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Prof. Wei-Chiang Hong
Jiangsu Normal University

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0 Forecasting Models
0 Support vector regression
0 meta-heuristic algorithms
0 electric load forecasting
0 short term load forecasting

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Support vector regression
electric load forecasting
Forecasting Models
short term load forecasting
meta-heuristic algorithms
energy demand forecasting

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Journal article
Published: 10 July 2021 in Knowledge-Based Systems
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Accurate electric load forecasting is critical in guaranteeing the efficiency of the load dispatch and supply by a power system, which prevents the wasting of electricity and facilitates energy sustainability. Applications of hybrid intelligent computing methods and swarm-based algorithms with the support vector regression (SVR) model are very promising for solving the problem of premature convergence. This paper presents a novel SVR-based electric load forecasting model by hybridizing variational mode decomposition (VMD), the chaotic mapping mechanism, and the grey wolf optimizer (GWO) in the VMD-SVR-CGWO model to improve the solution searching experiences and to determine the appropriate combination of SVR’s parameters that improve forecasting accuracy. Numerical examples that involve two widely known electric load data sets reveal that the proposed VMD-SVR-CGWO model outperforms other models with respect to forecasting accuracy.

ACS Style

Zichen Zhang; Wei-Chiang Hong. Application of variational mode decomposition and chaotic grey wolf optimizer with support vector regression for forecasting electric loads. Knowledge-Based Systems 2021, 228, 107297 .

AMA Style

Zichen Zhang, Wei-Chiang Hong. Application of variational mode decomposition and chaotic grey wolf optimizer with support vector regression for forecasting electric loads. Knowledge-Based Systems. 2021; 228 ():107297.

Chicago/Turabian Style

Zichen Zhang; Wei-Chiang Hong. 2021. "Application of variational mode decomposition and chaotic grey wolf optimizer with support vector regression for forecasting electric loads." Knowledge-Based Systems 228, no. : 107297.

Journal article
Published: 08 July 2021 in Sustainability
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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.

ACS Style

Hongmei Zhao; Yang Xu; Wei-Chiang Hong; Yi Liang; Dandan Zou. Smart Evaluation of Green Campus Sustainability Considering Energy Utilization. Sustainability 2021, 13, 7653 .

AMA Style

Hongmei Zhao, Yang Xu, Wei-Chiang Hong, Yi Liang, Dandan Zou. Smart Evaluation of Green Campus Sustainability Considering Energy Utilization. Sustainability. 2021; 13 (14):7653.

Chicago/Turabian Style

Hongmei Zhao; Yang Xu; Wei-Chiang Hong; Yi Liang; Dandan Zou. 2021. "Smart Evaluation of Green Campus Sustainability Considering Energy Utilization." Sustainability 13, no. 14: 7653.

Original article
Published: 07 June 2021 in Journal of the Operational Research Society
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Tourism industry played an increasingly prominent role in the socio-economic development in China. Therefore, it is of great significance to forecast the tourism demand, to analyze the development tendency of tourism, to explore the mode of economic linkage, and eventually to reveal the development regulation of tourism industry. In this paper, the empirical mode decomposition, the support vector regression, and the error factor adjustment were combined to forecast the tourism demand of Sanya City. The results demonstrate that the proposed model is more accurate than other models. Meanwhile, this paper also provides the insight analyses of the economic behavior through the tourism demand’s rectangular-ambulatory matrix. The analyses reveal the regulation of tourism industry and the future benefits of Sanya’s tourism.

ACS Style

Guo-Feng Fan; Xiang-Ru Jin; Wei-Chiang Hong. Application of COEMD-S-SVR model in tourism demand forecasting and economic behavior analysis: The case of Sanya City. Journal of the Operational Research Society 2021, 1 -13.

AMA Style

Guo-Feng Fan, Xiang-Ru Jin, Wei-Chiang Hong. Application of COEMD-S-SVR model in tourism demand forecasting and economic behavior analysis: The case of Sanya City. Journal of the Operational Research Society. 2021; ():1-13.

