Article
Green & Sustainable Science & Technology
Elissaios Sarmas, Evangelos Spiliotis, Efstathios Stamatopoulos, Vangelis Marinakis, Haris Doukas
Summary: This paper proposes a meta-learning method to improve short-term deterministic forecasts of PV systems by blending the base forecasts of multiple DL models. Results indicate that different base models perform best at different PV plants, and meta-learning can improve accuracy by up to 5% over the most accurate base model per plant.
Article
Computer Science, Artificial Intelligence
Shiming Xiang, Bo Tang
Summary: The article introduces a variation of DNC architecture called CSLM-DNC, which includes convertible short-term and long-term memory to improve memory efficiency. Inspired by the human brain, this new scheme improves learning performance through different memory locations importance and memory transformation.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
Article
Engineering, Electrical & Electronic
Seyed Mohammad Jafar Jalali, Sajad Ahmadian, Mahdi Khodayar, Abbas Khosravi, Miadreza Shafie-khah, Saeid Nahavandi, Joao P. S. Catalao
Summary: In this paper, a novel hybrid model named EvolCNN is proposed to predict short-term wind power using deep convolutional neural network (CNN) and evolutionary search optimizer. The improved Grey Wolf Optimization (GWO) algorithm is used to find the optimal values of hyperparameters for the CNN model. The EvolCNN model outperforms other benchmarks in terms of accuracy for 10-min, 1-hr, and 3-hr ahead wind power forecasting.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Ronit Jaiswal, Girish K. Jha, Rajeev Ranjan Kumar, Kapil Choudhary
Summary: The study developed a deep long short-term memory (DLSTM) based model for accurate forecasting of nonstationary and nonlinear agricultural prices series. The DLSTM model, advantageous in capturing nonlinear and volatile patterns, demonstrated superiority in price forecasting ability.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Green & Sustainable Science & Technology
Luoxiao Yang, Zijun Zhang
Summary: This paper proposes a novel deep attention convolutional recurrent network (DACRN-KM) for accurate short-term wind speed prediction. The network utilizes DACRN to extract latent representations of wind speeds and enhances them with an auto-updated memory module. K-shape clustering algorithm is applied to derive K patterns of the rebuilt latent representations, and the final prediction layer generates the prediction results.
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
(2022)
Article
Automation & Control Systems
Ning Jin, Yongkang Zeng, Ke Yan, Zhiwei Ji
Summary: Artificial intelligence-based air quality index (AQI) forecasting is a hot research topic, and the proposed multiple nested long short term memory network (MTMC-NLSTM) model performs superior in accurate AQI forecasting.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Computer Science, Artificial Intelligence
Ziyu Sheng, Zeyu An, Huiwei Wang, Guo Chen, Kun Tian
Summary: As the energy system becomes more complex and flexible, accurate load forecasting is crucial for power scheduling, load switching, and infrastructure development. This paper proposes a neural network framework that combines modified deep residual network (DRN) and long short-term memory (LSTM) recurrent neural network (RNN) to address the short-term load forecasting (STLF) problem. The proposed model utilizes the strengths of DRN for avoiding vanishing gradient and LSTM for capturing nonlinear patterns, and incorporates dimension weighted units to improve performance in terms of depth, time, and feature dimension. The model is evaluated using public datasets and shows high accuracy, robustness, and generalization capability compared to existing mainstream models.
APPLIED SOFT COMPUTING
(2023)
Article
Green & Sustainable Science & Technology
A. Mellit, A. Massi Pavan, V. Lughi
Summary: This paper develops and compares different types of deep learning neural networks (DLNN) for short-term output PV power forecasting, showing very good accuracy in one-step prediction within a 1-minute time horizon and acceptable results in multi-step prediction.
