Article
Engineering, Electrical & Electronic
Wei Shi, Yufeng Wang, Yiyuan Chen, Jianhua Ma
Summary: With the development of global power market reform, the high volatility and uncertainty of electricity prices pose challenges to accurate price prediction, especially in the case of sudden events. A novel two-stage electricity price forecasting scheme (TSEP) is proposed, utilizing techniques like deep neural networks (DNN) and artificial neural networks (ANN) to improve prediction accuracy.
ELECTRIC POWER SYSTEMS RESEARCH
(2021)
Article
Computer Science, Artificial Intelligence
Haizhou Guo, Dian Zhang, Siyuan Liu, Lei Wang, Ye Ding
Summary: This study focuses on the importance of cryptocurrency price forecasting and the challenges faced by traditional approaches. A new price forecasting model WT-CATCN is proposed, leveraging Wavelet Transform and Casual Multi-Head Attention Temporal Convolutional Network to forecast Bitcoin prices. Experimental results show that the model improves price forecasting performance by 25%.
DECISION SUPPORT SYSTEMS
(2021)
Article
Energy & Fuels
Eike Cramer, Dirk Witthaut, Alexander Mitsos, Manuel Dahmen
Summary: A probabilistic modeling approach is proposed to predict the intraday electricity price difference based on the hourly pattern of the day-ahead market prices. The study also analyzes the influence of external factors using explainable artificial intelligence (XAI). Among various models, the normalizing flow shows the highest accuracy and narrowest prediction intervals.
Article
Business, Finance
Mingxi Liu, Guowen Li, Jianping Li, Xiaoqian Zhu, Yinhong Yao
Summary: After constructing a feature system with 40 determinants affecting the price of Bitcoin, a deep learning method called SDAE was used for prediction. The SDAE model outperformed traditional methods such as BPNN and SVR in both directional and level prediction accuracy.
FINANCE RESEARCH LETTERS
(2021)
Article
Thermodynamics
Haolin Yang, Kristen R. Schell
Summary: In this paper, a data-driven deep learning network (GHTnet) was proposed to predict real-time electricity prices, with significant performance improvements achieved through the introduction of a new CNN module and time series summary statistics.
Article
Engineering, Electrical & Electronic
Jiayan Liu, Gang Lin, Christian Rehtanz, Sunhua Huang, Yang Zhou, Yong Li
Summary: A data-driven intelligent EV charging scheduling algorithm is proposed in this paper, which considers the charging costs, battery degradation, and users' dissatisfaction comprehensively. By forecasting the charging demand and establishing an optimization model based on time-of-use electricity price and charging facility limitation, the proposed algorithm achieves better effectiveness and performance compared to existing methods.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Yuanzheng Li, Yizhou Ding, Yun Liu, Tao Yang, Ping Wang, Jingfei Wang, Wei Yao
Summary: This paper proposes a dense skip attention based deep learning model for the day-ahead electricity price forecasting. The model improves the forecasting precision by addressing the feature-wise variability and temporal variability. Experimental results validate the superiority of the proposed approach in deterministic and interval forecasting.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2023)
Review
Public, Environmental & Occupational Health
Mariana Oliveira, Valerie Belanger, Angel Ruiz, Daniel Santos
Summary: Hospital managers adopt various strategies to address surgical backlogs, including increasing capacity, managing demand, and improving efficiency. Extending operating room hours has been used to reduce waiting lists, particularly during the COVID-19 pandemic, by utilizing unused operating rooms and existing surgical teams.
FRONTIERS IN PUBLIC HEALTH
(2023)
Article
Environmental Sciences
Fang Zhang, Nuan Wen
Summary: This paper proposes a new deep neural network model, TCN-Seq2Seq, for carbon price forecasting. The proposed model outperforms traditional statistical forecasting models and state-of-the-art deep learning prediction models in terms of predictive ability and robustness. The accuracy of carbon price forecasting is of great importance for policy makers and carbon market investors.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2022)
Article
Environmental Sciences
Shuailong Jiang, Hanjie Fan, Chunzai Wang
Summary: This paper proposes a novel model called TITP-Net to improve typhoon intensity forecasting, and conducts a series of experiments to demonstrate its effectiveness.
Article
Green & Sustainable Science & Technology
Nazila Pourhaji, Mohammad Asadpour, Ali Ahmadian, Ali Elkamel
Summary: This paper investigates the impact of monthly/seasonal data clustering on electricity price forecasting and selects effective parameters using a grey correlation analysis method to improve prediction accuracy. The results show that the prediction error decreases in the monthly clustering mode compared to the non-clustering and seasonal clustering modes.
Article
Business, Finance
Javier Vasquez Saenz, Facundo Manuel Quiroga, Aurelio F. Bariviera
Summary: This paper explores the use of clustering models of stocks to improve stock price prediction and trading algorithm returns. The stocks are clustered using k-means and alternative distance metrics with quarterly financial ratios, prices, and daily returns as features. ARIMA and LSTM forecasting models are trained for each cluster to predict the daily price of each stock, and the clustering-empowered forecasting models are used to analyze trading algorithm returns.
INTERNATIONAL REVIEW OF FINANCIAL ANALYSIS
(2023)
Article
Environmental Studies
Hong Zhang, Hoang Nguyen, Diep-Anh Vu, Xuan-Nam Bui, Biswajeet Pradhan
Summary: This study developed several forecast models for monthly copper prices using various machine learning algorithms, and found that the MLP neural network is the most reliable method. Additionally, the currency exchange rates of countries with the highest copper production significantly affect the volatility of monthly copper prices.