Chicago/Turabian Style

Guo-Feng Fan; Xiang-Ru Jin; Wei-Chiang Hong. 2021. "Application of COEMD-S-SVR model in tourism demand forecasting and economic behavior analysis: The case of Sanya City." Journal of the Operational Research Society , no. : 1-13.

Journal article
Published: 25 May 2021 in Sustainability
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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.

ACS Style

Yi Liang; Haichao Wang; Wei-Chiang Hong. 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, 5960 .

AMA Style

Yi Liang, Haichao Wang, Wei-Chiang Hong. 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.

Chicago/Turabian Style

Yi Liang; Haichao Wang; Wei-Chiang Hong. 2021. "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 13, no. 11: 5960.

Journal article
Published: 05 March 2021 in Sensors
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The escalated growth of the Internet of Things (IoT) has started to reform and reshape our lives. The deployment of a large number of objects adhered to the internet has unlocked the vision of the smart world around us, thereby paving a road towards automation and humongous data generation and collection. This automation and continuous explosion of personal and professional information to the digital world provides a potent ground to the adversaries to perform numerous cyber-attacks, thus making security in IoT a sizeable concern. Hence, timely detection and prevention of such threats are pre-requisites to prevent serious consequences. The survey conducted provides a brief insight into the technology with prime attention towards the various attacks and anomalies and their detection based on the intelligent intrusion detection system (IDS). The comprehensive look-over presented in this paper provides an in-depth analysis and assessment of diverse machine learning and deep learning-based network intrusion detection system (NIDS). Additionally, a case study of healthcare in IoT is presented. The study depicts the architecture, security, and privacy issues and application of learning paradigms in this sector. The research assessment is finally concluded by listing the results derived from the literature. Additionally, the paper discusses numerous research challenges to allow further rectifications in the approaches to deal with unusual complications.

ACS Style

Parushi Malhotra; Yashwant Singh; Pooja Anand; Deep Bangotra; Pradeep Singh; Wei-Chiang Hong. Internet of Things: Evolution, Concerns and Security Challenges. Sensors 2021, 21, 1809 .

AMA Style

Parushi Malhotra, Yashwant Singh, Pooja Anand, Deep Bangotra, Pradeep Singh, Wei-Chiang Hong. Internet of Things: Evolution, Concerns and Security Challenges. Sensors. 2021; 21 (5):1809.

Chicago/Turabian Style

Parushi Malhotra; Yashwant Singh; Pooja Anand; Deep Bangotra; Pradeep Singh; Wei-Chiang Hong. 2021. "Internet of Things: Evolution, Concerns and Security Challenges." Sensors 21, no. 5: 1809.

Original paper
Published: 06 January 2021 in Nonlinear Dynamics
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The bat algorithm (BA) has fast convergence, a simple structure, and strong search ability. However, the standard BA has poor local search ability in the late evolution stage because it references the historical speed; its population diversity also declines rapidly. Moreover, since it lacks a mutation mechanism, it easily falls into local optima. To improve its performance, this paper develops a hybrid approach to improving its evolution mechanism, local search mechanism, mutation mechanism, and other mechanisms. First, the quantum computing mechanism (QCM) is used to update the searching position in the BA to improve its global convergence. Secondly, the X-condition cloud generator is used to help individuals with better fitness values to increase the rate of convergence, with the sorting of individuals after a particular number of iterations; the individuals with poor fitness values are used to implement a 3D cat mapping chaotic disturbance mechanism to increase population diversity and thereby enable the BA to jump out of a local optimum. Thus, a hybrid optimization algorithm—the chaotic cloud quantum bats algorithm (CCQBA)—is proposed. To test the performance of the proposed CCQBA, it is compared with alternative algorithms. The evaluation functions are nine classical comparative functions. The results of the comparison demonstrate that the convergent accuracy and convergent speed of the proposed CCQBA are significantly better than those of the other algorithms. Thus, the proposed CCQBA represents a better method than others for solving complex problems.

ACS Style

Ming-Wei Li; Yu-Tain Wang; Jing Geng; Wei-Chiang Hong. Chaos cloud quantum bat hybrid optimization algorithm. Nonlinear Dynamics 2021, 103, 1167 -1193.

