Article
Energy & Fuels
Da Xu, Ziyi Bai, Xiaolong Jin, Xiaodong Yang, Shuangyin Chen, Ming Zhou
Summary: This paper proposes a high-renewable portfolio model of energy hub that explores the geothermal-solar-wind multi-energy complementarities. It formulates the problem as a mean-variance approach to deal with forecast uncertainties and optimally determine the energy generation, conversion, and storage candidates.
Article
Computer Science, Artificial Intelligence
Zhuangyan Fang, Shengyu Zhu, Jiji Zhang, Yue Liu, Zhitang Chen, Yangbo He
Summary: Learning causal structures represented by directed acyclic graphs (DAGs) in high-dimensional settings remains challenging, especially for non-sparse graphs. In this article, we propose using a low-rank assumption for the adjacency matrix of a DAG causal model to address this problem. By adapting existing low-rank techniques, we establish useful results connecting interpretable graphical conditions to the low-rank assumption. Our experiments demonstrate the utility of these low-rank adaptations, particularly for large and dense graphs, with comparable performance even when graphs are not restricted to be low rank.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Energy & Fuels
Elnaz Shahrabi, Seyed Mehdi Hakimi, Arezoo Hasankhani, Ghasem Derakhshan, Babak Abdi
Summary: This study introduces an improved energy hub system model that optimizes energy planning and scheduling by incorporating various renewable energy sources and storage devices. The utilization of thermal energy storage in the energy hub system significantly reduces fuel consumption and CO2 emissions. The efficiency of the proposed Quantum Particle Swarm Optimization (QPSO) algorithm surpasses Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithms in terms of convergence speed and global search ability for optimal scheduling and planning of the energy hub system in the presence of stochastic renewable energy systems.
SUSTAINABLE ENERGY GRIDS & NETWORKS
(2021)
Article
Thermodynamics
Jiawei Lyu, Shenxi Zhang, Haozhong Cheng, Kai Yuan, Yi Song
Summary: This paper proposes a novel optimal configuration method for an energy hub (EH) considering the integration of electric vehicles (EVs). The method optimizes the structure and capacity of the EH as well as the charging and discharging schemes of EVs. The EH is modeled using graph theory and the EH configuration problem is transformed into mixed integer linear programming. Case studies validate the effectiveness of the proposed method.
Article
Computer Science, Artificial Intelligence
Xiaohan Liu, Xiaoguang Gao, Zidong Wang, Xinxin Ru, Qingfu Zhang
Summary: This paper proposes a generic metaheuristic method for causal discovery by directly finding causal relationships in a directed acyclic graph. Several novel heuristic factors are introduced to expand the search space and maintain acyclicity. A metaheuristic algorithm is then used to search for an optimal solution closer to real causality. The proposed method is theoretically proven and extensively validated through experiments comparing it with other state-of-the-art causal solvers on real-world and simulated structures.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Jiaxuan Liang, Jun Wang, Guoxian Yu, Wei Guo, Carlotta Domeniconi, Maozu Guo
Summary: This paper proposes a method called HetDAG to learn causal relationships between nodes in heterogeneous networks. By embedding node attributes and using prior network structure to update node representations, and then using attention mechanism for DAG learning, HetDAG is able to learn DAG efficiently and performs well in experiments.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Public, Environmental & Occupational Health
Michael Webster-Clark, Alexander Breskin
Summary: The study provides two rules regarding effect measure modification, indicating whether a variable has an effect on the outcome at different treatment levels, and how to identify sufficient adjustments to generalize study results to a broader population.
AMERICAN JOURNAL OF EPIDEMIOLOGY
(2021)
Article
Computer Science, Interdisciplinary Applications
Ken Aho, Cathy Kriloff, Sarah E. Godsey, Rob Ramos, Chris Wheeler, Yaqi You, Sara Warix, DeWayne Derryberry, Sam Zipper, Rebecca L. Hale, Charles T. Bond, Kevin A. Kuehn
Summary: To address the incompatibility of conventional stream network metrics with non-perennial streams, the researchers treat non-perennial stream networks as directed acyclic graphs (DAGs). DAG metrics enable the summarization of important characteristics of non-perennial streams and tracking of these features as networks change. They introduce a new R package, streamDAG, which includes procedures and functions for handling water presence data and analyzing both unweighted and weighted stream DAGs. The package is demonstrated using two North American non-perennial streams.
ENVIRONMENTAL MODELLING & SOFTWARE
(2023)
Article
Computer Science, Artificial Intelligence
Shih-Gu Huang, Jing Xia, Liyuan Xu, Anqi Qiu
Summary: We developed a deep learning framework for predicting cognition and disease using fMRI. The framework consists of two neural networks for learning spatial and temporal information of functional time series and functional connectivity features. It also includes an attention component for generating a spatial attention map. Experimental results demonstrate that the framework is generalizable and outperforms other machine learning techniques in cognition and age prediction.
