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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: closed (31 March 2022) | Viewed by 38090

Special Issue Editors

Prof. Dr. Yi Liang

E-Mail Website
Guest Editor
School of Management, Hebei GEO University, Shijiazhuang 050031, China
Interests: low carbon energy management; artificial intelligence applications; power load forecasting
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Dongxiao Niu

E-Mail
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

E-Mail Website
Guest Editor
Department of Information Management, Asia Eastern University of Science and Technology, Taipei 22064, 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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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. Sustainability 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.

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26 pages, 956 KiB  
Article
Spatial Correlation and Influencing Factors of Environmental Regulation Intensity in China
by Lili Feng, Jingchen Shao, Lin Wang and Wenjun Zhou
Sustainability 2022, 14(11), 6504; https://0-doi-org.brum.beds.ac.uk/10.3390/su14116504 - 26 May 2022
Cited by 1 | Viewed by 1258
Abstract
In this study, we examined the spatial difference of environmental regulation intensity in 30 provinces (autonomous regions and municipalities directly under the central government) of China. It was found that there were significant differences in environmental regulation intensity in the four regions, with [...] Read more.
In this study, we examined the spatial difference of environmental regulation intensity in 30 provinces (autonomous regions and municipalities directly under the central government) of China. It was found that there were significant differences in environmental regulation intensity in the four regions, with a decreasing trend of “west–central–northeast–east” on the whole. Applying the Theil index showed that intra-regional differences accounted for more than 85% of the overall differences in environmental regulation intensity. Goble Moran’s I index was used to verify the spatial correlation of China’s environmental regulation. It was found that the p-value of Goble Moran’s I index was less than 10% in 7 years from 2010 to 2019. It was verified that the environmental regulation intensity in China has had a spatial correlation. In addition, a positive spatial correlation between the environmental regulation intensity in each province was found, indicating that an increase in the environmental regulation intensity of one province will lead to an increase in the intensity of environmental regulation in neighboring provinces. Finally, through the construction of a spatial Markov model to test the spillover effect of environmental regulation intensity in China, it was found that the local environmental regulation intensity will change to different degrees when there are spatial differences in the intensity of environmental regulation in neighboring provinces. This research will be helpful for provincial governments to formulate appropriate environmental regulation targets based on regional characteristics, which is of great significance for China’s and other countries’ green economic development and other countries to solve the contradiction between environmental pollution and economic development. Full article
(This article belongs to the Special Issue Advanced Intelligent Technologies in Sustainable Energy Forecasting and Economical Applications)
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24 pages, 25572 KiB  
Article
Cloud Cover Forecast Based on Correlation Analysis on Satellite Images for Short-Term Photovoltaic Power Forecasting
by Yongju Son, Yeunggurl Yoon, Jintae Cho and Sungyun Choi
Sustainability 2022, 14(8), 4427; https://0-doi-org.brum.beds.ac.uk/10.3390/su14084427 - 08 Apr 2022
Cited by 11 | Viewed by 2186
Abstract
Photovoltaic power generation must be predicted to counter the system instability caused by an increasing number of photovoltaic power-plant connections. In this study, a method for predicting the cloud volume and power generation using satellite images is proposed. Generally, solar irradiance and cloud [...] Read more.