AMA Style

Ming-Wei Li, Yu-Tain Wang, Jing Geng, Wei-Chiang Hong. Chaos cloud quantum bat hybrid optimization algorithm. Nonlinear Dynamics. 2021; 103 (1):1167-1193.

Chicago/Turabian Style

Ming-Wei Li; Yu-Tain Wang; Jing Geng; Wei-Chiang Hong. 2021. "Chaos cloud quantum bat hybrid optimization algorithm." Nonlinear Dynamics 103, no. 1: 1167-1193.

Journal article
Published: 13 July 2020 in Sensors
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The lifetime of a node in wireless sensor networks (WSN) is directly responsible for the longevity of the wireless network. The routing of packets is the most energy-consuming activity for a sensor node. Thus, finding an energy-efficient routing strategy for transmission of packets becomes of utmost importance. The opportunistic routing (OR) protocol is one of the new routing protocol that promises reliability and energy efficiency during transmission of packets in wireless sensor networks (WSN). In this paper, we propose an intelligent opportunistic routing protocol (IOP) using a machine learning technique, to select a relay node from the list of potential forwarder nodes to achieve energy efficiency and reliability in the network. The proposed approach might have applications including e-healthcare services. As the proposed method might achieve reliability in the network because it can connect several healthcare network devices in a better way and good healthcare services might be offered. In addition to this, the proposed method saves energy, therefore, it helps the remote patient to connect with healthcare services for a longer duration with the integration of IoT services.

ACS Style

Deep Kumar Bangotra; Yashwant Singh; Arvind Selwal; Nagesh Kumar; Pradeep Kumar Singh; Wei-Chiang Hong. An Intelligent Opportunistic Routing Algorithm for Wireless Sensor Networks and Its Application Towards e-Healthcare. Sensors 2020, 20, 3887 .

AMA Style

Deep Kumar Bangotra, Yashwant Singh, Arvind Selwal, Nagesh Kumar, Pradeep Kumar Singh, Wei-Chiang Hong. An Intelligent Opportunistic Routing Algorithm for Wireless Sensor Networks and Its Application Towards e-Healthcare. Sensors. 2020; 20 (14):3887.

Chicago/Turabian Style

Deep Kumar Bangotra; Yashwant Singh; Arvind Selwal; Nagesh Kumar; Pradeep Kumar Singh; Wei-Chiang Hong. 2020. "An Intelligent Opportunistic Routing Algorithm for Wireless Sensor Networks and Its Application Towards e-Healthcare." Sensors 20, no. 14: 3887.

Journal article
Published: 10 July 2020 in Scientific Reports
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To detect substation faults for timely repair, this paper proposes a fault detection method that is based on the time series model and the statistical process control method to analyze the regulation and characteristics of the behavior in the switching process. As the first time, this paper proposes a fault detection model using SARIMA, statistical process control (SPC) methods, and 3σ criterion to analyze the characteristics in substation’s switching process. The employed approaches are both very common tools in the statistics field, however, via effectively combining them with industrial process fault diagnosis, these common statistical tolls play excellent role to achieve rich technical contributions. Finally, for different fault samples, the proposed method improves the rate of detection by at least 9% (and up to 15%) than other methods.

ACS Style

Guo-Feng Fan; Xiao Wei; Ya-Ting Li; Wei-Chiang Hong. Fault detection in switching process of a substation using the SARIMA–SPC model. Scientific Reports 2020, 10, 1 -17.

AMA Style

Guo-Feng Fan, Xiao Wei, Ya-Ting Li, Wei-Chiang Hong. Fault detection in switching process of a substation using the SARIMA–SPC model. Scientific Reports. 2020; 10 (1):1-17.

Chicago/Turabian Style

Guo-Feng Fan; Xiao Wei; Ya-Ting Li; Wei-Chiang Hong. 2020. "Fault detection in switching process of a substation using the SARIMA–SPC model." Scientific Reports 10, no. 1: 1-17.

