Article
Engineering, Chemical
Tong Lu, Sizu Hou, Yan Xu
Summary: This study proposes a method using a composite VTDS model to address the challenging issue of load prediction in user-level integrated energy systems (IESs). The IES multi-dimensional load time series is decomposed into intrinsic mode functions (IMFs) using variational mode decomposition (VMD). Various techniques such as data dimensionality reduction, clustering denoising, and artificial neural network are employed for feature selection and load prediction, resulting in higher accuracy in short-term forecasting.
Article
Thermodynamics
Maryam Imani
Summary: A new nonlinear relationship extraction method is proposed in this work, using convolutional neural network and support vector regression for load forecasting, showing superior performance compared to several outstanding forecasters.
Article
Computer Science, Information Systems
Mithun Madhukumar, Albino Sebastian, Xiaodong Liang, Mohsin Jamil, Md Nasmus Sakib Khan Shabbir
Summary: This paper presents an approach for day-ahead load forecasting, which is critical for power systems planning, operation, and control. Using a dataset of hourly load consumption and meteorological data collected over four years, the paper develops and evaluates 19 regression model-based load forecasting algorithms. The best models are found to be from the family of Gaussian Process Regression (GPR).
Article
Engineering, Multidisciplinary
Yu Yang, Fan Jinfu, Wang Zhongjie, Zhu Zheng, Xu Yukun
Summary: In this paper, a dynamic ensemble method is proposed to accurately forecast the short-term load of residential buildings. The method utilizes state-space approaches to dynamically adjust the weight coefficients of base models and employs a two-stage strategy to improve the ensemble accuracy. Three heterogeneous models are combined based on the weight coefficients to make load predictions. Numerical tests on public datasets demonstrate that the proposed method outperforms other approaches in different scenarios.
ALEXANDRIA ENGINEERING JOURNAL
(2023)
Article
Thermodynamics
Amir Rafati, Mahmood Joorabian, Elaheh Mashhour, Hamid Reza Shaker
Summary: This paper introduces a univariate data-driven method to improve the accuracy of very short-term electrical solar power forecasting by defining new features to efficiently tackle the nonlinear characteristics of electrical solar power and using instance-based variable selection to identify the best relevant features, thus significantly enhancing the performance of very short-term solar power forecasting.
Article
Construction & Building Technology
Renyin Cheng, Junqi Yu, Min Zhang, Chunyong Feng, Wanhu Zhang
Summary: This paper proposes a short-term hybrid forecasting model based on MIV-IGWO-SVR and applies it to ice storage air conditioning cooling load forecasting. Through verification using measured data, the results show that the model has higher prediction accuracy, shorter running time, and stronger robustness.
JOURNAL OF BUILDING ENGINEERING
(2022)
Article
Engineering, Chemical
Bashir Musa, Nasser Yimen, Sani Isah Abba, Humphrey Hugh Adun, Mustafa Dagbasi
Summary: This study introduces two hybrid SVR algorithms, SVR-HHO and SVR-PSO, which combine SVR with HHO and PSO algorithms for load forecasting in four states of Nigeria. The results show improved performance of SVR-HHO over traditional SVR, achieving higher R-2 values, lower MSE values, and lower MAPE values in load forecasting.
Article
Mathematical & Computational Biology
Xiaoqiang Dai, Kuicheng Sheng, Fangzhou Shu
Summary: This paper proposes a method for ship power load forecasting using support vector machine and improved particle swarm optimization algorithm. The experimental results show that this method can reduce prediction error, improve prediction accuracy, and is of great significance for the stability and safety of ship power system.
MATHEMATICAL BIOSCIENCES AND ENGINEERING
(2022)
Article
Engineering, Civil
Saeed Mozaffari, Saman Javadi, Hamid Kardan Moghaddam, Timothy O. Randhir
Summary: A simulation-optimization hybrid model using the PSO algorithm was developed to forecast groundwater levels in aquifers. The model outperformed other models in terms of RMSE and R 2 , providing a reliable tool for decision support and management of similar aquifers.