Photovoltaic power generation must be predicted to counter the system instability caused by an increasing number of photovoltaic power-plant connections. In this study, a method for predicting the cloud volume and power generation using satellite images is proposed. Generally, solar irradiance and cloud cover have a high correlation. However, because the predicted solar irradiance is not provided by the Meteorological Administration or a weather site, cloud cover can be used instead of the predicted solar radiation. A lot of information, such as the direction and speed of movement of the cloud is contained in the satellite image. Therefore, the spatio-temporal correlation of the cloud is obtained from satellite images, and this correlation is presented pictorially. When the learning is complete, the current satellite image can be entered at the current time and the cloud value for the desired time can be obtained. In the case of the predictive model, the artificial neural network (ANN) model with the identical hyperparameters or setting values is used for data performance evaluation. Four cases of forecasting models are tested: cloud cover, visible image, infrared image, and a combination of the three variables. According to the result, the multivariable case showed the best performance for all test periods. Among single variable models, cloud cover presented a fair performance for short-term forecasting, and visible image presented a good performance for ultra-short-term forecasting. Full article
(This article belongs to the Special Issue Advanced Intelligent Technologies in Sustainable Energy Forecasting and Economical Applications)
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24 pages, 2060 KiB  
Article
Research on the Impact of Economic Policy Uncertainty on Enterprises’ Green Innovation—Based on the Perspective of Corporate Investment and Financing Decisions
by Wenjun Zhou, Xiaorong Huang, Hao Dai, Yuanmeng Xi, Zhansheng Wang and Long Chen
Sustainability 2022, 14(5), 2627; https://0-doi-org.brum.beds.ac.uk/10.3390/su14052627 - 24 Feb 2022
Cited by 17 | Viewed by 3137
Abstract
Improving enterprises’ green innovation ability is beneficial to realize the “win–win” of economic development and environmental protection. As the global economic situation is complex and volatile, economic policies changed frequently. Will the rising uncertainty of economic policies affect enterprises’ green innovation? Taking China’s [...] Read more.
Improving enterprises’ green innovation ability is beneficial to realize the “win–win” of economic development and environmental protection. As the global economic situation is complex and volatile, economic policies changed frequently. Will the rising uncertainty of economic policies affect enterprises’ green innovation? Taking China’s A-share-listed companies from 2008 to 2019 as the research sample, the Baker index based on news media and network information is used to measure the uncertainty of national economic policy, and the official exchange index based on the complex network is used to measure the uncertainty of economic policy in prefecture-level cities. It is found that there is an inverted U-shaped relationship between economic policy uncertainty and firms’ green innovation capability. Moreover, the uncertainty index of national macroeconomic policy is mostly on the left side of the inverted U shape, which can promote the improvement of enterprises’ green innovation ability. However, too frequent changes in regional economic policies will inhibit enterprises’ green innovation ability. This paper further analyzes the moderating effect of financialization of investment behavior and financing constraint on the impact of economic policy uncertainty on green innovation of enterprises from the perspective of investment and financing behavior choice. It is found that the impact of economic policy uncertainty on green innovation is more obvious for firms with low financing constraints and low financialization. Full article
(This article belongs to the Special Issue Advanced Intelligent Technologies in Sustainable Energy Forecasting and Economical Applications)
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17 pages, 7330 KiB  
Article
Fault Detection of Wind Turbine Blades Using Multi-Channel CNN
by Meng-Hui Wang, Shiue-Der Lu, Cheng-Che Hsieh and Chun-Chun Hung
Sustainability 2022, 14(3), 1781; https://0-doi-org.brum.beds.ac.uk/10.3390/su14031781 - 04 Feb 2022
Cited by 21 | Viewed by 2658
Abstract
This study utilized the multi-channel convolutional neural network (MCNN) and applied it to wind turbine blade and blade angle fault detection. The proposed approach automatically and effectively captures fault characteristics from the imported original vibration signals and identifies their state in multiple convolutional [...] Read more.
This study utilized the multi-channel convolutional neural network (MCNN) and applied it to wind turbine blade and blade angle fault detection. The proposed approach automatically and effectively captures fault characteristics from the imported original vibration signals and identifies their state in multiple convolutional neural network (CNN) models. The result obtained from each model is sent to the output layer, which is a maximum output network (MAXNET), to compute the most accurate state. First, in terms of wind turbine blade state detection, this paper builds blade models based on the normal state and three common fault types, including blade angle anomaly, blade surface damage, and blade breakage. Vibration signals are employed for fault detection. The proposed wind turbine fault diagnosis approach adopts a triaxial vibration transducer and frame grabber to capture vibration signals and then applies the new MCNN algorithm to identify the state. The test results show that the proposed approach could deliver up to 87.8% identification accuracy for four fault types of large wind turbine blades. Full article
(This article belongs to the Special Issue Advanced Intelligent Technologies in Sustainable Energy Forecasting and Economical Applications)
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15 pages, 1549 KiB  
Article
Residential Electricity Load Forecasting Based on Fuzzy Cluster Analysis and LSSVM with Optimization by the Fireworks Algorithm
by Xinyue Zhao, Baoxing Shen, Lin Lin, Daohong Liu, Meng Yan and Gengyin Li
Sustainability 2022, 14(3), 1312; https://0-doi-org.brum.beds.ac.uk/10.3390/su14031312 - 24 Jan 2022
Cited by 12 | Viewed by 2021
Abstract
As the construction of the energy internet progresses, the proportion of residential electricity consumption in end-use energy consumption is increasing, the peak load on the grid is growing year on year, and seasonal and regional peak power supply tensions, mainly for residential electricity [...] Read more.
