Special Issue "Advanced Intelligent Technologies in Sustainable Energy Forecasting and Economical Applications"

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Energy Sustainability".

Deadline for manuscript submissions: 31 December 2021.

Special Issue Editors

Prof. Dr. Dongxiao Niu
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Guest Editor
School of Economics and Management, North China Electric Power University, Beijing 102206, China
Interests: wind speed forecasting; electric power; power grid
Prof. Dr. Wei-Chiang Hong
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Guest Editor
Department of Information Management, Oriental Institute of Technology, Taipei 220, Taiwan
Interests: short-term load forecasting; intelligent forecasting technologies (e.g., neural networks, knowledge–based expert systems, fuzzy inference systems, evolutionary computation, etc.); hybrid forecasting models (e.g., hybridizing traditional models with intelligent technologies, or hybridizing two or more different models to form a novel forecasting model); novel intelligent methodologies (chaos theory; cloud theory; quantum theory)
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Mengjie Zhang
E-Mail Website
Guest Editor
School of Engineering and Computer Science, Victoria University of Wellington, Wellington 6012, New Zealand
Interests: artificial intelligence; machine learning; big data

Special Issue Information

Dear Colleagues,

Accurate sustainable energy forecasting is an essential issue to achieve higher efficiency and reliability in power system operation and security, energy pricing problems, efficient scheduling and planning of energy supply systems, etc. During past several decades, many energy forecasting models have been proposed, including traditional statistical models (e.g., ARIMA-based models, regression models, exponential smoothing and Kalman filtering models, and Bayesian models) and artificial intelligent models (e.g., ANNs, expert systems, volutionary computation models, support vector regression, LSTM, etc.). However, most of these models often possess theoretical drawbacks which limit them from more satisfactory forecasting performance.

Meanwhile, in recent decades, there has been an important increase in the use of renewable energy sources aiming at reducing greenhouse gas emissions. In this vein, many countries are still implementing new actions to further reduce these emissions, such as the progressive replacement of combustion-engine vehicles by electric vehicles, the transition to fully renewable electric energy systems, and the development of new technologies that allow renewable energy in large quantities. All these actions will change the way that energy systems are operated, both from an economical and a technical point of view. Thus, new approaches are needed for the planning and economics of future energy systems.

Recently, due to the great development of advanced intelligent computing technologies (e.g., quantum computing, chaotic mapping mechanism, cloud mapping mechanism, seasonal mechanism, etc.), many novel hybridized models or models with the combined energy forecasting and economical planning mentioned above are receiving much attention. It is necessary to explore the tendency and development of the modeling methodology by applying these advanced intelligent technologies.

Potential topics include but are not limited to the following:

  • Statistical forecasting models
  • Artificial intelligent models
  • Hybrid (combined) models
  • Evolutionary algorithms
  • Meta-heuristic algorithms
  • Intelligent computing mechanisms (chaotic mapping; quantum computing; cloud mapping, seasonal mechanisms)
  • Energy forecasting
  • Renewable energy
  • Planning, economics
  • Robust optimization
  • Stochastic programming

Prof. Dr. Yi Liang
Prof. Dr. Dongxiao Niu
Prof. Dr. Wei-Chiang Hong
Prof. Dr. Mengjie Zhang
Guest Editors

Manuscript Submission Information

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Published Papers (8 papers)