WATER RESOURCES MANAGEMENT
(2022)
Review
Computer Science, Interdisciplinary Applications
Gourav Kumar, Sanjeev Jain, Uday Pratap Singh
Summary: Stock market forecasting is a crucial and challenging task in the financial domain, but with the development of computational intelligent methods, most risks can be reduced. This survey paper provides an up-to-date analysis of existing literature on stock market forecasting using computational intelligent methods, aiming to offer researchers and financial analysts a systematic approach to develop intelligent methodologies for stock market prediction.
ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING
(2021)
Article
Agronomy
Mohammad Valipour, Helaleh Khoshkam, Sayed M. Bateni, Changhyun Jun, Shahab S. Band
Summary: In this study, three hybrid machine learning approaches (WLSTM, WGMDH, and WGA-ANFIS) were used to forecast 1, 3, 7, and 10-day ahead daily reference evapotranspiration (ETo) at 30 sites. The results show that the third input scenario yields the most accurate forecasts, with WLSTM performing best for 1-day ahead ETo forecasting and WGMDH performing best for 3, 7, and 10-day ahead ETo forecasting. These deep learning models can provide more accurate ETo forecasts and facilitate agricultural water management and irrigation scheduling.
AGRICULTURAL WATER MANAGEMENT
(2023)
Article
Engineering, Civil
Fatemeh Rezaie Adaryani, S. Jamshid Mousavi, Fatemeh Jafari
Summary: This study compares the performances of three machine and deep learning-based rainfall forecasting approaches and improves the accuracy of the models through event classification and adding more predictors.
JOURNAL OF HYDROLOGY
(2022)
Article
Engineering, Electrical & Electronic
Guo-Feng Fan, Yan-Rong Liu, Hui-Zhen Wei, Meng Yu, Yin-He Li
Summary: This paper proposes a hybrid model based on EEMD-RF-SVR-RR algorithm for electric load forecasting. Numerical experiments have shown that the model outperforms other models in terms of forecasting accuracy, confirming its feasibility and effectiveness in short-term load forecasting.
ELECTRIC POWER SYSTEMS RESEARCH
(2022)
Article
Green & Sustainable Science & Technology
Alessandro Incremona, Giuseppe De Nicolao
Summary: This paper addresses the challenges posed by the intermittent nature of renewable energy sources in meeting sustainability goals for increased usage of these sources. It discusses the improvement of short-term load forecasting models and their performance, with a focus on forecasting the 24-hour profile of electric load in Italy. The proposed predictors outperform the models used by the Italian Transmission System Operator, Terna, with significant improvements in mean absolute percentage error and mean absolute error.
SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS
(2022)
Article
Computer Science, Artificial Intelligence
Clodomir Santana, Marcos Oliveira, Carmelo Bastos-Filho, Ronaldo Menezes
Summary: Memetic algorithms, which incorporate local-search methods into population-based metaheuristics, are known for their enhanced solution refinement capabilities. Designing a memetic algorithm is complex due to the need to balance exploitation-exploration and other algorithm operators. Analyzing the impact of local search in swarm-based algorithms helps understand the behavior of algorithms and the relevance of local search strategies.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Health Care Sciences & Services
M. Rakibul Hoque, Yukun Bao, Golam Sorwarb
INFORMATICS FOR HEALTH & SOCIAL CARE
(2017)
Article
Computer Science, Information Systems
Yukun Bao, Rakibul Hoque, Shiyu Wang
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
(2017)
Article
Mathematics, Interdisciplinary Applications
Zhongyi Hu, Yukun Bao, Raymond Chiong, Tao Xiong
JOURNAL OF SYSTEMS SCIENCE & COMPLEXITY
(2017)
Article
Agricultural Economics & Policy
Tao Xiong, Chongguang Li, Yukun Bao
AGRICULTURAL ECONOMICS-ZEMEDELSKA EKONOMIKA
(2017)
Article
Computer Science, Interdisciplinary Applications
Aboobucker Ilmudeen, Yukun Bao
INDUSTRIAL MANAGEMENT & DATA SYSTEMS
(2018)
Article
Computer Science, Artificial Intelligence
Tao Xiong, Chongguang Li, Yukun Bao
Article
Health Care Sciences & Services
G. M. Azmal Ali Quaosar, Md. Rakibul Hoque, Yukun Bao
TELEMEDICINE AND E-HEALTH
(2018)
Article
Health Care Sciences & Services
G. M. Azmal Ali Quaosar, Md. Rakibul Hoque, Yukun Bao
TELEMEDICINE AND E-HEALTH
(2018)
Article
Computer Science, Interdisciplinary Applications
Md Shamim Talukder, Raymond Chiong, Yukun Bao, Babur Hayat Malik
INDUSTRIAL MANAGEMENT & DATA SYSTEMS
(2019)
Article
Energy & Fuels
Samuel Atuahene, Yukun Bao, Yao Yevenyo Ziggah, Patricia Semwaah Gyan, Feng Li
Article