Review
Green & Sustainable Science & Technology
Meenu Ajith, Manel Martinez-Ramon
Summary: This study compares three categories of solar irradiance forecasting models and finds that the hybrid model MICNN-L performs better in predicting solar irradiance under cloudy conditions.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2023)
Article
Automation & Control Systems
Amir Bidokhti, Shahrokh Ghaemmaghami
Summary: This paper introduces a graph-based neural memory module that can be trained using differentiable mechanisms to solve tasks with long-term dependencies. Inspired by the human memory system, this module performs better than traditional methods in terms of convergence speed and final error.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Automation & Control Systems
Yue Song, Diyin Tang, Jinsong Yu, Zetian Yu, Xin Li
Summary: This article proposes a novel wind power forecasting approach based on a graph convolution network (GCN) and a multiresolution convolution neural network (CNN), combining spatial features and temporal features. The proposed method effectively considers the effects of multiple variables on wind power and provides interpretability for deep learning-based forecasting. Experimental results demonstrate the effectiveness and accuracy of the proposed method in short-term wind power forecasting.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Thermodynamics
Ahmed A. Ewees, Mohammed A. A. Al-qaness, Laith Abualigah, Mohamed Abd Elaziz
Summary: The study utilizes the Heap-based optimizer to enhance wind power prediction performance using LSTM models. It demonstrates significant improvement and outperforms other models in predicting wind power from different turbines, validated through multiple datasets and comparisons.
ENERGY CONVERSION AND MANAGEMENT
(2022)
Article
Environmental Sciences
Anqi Xie, Hao Yang, Jing Chen, Li Sheng, Qian Zhang
Summary: This study proposed a method based on a multi-variable long short-term memory network model for short-term wind speed forecasting, which proved to be feasible and superior to other forecasting methods.
Article
Computer Science, Artificial Intelligence
Keyhan Gavahi, Peyman Abbaszadeh, Hamid Moradkhani
Summary: The DeepYield model, which combines ConvLSTM layers with 3DCNN, is proposed for crop yield forecasting. The model is trained using historical data and remote sensing imagery, with comparisons showing significantly better forecasting performance compared to competing approaches in the soybean growing counties in the United States.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Engineering, Electrical & Electronic
Wei Junqiang, Wu Xuejie, Yang Tianming, Jiao Runhai
Summary: A new ultra-short-term wind power prediction method combining maximal information coefficient (MIC) with multi-task learning (MTL) and long short-term memory (LSTM) network is proposed. The method constructs the feature input sequence of the neural network based on the MIC correlation analysis results and optimizes the network parameters using grid search. The case study on a wind farm in the United States demonstrates that the proposed method achieves higher prediction accuracy compared to other existing methods.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2023)
Article
Physics, Multidisciplinary
Qiaoru Li, Zhe Zhang, Kun Li, Liang Chen, Zhenlin Wei, Jingchun Zhang
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2020)
Article
Thermodynamics
Huan Li, Kun Li, Nicholas Zafetti, Jianfeng Gu
Article
Mathematics, Interdisciplinary Applications
Xiaoyong Tian, Kun Li, Zengxin Kang, Yun Peng, Hongjun Cui
CHAOS SOLITONS & FRACTALS
(2020)
Article
Physics, Multidisciplinary
Qiaoru Li, Yaqing Li, Kun Li, Liang Chen, Qiang Zheng, Ke Chen
Article
Physics, Applied
Liang Chen, Yun Zhang, Kun Li, Qiaoru Li, Qiang Zheng
Summary: A new AHT-FVD model is proposed in this study, which can effectively stabilize traffic flow and alleviate traffic congestion. Numerical simulations show that increasing the average headway weight and electronic throttle angle difference control signal coefficients can both improve traffic flow stability.
MODERN PHYSICS LETTERS B
(2021)
Article
Physics, Multidisciplinary
Liang Chen, Qiang Zheng, Kun Li, Qiao-Ru Li, Jian-Lei Zhang
Summary: This study reveals that the arrangement of obstacles plays a crucial role in evacuation efficiency, with an optimal location for obstacles that minimizes total evacuation time and the length of obstacles also affecting efficiency. The dependence of evacuation efficiency on obstacles is further influenced by the number of pedestrians and desired velocity.
Article
Physics, Multidisciplinary
Qiao Jiang, Ying Zhou, Lei Zhang, Kun Li, Huan Li
Summary: This paper improves the pedestrian direction selection process and adjusts the contribution of the density factor and the distance factor to pedestrians' choice of moving direction by introducing a weight factor. The simulation results show that considering only the distance factor leads to congestion, while focusing too much on the density factor makes pedestrians hesitate. Therefore, comprehensively considering the impact of both factors is crucial for improving evacuation efficiency and exit utilization.