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Research

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Article
Transmission Mechanism of Stock Price Fluctuation in the Rare Earth Industry Chain
Sustainability 2021, 13(22), 12913; https://0-doi-org.brum.beds.ac.uk/10.3390/su132212913 - 22 Nov 2021
Viewed by 244
Abstract
The transmission of stock price fluctuations of listed companies in the rare earth industry has complex characteristics. Mastering its transmission law is of great meaning to understand the relationship between the upstream and downstream of the rare earth industry chain and market investment. [...] Read more.
The transmission of stock price fluctuations of listed companies in the rare earth industry has complex characteristics. Mastering its transmission law is of great meaning to understand the relationship between the upstream and downstream of the rare earth industry chain and market investment. This article uses the time series of daily closing prices of stocks in the global rare earth industry chain in the past ten years as the research object. The Granger causality test and complex network theory were used to construct the risk transmission network of the industrial chain. We have identified the key stocks in the network of stock price fluctuation in the rare earth industry chain and obtained the transmission path of stock price fluctuation. According to the results: (1) The stocks of Chinese and Japanese listed companies considerably influence the transmission of the stock price fluctuation in the rare earth industry chain. (2) The transmission distance of the stock price fluctuation of each network is relatively small, and the transmission speed is relatively fast. (3) The fluctuation of stock price in the rare earth industry chain is mainly transmitted from the upstream and midstream links to the midstream and downstream links. Full article
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Article
Who Will Establish New Trade Relations? Looking for Potential Relationship in International Nickel Trade
Sustainability 2021, 13(21), 11681; https://0-doi-org.brum.beds.ac.uk/10.3390/su132111681 - 22 Oct 2021
Viewed by 323
Abstract
Nickel ore sand and its concentrate are the main sources of raw nickel materials in various countries. Due to its uneven distribution throughout the world, the international trade of nickel ore sand is also unstable. Looking for potential links in the changing international [...] Read more.
Nickel ore sand and its concentrate are the main sources of raw nickel materials in various countries. Due to its uneven distribution throughout the world, the international trade of nickel ore sand is also unstable. Looking for potential links in the changing international nickel ore trade can help governments find potential partners, make strategic preparations in advance, and quickly find new partners when original trade relationships break down. In this paper, we build an international nickel ore trade network using a link prediction method to find potential trade relations between countries. The results show that China and Italy, China and Denmark, China and Indonesia, and China and India are most likely to establish trade relations within five years. Finally, according to the research results, suggestions regarding the international nickel ore trade are proposed. Full article
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Article
A Green Technological Innovation Efficiency Evaluation of Technology-Based SMEs Based on the Undesirable SBM and the Malmquist Index: A Case of Hebei Province in China
Sustainability 2021, 13(19), 11079; https://0-doi-org.brum.beds.ac.uk/10.3390/su131911079 - 07 Oct 2021
Viewed by 371
Abstract
Small- and medium-sized technology-based enterprises (technology-based SMEs) are an important part of China’s scientific and technological development. To a certain extent, the green technological innovation level of technology-based SMEs plays a significant role in supporting the overall development of social green innovation. Carrying [...] Read more.
Small- and medium-sized technology-based enterprises (technology-based SMEs) are an important part of China’s scientific and technological development. To a certain extent, the green technological innovation level of technology-based SMEs plays a significant role in supporting the overall development of social green innovation. Carrying out research on green technology innovation efficiency evaluations of technology-based SMEs is helpful to find existing problems to provide references for managers. Therefore, this paper proposes an evaluation system based on the undesirable slack based model (SBM) and the Malmquist index model. Firstly, the evaluation index system of the green technological innovation efficiency of technology-based SMEs in Hebei Province was constructed from the perspectives of input and output, in which environmental pollution is included in the evaluation factors of green innovation activities. Then, the undesirable SBM and the Malmquist index model of green technology innovation efficiency evaluation were constructed. Finally, the technological innovation efficiency of technology-based SMEs in Hebei Province in different regions and time nodes was comprehensively calculated and combined with the Malmquist index model to analyze the efficiency changes of technology-based SMEs in Hebei Province over different years. The results show that the overall level of green technological innovation efficiency of technology-based SMEs in Hebei Province is low, and the regional differences in various cities are obvious, but the main trend is rising. The research in this paper can further improve the research results in the field of evaluation of technology-based SMEs and technological innovation efficiency, as well as play an important role in improving the ecological competitiveness and sustainable development capabilities of the products of Hebei’s technology-based SMEs. Full article
