiaoping Ma
Dr. Difei Zhao
Dr. Qinxia Wang
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.

Keywords

  • AI+
  • energy and resources
  • AI-based geology
  • intelligent mining
  • simulation
  • AI+ applied sciences
  • intelligent building

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Published Papers (2 papers)

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Research

27 pages, 6162 KiB  
Article
Two-Stage Short-Term Power Load Forecasting Based on SSA–VMD and Feature Selection
by Weijian Huang, Qi Song and Yuan Huang
Appl. Sci. 2023, 13(11), 6845; https://doi.org/10.3390/app13116845 - 5 Jun 2023
Cited by 6 | Viewed by 1640
Abstract
Short-term power load forecasting is of great significance for the reliable and safe operation of power systems. In order to improve the accuracy of short-term load forecasting, for the problems of random fluctuation in load and the complexity of load-influencing factors, this paper [...] Read more.
Short-term power load forecasting is of great significance for the reliable and safe operation of power systems. In order to improve the accuracy of short-term load forecasting, for the problems of random fluctuation in load and the complexity of load-influencing factors, this paper proposes a two-stage short-term load forecasting method, SSA–VMD-LSTM-MLR-FE (SVLM–FE) based on sparrow search algorithm (SSA), to optimize variational mode decomposition (VMD) and feature engineering (FE). Firstly, an evaluation criterion on the loss of VMD decomposition is proposed, and SSA is used to find the optimal combination of parameters for VMD under this criterion. Secondly, the first stage of forecasting is carried out, and the different components obtained from SSA–VMD are predicted separately, with the high-frequency components input to a long short-term memory network (LSTM) for forecasting and the low-frequency components input to a multiple linear regression model (MLR) for forecasting. Finally, the forecasting values of the components obtained in the first stage are input to the second stage for error correction; factors with a high degree of influence on the load are selected using the Pearson correlation coefficient (PCC) and maximal information coefficient (MIC), and the load value at the moment that has a great influence on the load value at the time to be predicted is selected using autocorrelation function (ACF). The forecasting values of the components are fused with the selected feature values to construct a vector, which is fed into the fully connected layer for forecasting. In this paper, the performance of SVLM–FE is evaluated experimentally on two datasets from two places in China. In Place 1, the RMSE, MAE, and MAPE are 128.169 MW, 102.525 MW, and 1.562%, respectively; in Place 2, the RMSE, MAE, and MAPE are 111.636 MW, 92.291 MW, and 1.426%, respectively. The experimental results show that SVLM–FE has high accuracy and stability. Full article
(This article belongs to the Special Issue Advances in AI-Based (AI+) Energy and Resource Research)
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14 pages, 2654 KiB  
Article
Nickel and Cobalt Price Volatility Forecasting Using a Self-Attention-Based Transformer Model
by Shivam Swarup and Gyaneshwar Singh Kushwaha
Appl. Sci. 2023, 13(8), 5072; https://doi.org/10.3390/app13085072 - 18 Apr 2023
Viewed by 2446
Abstract
Both Nickel and Cobalt have been extensively used in cutting-edge technologies, such as electric vehicle battery manufacturing, stainless steel, and special alloys production. As governments focus on greener solutions for areas such as transportation and energy generation, both metals are increasingly used for [...] Read more.
Both Nickel and Cobalt have been extensively used in cutting-edge technologies, such as electric vehicle battery manufacturing, stainless steel, and special alloys production. As governments focus on greener solutions for areas such as transportation and energy generation, both metals are increasingly used for energy storage purposes. However, their price uncertainty makes for an interesting case in the modern economy. This study focuses on the price volatility forecasting of Nickel and Cobalt using ANN (Artificial Neural Network) built on a special class of Transformer models used for multi-step ahead forecasts. Our results suggest that the given model is only slightly better in predictive accuracy compared to traditional sequential deep learning models such as BiLSTM (Bidirectional Long Short-Term Memory) and GRUs (gated recurrent units). Moreover, our findings also show that, like conventional approaches, in-sample behavior does not guarantee out-of-sample behavior. The given study could be utilized by industry participants for an inquiry into new and efficient ways to forecast and identify temporal-based structural patterns in commodity-based time series. Full article
(This article belongs to the Special Issue Advances in AI-Based (AI+) Energy and Resource Research)
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