Article
Physics, Multidisciplinary
Kun Li, Shuai Wang, Rui Cong
Summary: This study investigates how individuals make traffic route choices based on evolutionary dynamics by considering skilled drivers and novice drivers as two distinct populations. The research finds that the evolutionary equilibrium is sensitive to factors such as expected traveling distance, proportion of novice drivers, and unit time cost, while the initial proportion of mixed strategies has minimal effect on the outcome. The work aims to explore an innovative way of incorporating evolutionary game theory into the traffic assignment model.
Article
Mathematics, Interdisciplinary Applications
Kun Li, Haocheng Xu, Xiao Liu
Summary: In recent years, road traffic accidents have been receiving increasing attention as a leading cause of accidental deaths across multiple disciplines. Studying the severity of accidents can help identify the causal relationship between different risk factors and road accidents, thereby improving road traffic safety. However, the application of data visualization in traffic safety investigations is still lacking. In this study, we propose a hybrid algorithm called LightGBM-TPE that incorporates data visualization into machine learning to analyze the UK's traffic accidents data in 2017. The algorithm outperforms other typical machine learning algorithms in terms of f1, accuracy, recall, and precision metrics. By calculating the SHAP value of each feature using LightGBM-TPE, we find that Longitude, Latitude, Hour, and Day_of_Week are the four risk factors most closely related to accident severity. The visualization of the data further confirms this conclusion. Overall, our research explores an innovative way to understand and evaluate the importance of features in road traffic accidents, which can contribute to suggesting effective solutions for improving traffic safety.
CHAOS SOLITONS & FRACTALS
(2022)
Article
Physics, Multidisciplinary
Liang Chen, Jingjie Sun, Kun Li, Qiaoru Li
Summary: Yield to pedestrian at crosswalks is an important issue in traffic engineering. By introducing penalty-incentive control, drivers can be motivated to give way to pedestrians, thus improving pedestrian safety while crossing roads.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2022)
Article
Engineering, Multidisciplinary
Qiaoru Li, Mingyang Zhao, Zhe Zhang, Kun Li, Liang Chen, Jianlei Zhang
Summary: This paper proposes an improved social force model to explore how system dynamics control collective behavior of pedestrians during pandemics. The study finds that maintaining social distance can promote evacuation efficiency, but it may decrease efficiency when the desired speed is too low. Moreover, the response time and epidemic sensitivity of pedestrians also affect evacuation efficiency. This research helps in formulating relevant evacuation plans to inhibit the spread of COVID-19 without lowering evacuation efficiency.
APPLIED MATHEMATICAL MODELLING
(2023)
Article
Automation & Control Systems
Chen Hou, Cangqi Zhou, Chu-Ge Wu, Rui Cong, Kun Li
Summary: This paper investigates the trade-off between data trustworthiness and usage cost in cloud-based multi-agent systems (MAS), and proposes an algorithm to ensure that agents obtain the most trustworthy data within an acceptable level of cost.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2022)
Article
Physics, Multidisciplinary
Qiaoru Li, Longyin Zhang, Kun Li, Liang Chen, Runbin Li
Summary: The study investigates the influence of cautious pedestrians on evacuation dynamics using an improved social force model, finding that a moderate number of cautious pedestrians with rational psychological tolerance can significantly enhance evacuation efficiency, while lower tolerance promotes emergency evacuation and increasing the tolerance threshold leads to slower evacuation.
Article
Computer Science, Information Systems
Huibin Jin, Zhanyao Hu, Kun Li, Mingjian Chu, Guoliang Zou, Guihua Yu, Jianlei Zhang
Summary: The distribution of visual attention among pilots affects flight performance, with experts showing more attention to key instruments, leading to better performance. An effective visual attention model could be developed based on this study to improve air traffic safety.