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Article
Smart Evaluation of Green Campus Sustainability Considering Energy Utilization
Sustainability 2021, 13(14), 7653; https://0-doi-org.brum.beds.ac.uk/10.3390/su13147653 - 08 Jul 2021
Viewed by 574
Abstract
With the change in energy utilization, a fast and accurate evaluation method is of great importance to promote green campus sustainability. In order to improve the feasibility and timeliness of evaluation, an intelligent evaluation model based on dynamic Bayesian inference and adaptive network [...] Read more.
With the change in energy utilization, a fast and accurate evaluation method is of great importance to promote green campus sustainability. In order to improve the feasibility and timeliness of evaluation, an intelligent evaluation model based on dynamic Bayesian inference and adaptive network fuzzy inference system (DBN-ANFIS) is proposed. Firstly, from the perspective of sustainability and considering the changes in energy utilization, a green campus evaluation index system is constructed from four levels: campus resource utilization, campus environment creation, campus usage management, and campus eco-efficiency. On this basis, the parameters of the adaptive network fuzzy inference system (ANFIS) are optimized based on dynamic Bayesian inference (DBN), so as to apply the modified model to the green campus evaluation work of the Spark big data operation platform. Finally, the scientificity of the model proposed in this paper is verified through example analysis, which is conducive to the real-time and effective evaluation of green campus sustainability and provides scientific and rational decision support to improve its management. Full article
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Article
Sustainable Development Evaluation of Innovation and Entrepreneurship Education of Clean Energy Major in Colleges and Universities Based on SPA-VFS and GRNN Optimized by Chaos Bat Algorithm
Sustainability 2021, 13(11), 5960; https://0-doi-org.brum.beds.ac.uk/10.3390/su13115960 - 25 May 2021
Cited by 2 | Viewed by 592
Abstract
The research on the sustainability evaluation of innovation and entrepreneurship education for clean energy majors in colleges and universities can not only cultivate more and better innovative and entrepreneurial talents for the development of sustainable energy but also provide a reference for the [...] Read more.
The research on the sustainability evaluation of innovation and entrepreneurship education for clean energy majors in colleges and universities can not only cultivate more and better innovative and entrepreneurial talents for the development of sustainable energy but also provide a reference for the sustainable development of innovation and entrepreneurship education for other majors. To achieve systematic and comprehensive scientific evaluation, this paper proposes an evaluation model based on SPA-VFS and Chaos bat algorithm to optimize GRNN. Firstly, the sustainability evaluation index system of innovation and entrepreneurship education for clean energy major in colleges and universities is constructed from the four aspects of the environment, investment, process, and results, and the meaning of each evaluation index is explained; Then, combined with variable fuzzy set evaluation theory (VFS) and set pair analysis theory (SPA), the classical evaluation model based on SPA-VFS is constructed, and the entropy weight method and rank method are coupled to obtain the index weight. The basic bat algorithm is improved by using Tent chaotic mapping, and the chaotic bat algorithm (CBA) is proposed. The generalized regression neural network (GRNN) model is optimized by CBA, and the intelligent evaluation model based on CBA-GRNN is obtained to realize fast real-time calculation; finally, a numerical example is used to verify the scientificity and accuracy of the model proposed in this paper. This study is conducive to a comprehensive evaluation of the sustainability of innovation and entrepreneurship education for clean energy major in colleges and universities, and is conducive to the healthy and sustainable development of innovation and entrepreneurship education for clean energy major in colleges and universities, so as to provide more innovative and entrepreneurial talents for the clean energy industry. Full article
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Article