Computer Science, Interdisciplinary Applications
Nattaporn Thongsri, Liang Shen, Bao Yukun
INTERNATIONAL JOURNAL OF INFORMATION AND LEARNING TECHNOLOGY
(2019)
Article
Social Issues
Md Shamim Talukder, Liang Shen, Md Farid Hossain Talukder, Yukun Bao
TECHNOLOGY IN SOCIETY
(2019)
Article
Regional & Urban Planning
Md Nahin Hossain, Md Shamim Talukder, Md Rakibul Hoque, Yukun Bao
Article
Business
G. M. Azmal Ali Quaosar, Md. Rakibul Hoque, Yukun Bao
COGENT BUSINESS & MANAGEMENT
(2018)
Article
Economics
Tao Xiong, Chongguang Li, Yukun Bao
ECONOMIC MODELLING
(2017)
Article
Computer Science, Artificial Intelligence
Jin Zhang, Zekang Bian, Shitong Wang
Summary: This study proposes a novel style linear k-nearest neighbor method to extract stylistic features using matrix expressions and improve the generalizability of the predictor through style membership vectors.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Qifeng Wan, Xuanhua Xu, Jing Han
Summary: In this study, we propose an innovative approach for dimensionality reduction in large-scale group decision-making scenarios that targets linguistic preferences. The method combines TF-IDF feature similarity and information loss entropy to address challenges in decision-making with a large number of decision makers.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Hegui Zhu, Yuchen Ren, Chong Liu, Xiaoyan Sui, Libo Zhang
Summary: This paper proposes an adversarial attack method based on frequency information, which optimizes the imperceptibility and transferability of adversarial examples in white-box and black-box scenarios respectively. Experimental results validate the superiority of the proposed method and its application in real-world online model evaluation reveals their vulnerability.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Jing Tang, Xinwang Liu, Weizhong Wang
Summary: This paper proposes a hybrid generalized TODIM approach in the Fine-Kinney framework to evaluate occupational health and safety hazards. The approach integrates CRP, dynamic SIN, and PLTSs to handle opinion interactions and incomplete opinions among decision makers. The efficiency and rationality of the proposed approach are demonstrated through a numerical example, comparison, and sensitivity studies.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Shigen Shen, Chenpeng Cai, Zhenwei Li, Yizhou Shen, Guowen Wu, Shui Yu
Summary: To address the damage caused by zero-day attacks on SIoT systems, researchers propose a heuristic learning intrusion detection system named DQN-HIDS. By integrating Deep Q-Networks (DQN) into the system, DQN-HIDS gradually improves its ability to identify malicious traffic and reduces resource workloads. Experiments demonstrate the superior performance of DQN-HIDS in terms of workload, delayed sample queue, rewards, and classifier accuracy.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Song Deng, Qianliang Li, Renjie Dai, Siming Wei, Di Wu, Yi He, Xindong Wu
Summary: In this paper, we propose a Chinese text classification algorithm based on deep active learning for the power system, which addresses the challenge of specialized text classification. By applying a hierarchical confidence strategy, our model achieves higher classification accuracy with fewer labeled training data.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Kaan Deveci, Onder Guler
Summary: This study proves the lack of robustness in nonlinear IF distance functions for ranking intuitionistic fuzzy sets (IFS) and proposes an alternative ranking method based on hypervolume metric. Additionally, the suggested method is extended as a new multi-criteria decision making method called HEART, which is applied to evaluate Turkey's energy alternatives.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Fu-Wing Yu, Wai-Tung Ho, Chak-Fung Jeff Wong
Summary: This research aims to enhance the energy management in commercial building air-conditioning systems, specifically focusing on chillers. Ridge regression is found to outperform lasso and elastic net regression when optimized with the appropriate hyperparameter, making it the most suitable method for modeling the system coefficient of performance (SCOP). The key variables that strongly influence SCOP include part load ratios, the operating numbers of chillers and pumps, and the temperatures of chilled water and condenser water. Additionally, July is identified as the month with the highest potential for performance improvement. This study introduces a novel approach that balances feature selection, model accuracy, and optimal tuning of hyperparameters, highlighting the significance of a generic and simplified chiller system model in evaluating energy management opportunities for sustainable operation. The findings from this research can guide future efforts towards more energy-efficient and sustainable operations in commercial buildings.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Xiaoyan Chen, Yilin Sun, Qiuju Zhang, Xuesong Dai, Shen Tian, Yongxin Guo