Article
Physics, Multidisciplinary
Qiao-Ru Li, Qin-Ze Lin, Meng-Jie Li, Liang Chen, Kun Li
Summary: This study investigates the violation behavior of e-bikes at signal intersections, identifying factors such as degree, transmission rate, and crossing time that influence illegal behavior. Results show that small increases in these factors can significantly boost violation behavior throughout the intersection, providing insights for intersection design and signal control.
Article
Energy & Fuels
Shitong Fang, Houfan Du, Tao Yan, Keyu Chen, Zhiyuan Li, Xiaoqing Ma, Zhihui Lai, Shengxi Zhou
Summary: This paper proposes a new type of nonlinear VIV energy harvester (ANVEH) that compensates for the decrease in peak energy output at low wind speeds by introducing an auxiliary structure. Theoretical and experimental results show that ANVEH performs better than traditional nonlinear VIV energy harvesters under various system parameter variations.
Article
Energy & Fuels
Wei Jiang, Shuo Zhang, Teng Wang, Yufei Zhang, Aimin Sha, Jingjing Xiao, Dongdong Yuan
Summary: A standardized method was developed to evaluate the availability of solar energy resources in road areas, which combined the Analytic Hierarchy Process (AHP) and the Geographic Information System (GIS). By analyzing critical factors and using a multi-indicator evaluation method, the method accurately evaluated the utilization of solar energy resources and guided the optimal location selection for road photovoltaic (PV) projects. The results provided guidance for the application of road PV projects and site selection for route corridors worldwide, promoting the integration of transportation and energy.
Article
Energy & Fuels
Chang Liu, Jacob A. Wrubel, Elliot Padgett, Guido Bender
Summary: The study investigates the effects of coating defects on the performance of the anode porous transport layer (PTL) in water electrolyzers. The results show that an increasing fraction of uncoated regions on the PTL leads to decreased cell performance, with continuous uncoated regions having a more severe impact compared to multiple thin uncoated strips.
Article
Energy & Fuels
Marcos Tostado-Veliz, Xiaolong Jin, Rohit Bhakar, Francisco Jurado
Summary: In this paper, a coordinated charging price mechanism for clusters of parking lots is proposed. The research shows that enabling vehicle-to-grid characteristics can bring significant economic benefits for users and the cluster coordinator, and vehicle-to-grid impacts noticeably on the risk-averse character of the uncertainty-aware strategies. The developed pricing mechanism can reduce the cost for users, avoiding to directly translate the energy cost to charging points.
Article
Energy & Fuels
Duan Kang
Summary: Building an energy superpower is a key strategy for China and a long-term goal for other countries. This study proposes an evaluation system and index for measuring energy superpower, and finds that China has significantly improved its ranking over the past 21 years, surpassing other countries.
Article
Energy & Fuels
Fucheng Deng, Yifei Wang, Xiaosen Li, Gang Li, Yi Wang, Bin Huang
Summary: This study investigated the synergistic blockage mechanism of sand and hydrate in gravel filling layer and the evolution of permeability in the layer. Experimental models and modified permeability models were established to analyze the effects of sand particles and hydrate formation on permeability. The study provided valuable insights for the safe and efficient exploitation of hydrate reservoirs.
Article
Energy & Fuels
Hao Wang, Xiwen Chen, Natan Vital, Edward Duffy, Abolfazl Razi
Summary: This study proposes a HVAC energy optimization model based on deep reinforcement learning algorithm. It achieves 37% energy savings and ensures thermal comfort for open office buildings. The model has a low complexity, uses a few controllable factors, and has a short training time with good generalizability.
Article
Energy & Fuels
Moyue Cong, Yongzhuo Gao, Weidong Wang, Long He, Xiwang Mao, Yi Long, Wei Dong
Summary: This study introduces a multi-strategy ultra-wideband energy harvesting device that achieves high power output without the need for external power input. By utilizing asymmetry, stagger array, magnetic coupling, and nonlinearity strategies, the device maintains a stable output voltage and high power density output at non-resonant frequencies. Temperature and humidity monitoring are performed using Bluetooth sensors to adaptively assess the device.