Journal article
Published: 08 June 2020 in Sustainable Cities and Society
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In recent years, the electricity industry has become increasingly important to social and economic development. For sustainability of the power industrial business, an accurate electricity consumption forecasting model can be used to adjust the production and consumption patterns of electricity, it can also support energy policy decision-making, such as load unit commitment, operational security of plants, and economic load dispatching. Using electricity consumption data to study electricity production and consumption patterns is useful in identifying the regulation of electricity economic development. This paper combines several machine learning approaches (the empirical mode decomposition (EMD) method, the support vector regression (SVR) model, and the particle swarm optimization (PSO) algorithm), thermal reaction dynamics theory, and the econometric model (AR-GARCH model), to develop a novel hybrid forecasting model, namely EMD-SVR-PSO-AR-GARCH model, for forecasting electricity consumption. It adopts a new perspective on electricity usage and consuming economic behaviors. Using electricity consumption data from the New South Wales (NSW, Australia) market, the developed model is used to forecast electricity consumption. Then, the Nash equilibrium and Porter’s five-force model are used to analyze the complex electricity usage and consuming economic behaviors, to identify the regulation of electricity and economic development, supporting the sustainable development of electricity.

ACS Style

Guo-Feng Fan; Xiao Wei; Ya-Ting Li; Wei-Chiang Hong. Forecasting electricity consumption using a novel hybrid model. Sustainable Cities and Society 2020, 61, 102320 .

AMA Style

Guo-Feng Fan, Xiao Wei, Ya-Ting Li, Wei-Chiang Hong. Forecasting electricity consumption using a novel hybrid model. Sustainable Cities and Society. 2020; 61 ():102320.

Chicago/Turabian Style

Guo-Feng Fan; Xiao Wei; Ya-Ting Li; Wei-Chiang Hong. 2020. "Forecasting electricity consumption using a novel hybrid model." Sustainable Cities and Society 61, no. : 102320.

Review
Published: 14 May 2020 in Sustainability
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Smart cities follow different strategies to face public health challenges associated with socio-economic objectives. Buildings play a crucial role in smart cities and are closely related to people’s health. Moreover, they are equally essential to meet sustainable objectives. People spend most of their time indoors. Therefore, indoor air quality has a critical impact on health and well-being. With the increasing population of elders, ambient-assisted living systems are required to promote occupational health and well-being. Furthermore, living environments must incorporate monitoring systems to detect unfavorable indoor quality scenarios in useful time. This paper reviews the current state of the art on indoor air quality monitoring systems based on Internet of Things and wireless sensor networks in the last five years (2014–2019). This document focuses on the architecture, microcontrollers, connectivity, and sensors used by these systems. The main contribution is to synthesize the existing body of knowledge and identify common threads and gaps that open up new significant and challenging future research directions. The results show that 57% of the indoor air quality monitoring systems are based on Arduino, 53% of the systems use Internet of Things, and WSN architectures represent 33%. The CO2 and PM monitoring sensors are the most monitored parameters in the analyzed literature, corresponding to 67% and 29%, respectively.

ACS Style

Gonçalo Marques; Jagriti Saini; Maitreyee Dutta; Pradeep Kumar Singh; Wei-Chiang Hong. Indoor Air Quality Monitoring Systems for Enhanced Living Environments: A Review toward Sustainable Smart Cities. Sustainability 2020, 12, 4024 .

AMA Style

Gonçalo Marques, Jagriti Saini, Maitreyee Dutta, Pradeep Kumar Singh, Wei-Chiang Hong. Indoor Air Quality Monitoring Systems for Enhanced Living Environments: A Review toward Sustainable Smart Cities. Sustainability. 2020; 12 (10):4024.

Chicago/Turabian Style

Gonçalo Marques; Jagriti Saini; Maitreyee Dutta; Pradeep Kumar Singh; Wei-Chiang Hong. 2020. "Indoor Air Quality Monitoring Systems for Enhanced Living Environments: A Review toward Sustainable Smart Cities." Sustainability 12, no. 10: 4024.