Research on Multi-Objective Optimization Model for Hybrid Energy System Considering Combination of Wind Power and Energy Storage
Sustainability 2021, 13(6), 3098; https://0-doi-org.brum.beds.ac.uk/10.3390/su13063098 - 11 Mar 2021
Viewed by 472
Abstract
With the development of renewable energy, the grid connection is faced with great pressure, for its generation uncertainty and fluctuation requires larger reserve capacity, and higher operation costs. Energy storage system, as a flexible unit in the energy system, can effectively share the [...] Read more.
With the development of renewable energy, the grid connection is faced with great pressure, for its generation uncertainty and fluctuation requires larger reserve capacity, and higher operation costs. Energy storage system, as a flexible unit in the energy system, can effectively share the reserve pressure of the system by charging and discharging behaviors. In order to further improve the renewable energy utilization, the combination of wind power and energy storage for hybrid energy system is proposed. On considering the power generation characteristics, the objective functions are maximizing the system revenue and minimizing the system energy loss. Combined with the robust optimization theory, the model is transformed and solved. The results show that the application of the energy storage will effectively promote the renewable energy consumption, and the combination of the wind power and energy storage will achieve more effective utilization of the night-time wind power and cut down the total system cost. Full article
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Article
A Hybrid Deep Learning Model and Comparison for Wind Power Forecasting Considering Temporal-Spatial Feature Extraction
Sustainability 2020, 12(22), 9490; https://0-doi-org.brum.beds.ac.uk/10.3390/su12229490 - 15 Nov 2020
Cited by 5 | Viewed by 816
Abstract
The inherent intermittency and uncertainty of wind power have brought challenges in accurate wind power output forecasting, which also cause tricky problems in the integration of wind power to the grid. In this paper, a hybrid deep learning model bidirectional long short term [...] Read more.
The inherent intermittency and uncertainty of wind power have brought challenges in accurate wind power output forecasting, which also cause tricky problems in the integration of wind power to the grid. In this paper, a hybrid deep learning model bidirectional long short term memory-convolutional neural network (BiLSTM-CNN) is proposed for short-term wind power forecasting. First, the grey correlation analysis is utilized to select the inputs for forecasting model; Then, the proposed hybrid model extracts multi-dimension features of inputs to predict the wind power from the temporal-spatial perspective, where the Bi-LSTM model is utilized to mine the bidirectional temporal characteristics while the convolution and pooling operations of CNN are utilized to extract the spatial characteristics from multiple input time series. Lastly, a case study is conducted to verify the superiority of the proposed model. Other deep learning models (Bi-LSTM, LSTM, CNN, LSTM-CNN, CNN-BiLSTM, CNN-LSTM) are also simulated to conduct comparison from three aspects. The results show that the BiLSTM-CNN model has the best accuracy with the lowest RMSE of 2.5492, MSE of 6.4984, MAE of 1.7344 and highest R2 of 0.9929. CNN has the fastest speed with an average computational time of 0.0741s. The hybrid model that mines the spatial feature based on the extracted temporal feature has a better performance than the model mines the temporal feature based on the extracted spatial feature. Full article
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Review

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Review
Analysis and Countermeasures of China’s Green Electric Power Development
Sustainability 2021, 13(2), 708; https://0-doi-org.brum.beds.ac.uk/10.3390/su13020708 - 13 Jan 2021
Viewed by 725
Abstract
The green development of electric power is a key measure to alleviate the shortage of energy supply, adjust the energy structure, reduce environmental pollution and improve energy efficiency. Firstly, the situation and challenges of China’s power green development is analyzed. On this basis, [...] Read more.
The green development of electric power is a key measure to alleviate the shortage of energy supply, adjust the energy structure, reduce environmental pollution and improve energy efficiency. Firstly, the situation and challenges of China’s power green development is analyzed. On this basis, the power green development models are categorized into two typical research objects, which are multi-energy synergy mode, represented by integrated energy systems, and multi-energy combination mode with clean energy participation. The key points of the green power development model with the consumption of new energy as the core are reviewed, and then China’s exploration of the power green development system and the latest research results are reviewed. Finally, the key scientific issues facing China’s power green development are summarized and put forward targeted countermeasures and suggestions. Full article
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