MEDICAL IMAGE ANALYSIS
(2022)
Article
Computer Science, Information Systems
Zifa Liu, Mingquan Zeng, Hanze Zhou, Jianyu Gao
Summary: The ZPM method utilizes the energy hub model and directed acyclic graph concept to effectively plan the structure and equipment configuration of regional integrated energy systems, improving the solution speed of the planning model.
Review
Green & Sustainable Science & Technology
Tao Ding, Wenhao Jia, Mohammad Shahidehpour, Ouzhu Han, Yuge Sun, Ziyu Zhang
Summary: This article provides a comprehensive review of the utilization of renewable energy resources, focusing on energy hub modeling and optimization algorithms. It also discusses the IoT-based energy hub control structure and corresponding management methods.
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
(2022)
Article
Energy & Fuels
Yiyang Qiao, Fan Hu, Wen Xiong, Yajun Li
Summary: This paper proposes a configuration optimization method of IES based on EH, which can achieve economic, efficient, and environmentally friendly operation of the system. The concept of EH is adopted to model energy devices separately, and the innovative and extended EH is proposed to study the coupling characteristics of multiple energy flows. EH-based operation configuration model and combination model of gas turbines are established to optimize the installed capacity and combination, ensuring that the gas turbines can flexibly adapt to the diversified energy demand.
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
Xiaokang Wang, Shan Lu, Rui Zhou, Huiwen Wang
Summary: This paper proposes a two-stage DAG skeleton estimation approach for highly correlated data. The first stage involves a novel neighborhood selection method based on sparse partial least squares regression, while the second stage estimates the DAG skeleton by evaluating conditional independence hypotheses. Simulation studies and tests on publicly available datasets demonstrate the effectiveness of the proposed method.
PATTERN RECOGNITION
(2023)
Article
Energy & Fuels
Hamed Asgarian Honarmand, Sara Mahmoudi Rashid
Summary: This paper presents a sustainable framework for the long-term planning of energy hub systems in the presence of renewable energy sources, including the needs of electrical, cooling, and heating loads, as well as the use of storage systems. The study shows that the use of Price-Based Demand Response program can reduce investment costs, while the absence of PV panels increases operating costs.
JOURNAL OF ENERGY STORAGE
(2022)
Article
Computer Science, Information Systems
Xiaokang Wang, Huiwen Wang, Zhichao Wang, Shan Lu, Ying Fan
Summary: This paper proposes a new method to identify the causal structure of asset risk spillover network and characterize the joint return distribution of the global financial system using directed acyclic graph skeleton estimation. Through a two-stage approach, the effectiveness of the proposed method is demonstrated through simulation studies.
INFORMATION SCIENCES
(2021)
Article
Engineering, Electrical & Electronic
Pei Yong, Audun Botterud, Ning Zhang, Chongqing Kang
Summary: This letter proposes a novel framework for evaluating the capacity value of UPS storage, taking into account their operational characteristics. The framework can effectively evaluate the capacity contributions of distributed UPS storage facilities.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2023)
Article
Engineering, Electrical & Electronic
Jiawei Zhang, Hongyang Jia, Ning Zhang
Summary: This paper proposes an alternative support vector machine decision tree method for rule extraction in order to deal with feasibility and stability issues. The method greatly enhances the efficiency, stability, and versatility of traditional decision tree algorithms, and demonstrates its effectiveness in various power and energy system scenarios.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2023)
Article
Engineering, Electrical & Electronic
Jean-Francois Toubeau, Fei Teng, Thomas Morstyn, Leandro Von Krannichfeldt, Yi Wang
Summary: This paper presents a new privacy-preserving framework for short-term probabilistic forecasting of nodal voltages in local energy communities. The framework utilizes federated learning to keep individuals' data decentralized and incorporates differential privacy to ensure sensitive local information cannot be inferred. The approach also employs cross-series learning to smoothly integrate new clients without data scarcity issues, achieving improved performance compared to non-collaborative models.
IEEE TRANSACTIONS ON SMART GRID
(2023)
Article
Chemistry, Analytical
Ning Zhang, Mengqi Tong, Zhuanzhuan Shi, Jianyu Yang, Bo Chen, Changming Li, Chunxian Guo
Summary: A unique carbon-based material comprising Pt-CuO nanocrystal interfacially anchored on functionalized carbon nanofiber (Pt-CuO@FCNF) was developed for ultrasensitive detection of cell-released H2O2 using a screen printed electrode (SPE). Pt-CuO@FCNF showed a wide linear response range, low detection limit, fast response time, and good selectivity.
ANALYTICA CHIMICA ACTA
(2023)
Article
Engineering, Multidisciplinary
Zelin Sun, Leandro Von Krannichfeldt, Yi Wang
Summary: This paper proposes a novel framework for day-ahead load forecast trading and valuation in an ensemble model. It highlights the importance of combining individual forecasts to improve accuracy. Three payoff-allocating schemes are proposed and compared.
IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS
(2023)
Article
Engineering, Electrical & Electronic
Yihong Zhou, Zhaohao Ding, Qingsong Wen, Yi Wang
Summary: In this study, a Bayesian training method is proposed to enhance the robustness of deep learning-based load forecasting models against adversarial attacks. The experimental results demonstrate that this method maintains good prediction performance under no attack and achieves higher robustness compared to four other benchmark methods.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2023)
Article
Dermatology
Yifan Wang, Ning Zhang, Xiaolu Li, Xiaohan Li, Cong Chen, Jiadong Zhang, Yong Hu
Summary: This study describes a novel, aesthetic, and minimally invasive method for treating ingrown toenails. By retrospectively analyzing 436 lesions of 395 ingrown toes, it was found that this new approach has comparable efficacy in treating ingrown toenails.
JOURNAL OF COSMETIC DERMATOLOGY
(2023)
Article
Computer Science, Information Systems
Fangyuan Si, Ning Zhang, Yi Wang, Peng-Yong Kong, Wenjie Qiao
Summary: This article introduces a method of using secure multiparty computation (SMPC) to enhance the security of integrated energy systems (IES) in distributed energy resource integration. By utilizing standardized modeling and privacy-preserving distributed optimization algorithm, this method can achieve optimization control of IES without disclosing sensitive information.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Energy & Fuels
Zihang Dong, Xi Zhang, Ning Zhang, Chongqing Kang, Goran Strbac
Summary: This paper proposes a distributed control strategy for coordinating the charging/discharging schedules of electrical vehicles to enhance the resilience of an urban energy system. The strategy takes into account the local constraints and interconnection capacities of the multi-energy microgrid, and aims to reduce essential load shedding. Additionally, it minimizes the gap between the energy of the electrical vehicles and the required energy level at the departure time. A series of case studies demonstrate the effectiveness of the introduced distributed coordinated approach on energy arbitrage and congestion management.
ADVANCES IN APPLIED ENERGY
(2023)
Article
Energy & Fuels
Dalin Qin, Guobing Liu, Zengxiang Li, Weicheng Guan, Shubao Zhao, Yi Wang
Summary: Accurate mid-term gas demand forecasting is crucial for gas companies and policymakers to meet increasing gas demand. However, data paucity and heterogeneous consumption patterns pose challenges. This paper proposes a novel method, FedCon-LCF, that integrates federated learning, deep contrastive learning, and clustering approaches to address these challenges. The method achieves high-performance forecasting by utilizing data from multiple gas companies and considering heterogeneous patterns. Evaluation on a dataset from 11 Chinese cities shows significant improvements over the benchmark LSTM model.
Article
Engineering, Electrical & Electronic
Dawei Qiu, Jianhong Wang, Zihang Dong, Yi Wang, Goran Strbac
Summary: With the increasing importance of advanced energy management schemes in multi-energy systems, incorporating these systems into the existing energy market is promising for future power systems. The continuous double auction (CDA) market is ideal for enabling peer-to-peer (P2P) energy trading due to its transparency and efficiency. However, modeling the CDA market is challenging due to stochastic and dynamic behaviors of participants. This study proposes a novel multi-agent reinforcement learning method to address these challenges and outperforms existing methods in terms of policy performance, scalability, and computational performance in P2P energy trading.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2023)
Article
Engineering, Electrical & Electronic
Ning Zhang, Hongyang Jia, Qingchun Hou, Ziyang Zhang, Tian Xia, Xiao Cai, Jiaxin Wang
Summary: This article introduces the application of machine learning techniques in optimizing power system security and stability. It analyzes the impact of high penetration renewable energy on power systems, discusses the modeling of complex security and stability boundaries using machine learning techniques, and demonstrates how machine learning models can be embedded into power system operation models.
PROCEEDINGS OF THE IEEE
(2023)
Article
Green & Sustainable Science & Technology
Fangyuan Si, Ershun Du, Ning Zhang, Yi Wang, Yinghua Han
Summary: Urban areas account for more than 70% of total carbon emissions, and in China, this proportion is even higher, at around 80%, due to human activities in urban energy consumption. The study compares the low-carbon transition of urban energy systems in Beijing and Suzhou, evaluating the effectiveness of decarbonization policies, providing methodologies for low carbon transitions, and analyzing challenges and solutions. The findings suggest promoting electrification and demand-side response in Beijing, and improving the cleanliness of local generation and external energy in Suzhou. Under the goal of carbon neutrality, urban energy systems can achieve green and low-carbon transitions through clean energy substitution, electrification, carbon emission accounting, and collaborative governance mechanisms.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2023)
Review
Green & Sustainable Science & Technology
Shixu Zhang, Yaowang Li, Ershun Du, Chuan Fan, Zhenlong Wu, Yong Yao, Lurao Liu, Ning Zhang
Summary: This paper introduces the basic concept, application scenarios, and research progress of cloud energy storage technology, and discusses its combination with other advanced energy and information technology as well as the future development prospects.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2023)
Article
Green & Sustainable Science & Technology
Haiyang Jiang, Ershun Du, Boyu He, Ning Zhang, Peng Wang, Fuqiang Li, Jie Ji
Summary: This study aims to analyze the seasonal fluctuation characteristics of renewable energy in different time scales. By decomposing the time series into climate, seasonal, and daily components, and introducing an Ito process for modeling, it was found that low-renewable-output events in central China tend to have longer durations.