Journal article
Published: 17 March 2020 in IEEE Access
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Modern industry 4.0 applications are shifting towards decentralized automation of computing and cyber-physical systems (CPS), which necessitates building a robust, secure, and efficient system that performs complex interactions with other physical processes. To handle complex interactions in CPS, trust and consensus among various stakeholders is a prime concern. In a similar direction, consensus algorithms in blockchain have evolved over the years that focus on building smart, robust, and secure CPS. Thus, it is imperative to understand the key components, functional characteristics, and architecture of different consensus algorithms used in CPS. Many consensus algorithms exist in the literature with a specified set of functionalities, performance, and computing services. Motivated from these facts, in this survey, we present a comprehensive analysis of existing state-of-the-art consensus mechanisms and highlight their strength and weaknesses in decentralized CPS applications. In the first part, we present the scope of the proposed survey and identify gaps in the existing surveys. Secondly, we present the review method and objectives of the proposed survey based on research questions that address the gaps in existing studies. Then, we present a solution taxonomy of decentralized consensus mechanisms for various CPS applications. Then, open issues and challenges are also discussed in deploying various consensus mechanisms in the CPS with their merits and demerits. The proposed survey will act as a road-map for blockchain developers and researchers to evaluate and design future consensus mechanisms, which helps to build an efficient CPS for industry 4.0 stakeholders.

ACS Style

Umesh Bodkhe; Dhyey Mehta; Sudeep Tanwar; Pronaya Bhattacharya; Pradeep Kumar Singh; Wei-Chiang Hong. A Survey on Decentralized Consensus Mechanisms for Cyber Physical Systems. IEEE Access 2020, 8, 54371 -54401.

AMA Style

Umesh Bodkhe, Dhyey Mehta, Sudeep Tanwar, Pronaya Bhattacharya, Pradeep Kumar Singh, Wei-Chiang Hong. A Survey on Decentralized Consensus Mechanisms for Cyber Physical Systems. IEEE Access. 2020; 8 (99):54371-54401.

Chicago/Turabian Style

Umesh Bodkhe; Dhyey Mehta; Sudeep Tanwar; Pronaya Bhattacharya; Pradeep Kumar Singh; Wei-Chiang Hong. 2020. "A Survey on Decentralized Consensus Mechanisms for Cyber Physical Systems." IEEE Access 8, no. 99: 54371-54401.

Editorial
Published: 27 February 2020 in Sustainable Computing: Informatics and Systems
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ACS Style

Pradeep Kumar Singh; Bharat Bhargava; Pao Ann Hsuing; Wei-Chiang Hong. Special issue on networking technologies for sustainable computing. Sustainable Computing: Informatics and Systems 2020, 25, 100371 .

AMA Style

Pradeep Kumar Singh, Bharat Bhargava, Pao Ann Hsuing, Wei-Chiang Hong. Special issue on networking technologies for sustainable computing. Sustainable Computing: Informatics and Systems. 2020; 25 ():100371.

Chicago/Turabian Style

Pradeep Kumar Singh; Bharat Bhargava; Pao Ann Hsuing; Wei-Chiang Hong. 2020. "Special issue on networking technologies for sustainable computing." Sustainable Computing: Informatics and Systems 25, no. : 100371.

Journal article
Published: 15 January 2020 in IEEE Access
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ACS Style

Zichen Zhang; Wei-Chiang Hong; Junchi Li. Electric Load Forecasting by Hybrid Self-Recurrent Support Vector Regression Model With Variational Mode Decomposition and Improved Cuckoo Search Algorithm. IEEE Access 2020, 8, 14642 -14658.

AMA Style

Zichen Zhang, Wei-Chiang Hong, Junchi Li. Electric Load Forecasting by Hybrid Self-Recurrent Support Vector Regression Model With Variational Mode Decomposition and Improved Cuckoo Search Algorithm. IEEE Access. 2020; 8 ():14642-14658.

Chicago/Turabian Style

Zichen Zhang; Wei-Chiang Hong; Junchi Li. 2020. "Electric Load Forecasting by Hybrid Self-Recurrent Support Vector Regression Model With Variational Mode Decomposition and Improved Cuckoo Search Algorithm." IEEE Access 8, no. : 14642-14658.