Summary: In this study, a method for dynamically non-destructive grasping of thin-skinned fruits is proposed. It utilizes a multi-modal depth fusion convolutional neural network for image processing and segmentation, and combines the evaluation mechanism of optimal grasping stability and the forward-looking non-destructive grasp control algorithm. The proposed method greatly improves the comprehensive performance of grasping delicate fruits using flexible hands.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Yuxuan Yang, Siyuan Zhou, He Weng, Dongjing Wang, Xin Zhang, Dongjin Yu, Shuiguang Deng
Summary: The study proposes a novel model, POIGDE, which addresses the challenges of data sparsity and elusive motives by solving graph differential equations to capture continuous variation of users' interests. The model learns interest transference dynamics using a time-serial graph and an interval-aware attention mechanism, and applies Siamese learning to directly learn from label representations for predicting future POI visits. The model outperforms state-of-the-art models on real-world datasets, showing potential in the POI recommendation domain.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
S. Karthika, P. Rathika
Summary: The widespread development of monitoring devices in the power system has generated a large amount of power consumption data. Storing and transmitting this data has become a significant challenge. This paper proposes an adaptive data compression algorithm based on the discrete wavelet transform (DWT) for power system applications. It utilizes multi-objective particle swarm optimization (MO-PSO) to select the optimal threshold. The algorithm has been tested and outperforms other existing algorithms.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Jiaqi Guo, Haiyan Wu, Xiaolei Chen, Weiguo Lin
Summary: In this study, an adaptive SV-Borderline SMOTE-SVM algorithm is proposed to address the challenge of imbalanced data classification. The algorithm maps the data into kernel space using SVM and identifies support vectors, then generates new samples based on the neighbors of these support vectors. Extensive experiments show that this method is more effective than other approaches in imbalanced data classification.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Qiumei Zheng, Linkang Xu, Fenghua Wang, Yongqi Xu, Chao Lin, Guoqiang Zhang
Summary: This paper proposes a new semantic segmentation network model called HilbertSCNet, which combines the Hilbert curve traversal and the dual pathway idea to design a new spatial computation module to address the problem of loss of information for small targets in high-resolution images. The experiments show that the proposed network performs well in the segmentation of small targets in high-resolution maps such as drone aerial photography.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Mojtaba Ashour, Amir Mahdiyar
Summary: Analytic Hierarchy Process (AHP) is a widely applied technique in multi-criteria decision-making problems, but the sheer number of AHP methods presents challenges for scholars and practitioners in selecting the most suitable method. This paper reviews articles published between 2010 and 2023 proposing hybrid, improved, or modified AHP methods, classifies them based on their contributions, and provides a comprehensive summary table and roadmap to guide the method selection process.
APPLIED SOFT COMPUTING
(2024)
Review
Computer Science, Artificial Intelligence
Gerardo Humberto Valencia-Rivera, Maria Torcoroma Benavides-Robles, Alonso Vela Morales, Ivan Amaya, Jorge M. Cruz-Duarte, Jose Carlos Ortiz-Bayliss, Juan Gabriel Avina-Cervantes
Summary: Electric power system applications are complex optimization problems. Most literature reviews focus on studying electrical paradigms using different optimization techniques, but there is a lack of review on Metaheuristics (MHs) in these applications. Our work provides an overview of the paradigms underlying such applications and analyzes the most commonly used MHs and their search operators. We also discover a strong synergy between the Renewable Energies paradigm and other paradigms, and a significant interest in Load-Forecasting optimization problems. Based on our findings, we provide helpful recommendations for current challenges and potential research paths to support further development in this field.
APPLIED SOFT COMPUTING
(2024)