As the construction of the energy internet progresses, the proportion of residential electricity consumption in end-use energy consumption is increasing, the peak load on the grid is growing year on year, and seasonal and regional peak power supply tensions, mainly for residential electricity consumption, have become common problems across the country. Accurate residential load forecasting can provide strong data support for the operation of electricity demand response and the incentive setting of the response. For the accuracy and stability of residential electricity load forecasting, a forecasting model is presented in this paper based on fuzzy cluster analysis (FC), least-squares support vector machine (LSSVM), and a fireworks algorithm (FWA). First of all, to reduce the redundancy of input data, it is necessary to reduce the dimension of data features. Then, FWA is used to optimize the arguments γ and σ2 of LSSVM, where γ is the penalty factor and σ2 denotes the kernel width. Finally, a load forecasting method of FC–FWA–LSSVM is developed. Relevant data from Beijing, China, are selected for training tests to demonstrate the effectiveness of the proposed model. The results show that the FC–FWA–LSSVM hybrid model proposed in this paper has high accuracy in residential power load forecasting, and the model has good stability and versatility. Full article
(This article belongs to the Special Issue Advanced Intelligent Technologies in Sustainable Energy Forecasting and Economical Applications)
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19 pages, 2741 KiB  
Article
Electricity Substitution Potential Prediction Based on Tent-CSO-CG-SSA-Improved SVM—A Case Study of China
by Jinqiang Geng, Weigao Meng and Qiaoran Yang
Sustainability 2022, 14(2), 853; https://0-doi-org.brum.beds.ac.uk/10.3390/su14020853 - 12 Jan 2022
Cited by 5 | Viewed by 1473
Abstract
Nowadays, fossil energy continues to dominate China’s energy usage; its inefficient use and large crude emissions of coal and fuel oil in its end-consumption have brought about great pressure to reduce emissions. Electrical power substitution as a development strategy is an important step [...] Read more.
Nowadays, fossil energy continues to dominate China’s energy usage; its inefficient use and large crude emissions of coal and fuel oil in its end-consumption have brought about great pressure to reduce emissions. Electrical power substitution as a development strategy is an important step toward achieving sustainable development, the transformation of the end-use energy consumption structure, and double carbon goals. To better guide the broad promotion of electrical power substitution, and to offer theoretical support for its development, this paper quantifies the amount of electrical power substitution and the influencing factors that affect the potential of electrical energy substitution. This paper proposes a hybrid model, combining Tent chaos mapping (Tent), chicken swarm optimization (CSO), Cauchy–Gaussian mutation (CG), the sparrow search algorithm (SSA), and a support vector machine (SVM), as a Tent-CSO-CG-SSA-SVM model, which first uses the method of Tent chaos mapping to initialize the sparrow population in order to increase population diversity and improve the search ability of the algorithm. Then, the CSO is introduced to update the positions of sparrows, and the CG method is introduced to make the algorithm jump out of the local optimum, in order to improve the global search ability of the SSA. Finally, the final electrical power substitution potential prediction model is obtained by optimizing the SVM through a multi-algorithm combination approach. To verify the validity of the model, two regions in China were used as case studies for the prediction analysis of electrical energy substitution potential, and the prediction results were compared with multiple models. The results of the study show that Tent-CSO-CG-SSA-SVM offers a good improvement in prediction accuracy, and that Tent-CSO-CG-SSA-SVM is a promising method for the prediction of electrical power substitution potential. Full article
(This article belongs to the Special Issue Advanced Intelligent Technologies in Sustainable Energy Forecasting and Economical Applications)
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16 pages, 16730 KiB  
Article
A Novel CNC Milling Energy Consumption Prediction Method Based on Program Parsing and Parallel Neural Network
by Jianhua Cao, Xuhui Xia, Lei Wang, Zelin Zhang and Xiang Liu
Sustainability 2021, 13(24), 13918; https://0-doi-org.brum.beds.ac.uk/10.3390/su132413918 - 16 Dec 2021
Cited by 5 | Viewed by 2089
Abstract
Accurate and rapid prediction of the energy consumption of CNC machining is an effective means to realize the lean management of CNC machine tools energy consumption as well as to achieve the sustainable development of the manufacturing industry. Aiming at the drawbacks of [...] Read more.