Research article
Published: 14 January 2020 in Journal of Forecasting
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Since load forecasting plays a decisive role in the safe and stable operation of power system, it is particularly important to explore accurately forecasting methods. In this article, the hybrid empirical mode decomposition (EMD) and support vector regression (SVR) with back propagation neural network (BPNN), namely EMDHR‐SVR‐BPNN model, is proposed. Information theory is mainly used to solve the data tendency problem, and the EMD method is used to solve the data volatility problem. There is no interaction between these two methods, thus, these two models can complement each other through generalized regression of orthogonal decomposition. Taking the load data from the New South Wales (NSW, Australia) market as an example, the obtained simulation results are compared with other models, it is concluded that the proposed EMDHR‐SVR‐BPNN model not only improves the forecasting accuracy, but also has good fitting ability. It can timely reflect the changing tendency of data, providing a strong basis for the electricity generation of the power sector in the future, thus reducing the electricity waste. The proposed EMDHR‐SVR‐BPNN model is potential employed in the mid‐short term load forecasting.

ACS Style

Guo‐Feng Fan; Yan‐Hui Guo; Jia‐Mei Zheng; Wei‐Chiang Hong. A generalized regression model based on hybrid empirical mode decomposition and support vector regression with back‐propagation neural network for mid‐short‐term load forecasting. Journal of Forecasting 2020, 39, 737 -756.

AMA Style

Guo‐Feng Fan, Yan‐Hui Guo, Jia‐Mei Zheng, Wei‐Chiang Hong. A generalized regression model based on hybrid empirical mode decomposition and support vector regression with back‐propagation neural network for mid‐short‐term load forecasting. Journal of Forecasting. 2020; 39 (5):737-756.

Chicago/Turabian Style

Guo‐Feng Fan; Yan‐Hui Guo; Jia‐Mei Zheng; Wei‐Chiang Hong. 2020. "A generalized regression model based on hybrid empirical mode decomposition and support vector regression with back‐propagation neural network for mid‐short‐term load forecasting." Journal of Forecasting 39, no. 5: 737-756.

Chapter
Published: 02 January 2020 in Hybrid Intelligent Technologies in Energy Demand Forecasting
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As mentioned in Chap. 1 that the methods of data pre-processing can effectively decompose the time series with non-stationary characteristics into several intrinsic mode functions, such as the decomposition methods (Huang et al. in Proc R Soc A Math Phys Eng Sci 454(1971):903–995, 1998 [1]). Huang et al. (Proc R Soc A Math Phys Eng Sci 454:903–995, 1998 [1]) proposed the empirical mode decomposition (EMD) to decompose the complex time series into several intrinsic mode functions (IMFs), which is dedicated to provide extracted components to demonstrate high accurate clustering performances, and it has also received lots of attention in relevant applications fields, such as communication, economics, engineering, and so on (Huang and Kunoth in J Comput Appl Math 240:174–183, 2013 [2, Fan et al. in Math Probl Eng 720849, 2012 3, Premanode and Toumazou in Expert Syst Appl 40:377–384, 2013 4]).

ACS Style

Wei-Chiang Hong. Data Pre-processing Methods. Hybrid Intelligent Technologies in Energy Demand Forecasting 2020, 45 -67.

AMA Style

Wei-Chiang Hong. Data Pre-processing Methods. Hybrid Intelligent Technologies in Energy Demand Forecasting. 2020; ():45-67.

Chicago/Turabian Style

Wei-Chiang Hong. 2020. "Data Pre-processing Methods." Hybrid Intelligent Technologies in Energy Demand Forecasting , no. : 45-67.

Chapter
Published: 02 January 2020 in Hybrid Intelligent Technologies in Energy Demand Forecasting
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As mentioned in Chap. 2 that the traditional determination of these three parameters in an SVR model does not guarantee improved forecasting accuracy level, because of its unable to set up more suitable initial values of parameters σ, C, and ε in the initial step, and unable to simultaneously consider the interaction effects among three parameters to efficiently find out the near optimal solution for large scale data set. Therefore, it is feasible to apply meta-heuristic algorithms to implement intelligent searching around the solution range to determine most appropriate parameter combination by minimizing the objective function describing the structural risk of an SVR model. This chapter will introduce more recent representative meta-heuristic algorithms (including gravitational search algorithm, GSA; cuckoo search algorithm, CSA; bat algorithm, BA; and fruit fly optimization algorithm, FOA) hybridized with the SVR forecasting model to look for the most suitable parameter combination to increase forecasting accurate level.