Article
Energy & Fuels
Tianshu Dong, Xiudong Duan, Yuanyuan Huang, Danji Huang, Yingdong Luo, Ziyu Liu, Xiaomeng Ai, Jiakun Fang, Chaolong Song
Summary: Electrochemical water splitting is crucial for hydrogen production, and improving the hydrogen separation rate from the electrode is essential for enhancing water electrolyzer performance. However, issues such as air bubble adhesion to the electrode plate hinder the process. Therefore, a methodology to investigate the two-phase flow within the electrolyzer is in high demand. This study proposes using a microfluidic system as a simulator for the electrolyzer and optimizing the two-phase flow by manipulating the micro-structure of the flow.
Article
Energy & Fuels
Shuo Han, Yifan Yuan, Mengjiao He, Ziwen Zhao, Beibei Xu, Diyi Chen, Jakub Jurasz
Summary: Giving full play to the flexibility of hydropower and integrating more variable renewable energy is of great significance for accelerating the transformation of China's power energy system. This study proposes a novel day-ahead scheduling model that considers the flexibility limited by irregular vibration zones (VZs) and the probability of flexibility shortage in a hydropower-variable renewable energy hybrid generation system. The model is applied to a real hydropower station and effectively improves the flexibility supply capacity of hydropower, especially during heavy load demand in flood season.
Article
Energy & Fuels
Zhen Wang, Kangqi Fan, Shizhong Zhao, Shuxin Wu, Xuan Zhang, Kangjia Zhai, Zhiqi Li, Hua He
Summary: This study developed a high-performance rotary energy harvester (AI-REH) inspired by archery, which efficiently accumulates and releases ultralow-frequency vibration energy. By utilizing a magnetic coupling strategy and an accumulator spring, the AI-REH achieves significantly accelerated rotor speeds and enhanced electric outputs.
Article
Energy & Fuels
Yi Yang, Qianyi Xing, Kang Wang, Caihong Li, Jianzhou Wang, Xiaojia Huang
Summary: In this study, a novel hybrid Quantile Regression (QR) model is proposed for Probabilistic Load Forecasting (PLF). The model integrates causal dilated convolution, residual connection, and Bidirectional Long Short-Term Memory (BiLSTM) for multi-scale feature extraction. In addition, a Combined Probabilistic Load Forecasting System (CPLFS) is proposed to overcome the inherent flaws of relying on a single model. Simulation results show that the hybrid QR outperforms traditional models and CPLFS exceeds the best benchmarks in terms of prediction accuracy and stability.
Article
Energy & Fuels
Wen-Jiang Zou, Young-Bae Kim, Seunghun Jung
Summary: This paper proposes a dynamic prediction model for capacity fade in vanadium redox flow batteries (VRFBs). The model accurately predicts changes in electrolyte volume and capacity fade, enhancing the competitiveness of VRFBs in energy storage applications.
Article
Energy & Fuels
Yuechao Ma, Shengtie Wang, Guangchen Liu, Guizhen Tian, Jianwei Zhang, Ruiming Liu
Summary: This paper focuses on the balance of state of charge (SOC) among multiple battery energy storage units (MBESUs) and bus voltage balance in an islanded bipolar DC microgrid. A SOC automatic balancing strategy is proposed considering the energy flow relationship and utilizing the adaptive virtual resistance algorithm. The simulation results demonstrate the effectiveness of the proposed strategy in achieving SOC balancing and decreasing bus voltage unbalance.
Article
Energy & Fuels
Raad Z. Homod, Basil Sh. Munahi, Hayder Ibrahim Mohammed, Musatafa Abbas Abbood Albadr, Aissa Abderrahmane, Jasim M. Mahdi, Mohamed Bechir Ben Hamida, Bilal Naji Alhasnawi, A. S. Albahri, Hussein Togun, Umar F. Alqsair, Zaher Mundher Yaseen
Summary: In this study, the control problem of the multiple-boiler system (MBS) is formulated as a dynamic Markov decision process and a deep clustering reinforcement learning approach is applied to obtain the optimal control policy. The proposed strategy, based on bang-bang action, shows superior response and achieves more than 32% energy saving compared to conventional fixed parameter controllers under dynamic indoor/outdoor actual conditions.