Accurate and rapid prediction of the energy consumption of CNC machining is an effective means to realize the lean management of CNC machine tools energy consumption as well as to achieve the sustainable development of the manufacturing industry. Aiming at the drawbacks of existing CNC milling energy consumption prediction methods in terms of efficiency and precision, a novel milling energy consumption prediction method based on program parsing and parallel neural network is proposed. Firstly, the relationship between CNC program and energy consumption of CNC machine tool is analyzed. Based on the structural characteristics of the CNC program, an automatic parsing algorithm for the CNC program is proposed. Moreover, based on the improved parallel neural network, the mapping relationship between the energy consumption parameters of each CNC instruction and the milling energy consumption is constructed. Finally, the proposed method is compared with the literature to verify the superiority of the proposed method in terms of prediction efficiency and accuracy, and the practicability of the method is verified through the case study. The proposed method lays the foundation for efficient and low-consumption process planning and energy efficiency improvement of machine tools and is conducive to the sustainable development of the environment. Full article
(This article belongs to the Special Issue Advanced Intelligent Technologies in Sustainable Energy Forecasting and Economical Applications)
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17 pages, 3716 KiB  
Article
Research on Substation Project Cost Prediction Based on Sparrow Search Algorithm Optimized BP Neural Network
by Xiaomin Xu, Luyao Peng, Zhengsen Ji, Shipeng Zheng, Zhuxiao Tian and Shiping Geng
Sustainability 2021, 13(24), 13746; https://0-doi-org.brum.beds.ac.uk/10.3390/su132413746 - 13 Dec 2021
Cited by 24 | Viewed by 2557
Abstract
The prediction of power grid engineering cost is the basis of fine management of power grid engineering, and accurate prediction of substation engineering cost can effectively ensure the fine operation of engineering funds. With the continuous expansion of the engineering system, the influencing [...] Read more.