ACS Style

Wei-Chiang Hong. Hybridizing Meta-heuristic Algorithms with CMM and QCM for SVR’s Parameters Determination. Hybrid Intelligent Technologies in Energy Demand Forecasting 2020, 69 -133.

AMA Style

Wei-Chiang Hong. Hybridizing Meta-heuristic Algorithms with CMM and QCM for SVR’s Parameters Determination. Hybrid Intelligent Technologies in Energy Demand Forecasting. 2020; ():69-133.

Chicago/Turabian Style

Wei-Chiang Hong. 2020. "Hybridizing Meta-heuristic Algorithms with CMM and QCM for SVR’s Parameters Determination." Hybrid Intelligent Technologies in Energy Demand Forecasting , no. : 69-133.

Chapter
Published: 02 January 2020 in Hybrid Intelligent Technologies in Energy Demand Forecasting
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As mentioned in Chapter 1 that electric load forecasting methods can be classified in three categories, Statistical approaches, including Box–Jenkins autoregressive integrated moving average (ARIMA) models (auto-regressive and moving average with exogenous variables (ARMAX) model, seasonal ARIMA (SARIMA) model) [1, 2, 3, 4, 5], regression models [6, 7, 8, 9], exponential smoothing models [including Holt-Winters model (HW) and seasonal Holt and Winters’ linear exponential smoothing (SHW)] [10, 11, 12], state space/Kalman filtering models [13, 14, 15], Bayesian estimation models [16, 17, 18, 19]. Artificial-intelligence-based approaches, including artificial neural networks (ANNs) [20, 21, 22, 23, 24, 25, 26], knowledge-based system (KBS) models [27, 28, 29, 30, 31], fuzzy inference models [32, 33, 34, 35, 36], and kernel-based models [37]. Support vector regression (SVR) model and its related hybrid/combined models in many fields, including financial time series forecasting [38, 39, 40, 41, 42, 43, 44, 45, 46], solar irradiation forecasting [47, 48, 49, 50], daily traffic peak flow management [51, 52, 53], rainfall/flood hydrological forecasting [54, 55, 56, 57, 58, 59, 60], tourism forecasting [61, 62], and so on.

ACS Style

Wei-Chiang Hong. Modeling for Energy Demand Forecasting. Hybrid Intelligent Technologies in Energy Demand Forecasting 2020, 25 -44.

AMA Style

Wei-Chiang Hong. Modeling for Energy Demand Forecasting. Hybrid Intelligent Technologies in Energy Demand Forecasting. 2020; ():25-44.

Chicago/Turabian Style

Wei-Chiang Hong. 2020. "Modeling for Energy Demand Forecasting." Hybrid Intelligent Technologies in Energy Demand Forecasting , no. : 25-44.

Chapter
Published: 02 January 2020 in Hybrid Intelligent Technologies in Energy Demand Forecasting
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As indicated in Chap. 4 that hybridizing different meta-heuristic algorithms [including gravitational search algorithm (GSA), cuckoo search algorithm (CSA), bat algorithm (BA), and fruit fly optimization algorithm (FOA)] with an SVR-based electric load forecasting model can receive superior forecasting performance than other competitive forecasting models (including ARIMA, HW, GRNN, and BPNN models).

ACS Style

Wei-Chiang Hong. Hybridizing QCM with Dragonfly Algorithm to Enrich the Solution Searching Behaviors. Hybrid Intelligent Technologies in Energy Demand Forecasting 2020, 135 -152.

AMA Style

Wei-Chiang Hong. Hybridizing QCM with Dragonfly Algorithm to Enrich the Solution Searching Behaviors. Hybrid Intelligent Technologies in Energy Demand Forecasting. 2020; ():135-152.

Chicago/Turabian Style

Wei-Chiang Hong. 2020. "Hybridizing QCM with Dragonfly Algorithm to Enrich the Solution Searching Behaviors." Hybrid Intelligent Technologies in Energy Demand Forecasting , no. : 135-152.

