Article
Green & Sustainable Science & Technology
Marcelo Azevedo Costa, Ramiro Ruiz-Cardenas, Leandro Brioschi Mineti, Marcos Oliveira Prates
Summary: This paper introduces a novel analog-based methodology for multi-step time series forecasting, called dynamic time scan forecasting (DTSF), which combines similarity functions with goodness-of-fit statistics to predict future multi-step data by identifying similar patterns throughout the time series. An ensemble version of the method (eDTSF) achieves competitive results in wind speed time series forecasting, even in situations of high variability, compared to eleven selected concurrent forecasting methods.
Article
Economics
Karim Barigou, Pierre-Olivier Goffard, Stephane Loisel, Yahia Salhi
Summary: Predicting the evolution of mortality rates is crucial for life insurance and pension funds. This study proposes a Bayesian negative-binomial framework for mortality modeling to account for overdispersion and parameter uncertainty. Model averaging techniques are employed to address model misspecifications. Two out-of-sample validation methods are proposed and compared with standard Bayesian model averaging. Numerical simulations and real-life mortality datasets demonstrate that the proposed methods outperform the standard approach in terms of prediction performance and robustness.
INTERNATIONAL JOURNAL OF FORECASTING
(2023)
Article
Energy & Fuels
Thi Hoai Thu Nguyen, Quoc Bao Phan
Summary: In this paper, a novel hybrid model combining decomposition and deep learning, embedded with GA optimization, was proposed for wind speed forecasting. By decomposing and training the historical wind speed time series, better forecasting results than other methods were obtained.
Article
Economics
Oliver Grothe, Fabian Kaechele, Fabian Krueger
Summary: Modeling price risks in energy markets is crucial for economic decision making. This study proposes a generic and easy-to-implement method for generating multivariate probabilistic forecasts based on univariate point forecasts of day-ahead electricity prices. The method models dependencies across hours using copula techniques and an optional time series component. An example is demonstrated to construct realistic prediction intervals for pricing individual load profiles.
Article
Meteorology & Atmospheric Sciences
Guangpeng Liu, Annalisa Bracco, Julien Brajard
Summary: This paper proposes a machine learning approach to improve the output of an ocean circulation model by learning and predicting its systematic biases. The method utilizes a sequence-to-sequence model to improve the representation of sea surface anomalies in model outputs using satellite altimeter data. The proposed method outperforms persistence in forecasting the systematic bias in the ocean circulation model and offers potential for further development of hybrid modeling tools.
JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS
(2023)
Article
Thermodynamics
William Duarte Jacondino, Ana Lucia da Silva Nascimento, Leonardo Calvetti, Gilberto Fisch, Cesar Augustus Assis Beneti, Sheila Radman da Paz
Summary: The study investigated the impact of different physics parameterization on wind speed forecasting in two onshore wind farms in Brazil. The findings suggest that specific model forecast settings perform better, with the TKE closure scheme showing superior performance.
Article
Energy & Fuels
Leonard Tschora, Erwan Pierre, Marc Plantevit, Celine Robardet
Summary: This article investigates the capabilities of different machine learning techniques in accurately predicting electricity prices. By considering previously unused predictive features such as price histories of neighboring countries, the current state-of-the-art approaches are extended. It is shown that these features significantly improve the quality of forecasts, even during periods of sudden changes. Analyzing the contribution of different features in model prediction using Shap values further sheds light on how models make their predictions and builds user confidence.
Article
Statistics & Probability
Boxiang Wang, Hui Zou
Summary: This study focuses on estimating the generalization error of a CV-tuned predictive model and proposes the use of an honest leave-one-out cross-validation framework for an unbiased estimator. Demonstrations with kernel SVM and kernel logistic regression show competitive performance even against the state-of-the-art .632+ estimator.
Article
Economics
Kadir Ozen, Dilem Yildirim
Summary: Electricity price forecasting is a challenging task that requires consideration of multiple potential factors to improve accuracy and extract more information. This study introduces Bootstrap Aggregation method and shows substantial forecast improvements in electricity price prediction across multiple markets compared to the popular LASSO estimation method.
Article
Engineering, Civil
Ran Mo, Bin Xu, Ping-an Zhong, Feilin Zhu, Xin Huang, Weifeng Liu, Sunyu Xu, Guoqing Wang, Jianyun Zhang
Summary: This study developed a dynamic long-term streamflow probabilistic forecasting model that addresses spatio-temporal dependent error correction for a multisite system. The model successfully reduced forecast uncertainty and improved accuracy and reliability by utilizing MMFE for error correction and Copula function for error characteristics capture.
JOURNAL OF HYDROLOGY
(2021)
Article
Statistics & Probability
Dingdong Yi, Shaoyang Ning, Chia-Jung Chang, S. C. Kou
Summary: The PRISM method uses online search data to forecast unemployment initial claims, outperforming previous methods during both the 2008-2009 financial crisis and the near-future COVID-19 pandemic period. The timely and accurate unemployment forecasts by PRISM can assist government agencies and financial institutions in assessing economic trends and making informed decisions.