The prediction of power grid engineering cost is the basis of fine management of power grid engineering, and accurate prediction of substation engineering cost can effectively ensure the fine operation of engineering funds. With the continuous expansion of the engineering system, the influencing factors and data dimensions of substation project investment are gradually diversified and complex, which further increases the uncertainty and complexity of substation project cost. Based on the concept of substation engineering data space, this paper investigates the influencing factors and constructs the static total investment intelligent prediction model of substation engineering. The emerging swarm intelligence algorithm, sparrow search algorithm (SSA), is used to optimize the parameters of the BP neural network to improve the prediction accuracy and convergence speed of neural network. In order to test the validity of the model, an example analysis is carried out based on the data of a provincial substation project. It was found that the SSA-BP can effectively improve the prediction accuracy and provide new methods and approaches for practical application and research. Full article
(This article belongs to the Special Issue Advanced Intelligent Technologies in Sustainable Energy Forecasting and Economical Applications)
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21 pages, 5347 KiB  
Article
Transmission Mechanism of Stock Price Fluctuation in the Rare Earth Industry Chain
by Yanjing Jia, Chao Ding and Zhiliang Dong
Sustainability 2021, 13(22), 12913; https://0-doi-org.brum.beds.ac.uk/10.3390/su132212913 - 22 Nov 2021
Cited by 5 | Viewed by 1963
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
(This article belongs to the Special Issue Advanced Intelligent Technologies in Sustainable Energy Forecasting and Economical Applications)
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15 pages, 3295 KiB  
Article
Who Will Establish New Trade Relations? Looking for Potential Relationship in International Nickel Trade
by Qiaoran Yang, Zhiliang Dong, Yichi Zhang, Man Li, Ziyi Liang and Chao Ding
Sustainability 2021, 13(21), 11681; https://0-doi-org.brum.beds.ac.uk/10.3390/su132111681 - 22 Oct 2021
Cited by 4 | Viewed by 1610
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
(This article belongs to the Special Issue Advanced Intelligent Technologies in Sustainable Energy Forecasting and Economical Applications)
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19 pages, 1414 KiB  
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
by Yongfang Peng, Yingying Fan and Yi Liang
Sustainability 2021, 13(19), 11079; https://0-doi-org.brum.beds.ac.uk/10.3390/su131911079 - 07 Oct 2021
Cited by 17 | Viewed by 2219
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
(This article belongs to the Special Issue Advanced Intelligent Technologies in Sustainable Energy Forecasting and Economical Applications)
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21 pages, 6122 KiB  
Article
Smart Evaluation of Green Campus Sustainability Considering Energy Utilization
by Hongmei Zhao, Yang Xu, Wei-Chiang Hong, Yi Liang and Dandan Zou
Sustainability 2021, 13(14), 7653; https://0-doi-org.brum.beds.ac.uk/10.3390/su13147653 - 08 Jul 2021
Cited by 5 | Viewed by 2260
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
(This article belongs to the Special Issue Advanced Intelligent Technologies in Sustainable Energy Forecasting and Economical Applications)
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26 pages, 16911 KiB  
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
by Yi Liang, Haichao Wang and Wei-Chiang Hong
Sustainability 2021, 13(11), 5960; https://0-doi-org.brum.beds.ac.uk/10.3390/su13115960 - 25 May 2021
Cited by 36 | Viewed by 2231
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
(This article belongs to the Special Issue Advanced Intelligent Technologies in Sustainable Energy Forecasting and Economical Applications)
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15 pages, 3468 KiB  
Article
Research on Multi-Objective Optimization Model for Hybrid Energy System Considering Combination of Wind Power and Energy Storage
by Jing Wu, Zhongfu Tan, Keke Wang, Yi Liang and Jinghan Zhou
Sustainability 2021, 13(6), 3098; https://0-doi-org.brum.beds.ac.uk/10.3390/su13063098 - 11 Mar 2021
Cited by 8 | Viewed by 1442
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
(This article belongs to the Special Issue Advanced Intelligent Technologies in Sustainable Energy Forecasting and Economical Applications)
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24 pages, 35586 KiB  
Article
A Hybrid Deep Learning Model and Comparison for Wind Power Forecasting Considering Temporal-Spatial Feature Extraction
by Hao Zhen, Dongxiao Niu, Min Yu, Keke Wang, Yi Liang and Xiaomin Xu
Sustainability 2020, 12(22), 9490; https://0-doi-org.brum.beds.ac.uk/10.3390/su12229490 - 15 Nov 2020
Cited by 37 | Viewed by 3764
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
(This article belongs to the Special Issue Advanced Intelligent Technologies in Sustainable Energy Forecasting and Economical Applications)
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Review

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22 pages, 662 KiB  
Review
Analysis and Countermeasures of China’s Green Electric Power Development
by Keke Wang, Dongxiao Niu, Min Yu, Yi Liang, Xiaolong Yang, Jing Wu and Xiaomin Xu
Sustainability 2021, 13(2), 708; https://0-doi-org.brum.beds.ac.uk/10.3390/su13020708 - 13 Jan 2021
Cited by 8 | Viewed by 3396
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
(This article belongs to the Special Issue Advanced Intelligent Technologies in Sustainable Energy Forecasting and Economical Applications)
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