Journal article
Published: 23 December 2019 in IEEE Access
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In recent years, the emergence of blockchain technology (BT) has become a unique, most disruptive, and trending technology. The decentralized database in BT emphasizes data security and privacy. Also, the consensus mechanism in it makes sure that data is secured and legitimate. Still, it raises new security issues such as majority attack and double-spending. To handle the aforementioned issues, data analytics is required on blockchain based secure data. Analytics on these data raises the importance of arisen technology Machine Learning (ML). ML involves the rational amount of data to make precise decisions. Data reliability and its sharing are very crucial in ML to improve the accuracy of results. The combination of these two technologies (ML and BT) can provide highly precise results. In this paper, we present a detailed study on ML adoption for making BT-based smart applications more resilient against attacks. There are various traditional ML techniques, for instance, Support Vector Machines (SVM), clustering, bagging, and Deep Learning (DL) algorithms such as Convolutional Neural Network (CNN) and Long short-term memory (LSTM) can be used to analyses the attacks on a blockchain-based network. Further, we include how both the technologies can be combined in several smart applications such as Unmanned Aerial Vehicle (UAV), Smart Grid (SG), healthcare, and smart cities. Then, future research issues and challenges are explored. At last, a case study is presented with a detailed conclusion.

ACS Style

Sudeep Tanwar; Qasim Bhatia; Pruthvi Patel; Aparna Kumari; Pradeep Kumar Singh; Wei-Chiang Hong. Machine Learning Adoption in Blockchain-Based Smart Applications: The Challenges, and a Way Forward. IEEE Access 2019, 8, 474 -488.

AMA Style

Sudeep Tanwar, Qasim Bhatia, Pruthvi Patel, Aparna Kumari, Pradeep Kumar Singh, Wei-Chiang Hong. Machine Learning Adoption in Blockchain-Based Smart Applications: The Challenges, and a Way Forward. IEEE Access. 2019; 8 (99):474-488.

Chicago/Turabian Style

Sudeep Tanwar; Qasim Bhatia; Pruthvi Patel; Aparna Kumari; Pradeep Kumar Singh; Wei-Chiang Hong. 2019. "Machine Learning Adoption in Blockchain-Based Smart Applications: The Challenges, and a Way Forward." IEEE Access 8, no. 99: 474-488.

Journal article
Published: 04 December 2019 in Mathematics
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In the context of the nationwide call for “energy savings” in China, it is desirable to establish a more accurate forecasting model to manage the electricity consumption from the university dormitory, to provide a suitable management approach, and eventually, to achieve the “green campus” policy. This paper applies the empirical mode decomposition (EMD) method and the quantum genetic algorithm (QGA) hybridizing with the support vector regression (SVR) model to forecast the daily electricity consumption. Among the decomposed intrinsic mode functions (IMFs), define three meaningful items: item A contains the terms but the residual term; item B contains the terms but without the top two IMFs (with high randomness); and item C contains the terms without the first two IMFs and the residual term, where the first two terms imply the first two high-frequency part of the electricity consumption data, and the residual term is the low-frequency part. These three items are separately modeled by the employed SVR-QGA model, and the final forecasting values would be computed as A + B − C. Therefore, this paper proposes an effective electricity consumption forecasting model, namely EMD-SVR-QGA model, with these three items to forecast the electricity consumption of a university dormitory, China. The forecasting results indicate that the proposed model outperforms other compared models.

ACS Style

Yuanyuan Zhou; Min Zhou; Qing Xia; Wei-Chiang Hong; Zhou. Construction of EMD-SVR-QGA Model for Electricity Consumption: Case of University Dormitory. Mathematics 2019, 7, 1188 .

AMA Style

Yuanyuan Zhou, Min Zhou, Qing Xia, Wei-Chiang Hong, Zhou. Construction of EMD-SVR-QGA Model for Electricity Consumption: Case of University Dormitory. Mathematics. 2019; 7 (12):1188.

Chicago/Turabian Style

Yuanyuan Zhou; Min Zhou; Qing Xia; Wei-Chiang Hong; Zhou. 2019. "Construction of EMD-SVR-QGA Model for Electricity Consumption: Case of University Dormitory." Mathematics 7, no. 12: 1188.