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
(2021)
Article
Computer Science, Artificial Intelligence
Jie Man, Honghui Dong, Jiayang Gao, Jun Zhang, Limin Jia, Yong Qin
Summary: This paper introduces a method named GA-GRGAT that uses GAT and GAN for long-term axle temperature prediction. By fusing historical axle temperature information, the proposed method improves prediction accuracy. Evaluation on actual high-speed trains datasets demonstrates that the method achieves high accuracy with short computation time.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Jianli Zhao, Zhongbo Liu, Qiuxia Sun, Qing Li, Xiuyan Jia, Rumeng Zhang
Summary: This study proposes an attention-based dynamic spatial-temporal graph convolutional network (ADSTGCN) model to address the challenges of spatial-temporal modeling and long-term forecasting in traffic forecasting research. The model consists of multiple dynamic spatial-temporal blocks, each containing modules for dynamic adjustment, gated dilated convolution, and spatial convolution. Experimental results on three public traffic datasets demonstrate the model's good performance.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Energy & Fuels
Jianzhou Wang, Shuai Wang, Bo Zeng, Haiyan Lu
Summary: In this study, an ensemble probabilistic forecasting system is proposed to quantify the uncertainty of wind speed. Experimental results demonstrate that the system has good sharpness while maintaining high interval coverage. The proposed system accurately assesses the uncertainty of wind speed and improves the efficiency of wind energy utilization, thereby reducing the operation cost of power systems.
Article
Thermodynamics
Jikai Duan, Hongchao Zuo, Yulong Bai, Jizheng Duan, Mingheng Chang, Bolong Chen
Summary: This study presents a novel hybrid forecasting system that significantly enhances the accuracy of wind speed prediction, achieving superior prediction results compared to other models through a process of decomposition, prediction, and error correction.
Article
Energy & Fuels
Chen Wang, Jie Wu, Jianzhou Wang, Weigang Zhao
Article
Engineering, Environmental
Xiao-Chen Yuan, Xun Sun, Weigang Zhao, Zhifu Mi, Bing Wang, Yi-Ming Wei
RESOURCES CONSERVATION AND RECYCLING
(2017)
Article
Thermodynamics
Ning An, Weigang Zhao, Jianzhou Wang, Duo Shang, Erdong Zhao
Article
Management
Weigang Zhao, Jianzhou Wang, Haiyan Lu
OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE
(2014)
Article
Green & Sustainable Science & Technology
Zhenhai Guo, Weigang Zhao, Haiyan Lu, Jianzhou Wang
Article
Economics
Zhenling Chen, Jinkai Li, Weigang Zhao, Xiao-Chen Yuan, Guo-liang Yang
Article
Green & Sustainable Science & Technology
Zhenling Chen, Wenju Wang, Feng Li, Weigang Zhao
JOURNAL OF CLEANER PRODUCTION
(2020)
Article
Computer Science, Artificial Intelligence
Jianming Hu, Weigang Zhao, Jingwei Tang, Qingxi Luo
Summary: This study introduces a novel framework that integrates a softened multi-interval loss function into neural networks to simultaneously generate multiple prediction intervals (PIs) for wind power prediction. The proposed loss function effectively avoids the cross-bound phenomenon and reduces the mean prediction interval width of PIs. Among the investigated models, the echo state network (ESN) with the proposed loss function shows the best forecasting performance for both point prediction and interval prediction.
APPLIED SOFT COMPUTING
(2021)
Article
Thermodynamics
Shuai Wang, Jianzhou Wang, Haiyan Lu, Weigang Zhao
Summary: This paper presents a novel wind speed forecasting model that combines noise processing, statistical approaches, deep learning frameworks, and multi-objective optimization algorithms. Experimental results from three sites in China demonstrate excellent forecasting performance of the proposed model.
Article
Computer Science, Artificial Intelligence
Quande Qin, Zhaorong Huang, Zhihao Zhou, Yu Chen, Weigang Zhao
Summary: Accurate carbon pricing guidance is crucial for reducing carbon dioxide emissions. This study proposes a novel filter-based model using the Hodrick-Prescott filter for carbon price forecasting. The model incorporates adaptive noise residual decomposition and Bayesian optimization to improve performance. Compared to existing models, it shows better stability and statistical advantage.
APPLIED SOFT COMPUTING
(2022)
Article
Management
Xinzhi Zhu, Shuo Yang, Jingyi Lin, Yi-Ming Wei, Weigang Zhao
JOURNAL OF MODELLING IN MANAGEMENT
(2018)
Proceedings Paper
Computer Science, Artificial Intelligence
Hongya Xu, Yao Dong, Jie Wu, Weigang Zhao
ADVANCES IN INTELLIGENT SYSTEMS
(2012)
Proceedings Paper
Engineering, Electrical & Electronic
Weigang Zhao, Nan Wang, Suling Zhu, Xiuying Liu
2012 7TH INTERNATIONAL CONFERENCE ON SYSTEM OF SYSTEMS ENGINEERING (SOSE)
(2012)
Article
Economics
Weigang Zhao, Yunfei Cao, Bo Miao, Ke Wang, Yi-Ming Wei
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.