Article
Engineering, Electrical & Electronic
Yifei Ding, Minping Jia, Yudong Cao
Summary: The article proposes a deep subdomain adaptive regression network (DSARN) for aligning relevant subdomains in source and target domains and demonstrates its effectiveness in the field of prognostics and health management (PHM) of bearings. The DSARN method shows superior performance compared to other state-of-the-art deep learning (DL) and transfer learning (TL) methods.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2021)
Article
Engineering, Electrical & Electronic
Haolin Yang, Kristen R. Schell
Summary: This paper studies the application of autoencoders in real-time price forecasting models and develops a pre-trained, quadruple branch, CNN-based autoencoder (QCAE). The integration of QCAE with the forecasting model is tested and validated on the New York Independent System Operator (NYISO) power grid, demonstrating its superiority in improving prediction accuracy.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Wandry R. Faria, Gregorio Munoz-Delgado, Javier Contreras, Benvindo R. Pereira Jr
Summary: This paper proposes a new bilevel mathematical model for competitive electricity markets, taking into account the participation of distribution systems operators. A new pricing method is introduced as an alternative to the inaccessible dual variables of the transmission system.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2024)
Article
Engineering, Electrical & Electronic
Chao Zhang, Liwei Zhang, Dong Wang, Kaiyuan Lu
Summary: The load disturbance rejection ability of electrical machine systems is crucial in many applications. Existing studies mainly focus on improving disturbance observers, but the speed response control during the transient also plays a significant role. This paper proposes a sliding mode disturbance observer-based load disturbance rejection control with an adaptive filter and a Smith predictor-based speed filter delay compensator to enhance the transient speed response.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2024)
Article
Engineering, Electrical & Electronic
Arif Hussain, Arif Mehdi, Chul-Hwan Kim
Summary: The proposed scheme in this research paper is a communication-less islanding detection system based on recurrent neural network (RNN) for hybrid distributed generator (DG) systems. The scheme demonstrates good performance in feature extraction, feature selection, and islanding detection, and it also performs effectively in noisy environments.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2024)
Article
Engineering, Electrical & Electronic
Zonghui Sun, Xizheng Guo, Shinan Wang, Xiaojie You
Summary: This paper presents a status pre-matching method (SPM) that eliminates the iterative calculations for resistance switch model, and simulates all operation modes of PECs through a more convenient approach. Furthermore, a FPGA implementation scheme is proposed to fully utilize the multiplier units of FPGA.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2024)
Article
Engineering, Electrical & Electronic
Rui Zhou, Shuheng Chen, Yang Han, Qunying Liu, Zhe Chen, Weihao Hu
Summary: In power system scheduling with variable renewable energy sources, considering both spatial and temporal correlations is a challenging task due to the complex intertwining of spatiotemporal characteristics and computational complexity caused by high dimensionality. This paper proposes a novel probabilistic spatiotemporal scenario generation (PSTSG) method that generates probabilistic scenarios accounting for spatial and temporal correlations simultaneously. The method incorporates Latin hypercube sampling, copula-importance sampling theory, and probability-based scenario reduction technique to efficiently capture the spatial and temporal correlation in the dynamic optimal power flow problem. Numerical simulations demonstrate the superiority of the proposed approach in terms of computational efficiency and accuracy compared to existing methods.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2024)
Article
Engineering, Electrical & Electronic
Juan Manuel Mauricio, J. Carlos Olives-Camps, Jose Maria Maza-Ortega, Antonio Gomez-Exposito
Summary: This paper proposes a simplified thermal model of VSC, which can produce accurate results at a low computational cost. The model consists of a simple first-order thermal dynamics system and two quadratic equations to model power losses. A methodology is also provided to derive the model parameters from manufacturer data.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2024)
Article
Engineering, Electrical & Electronic
Jae-Kyeong Kim, Kyeon Hur
Summary: This paper investigates the relationship between the accuracy of finite difference-based trajectory sensitivity (FDTS) analysis and the perturbation size in non-smooth systems. The study reveals that the approximation accuracy is significantly influenced by the perturbation size, and linear approximation is the most suitable method for practical applications.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2024)
Article
Engineering, Electrical & Electronic
Yuan Si, Amjad Anvari-Moghaddam
Summary: This paper investigates the impact of geomagnetic disturbances on small signal stability in power systems and proposes the installation of blocking devices to mitigate the negative effects. Quantitative evaluation reveals that intense geomagnetic disturbances significantly increase the risk of small signal instability. Optimal placement of blocking devices based on sensitivity scenarios results in a significant reduction in the risk index compared to constant and varying induced geoelectric fields scenarios.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2024)
Article
Engineering, Electrical & Electronic
Xuejian Zhang, Wenxin Kong, Nian Yu, Huang Chen, Tianyang Li, Enci Wang
Summary: The intensity estimation of geomagnetically induced currents (GICs) varies depending on the method used. The estimation using field magnetotelluric (MT) data provides the highest accuracy, followed by the estimation using 3D conductivity models and the estimation using a 1D conductivity model. The GICs in the North China 1000-kV power grid have reached a very high-risk level, with C3 and C4 having a significant impact on the geoelectric field and GICs.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2024)
Article
Engineering, Electrical & Electronic
Yue Pan, Shunjiang Lin, Weikun Liang, Xiangyong Feng, Xuan Sheng, Mingbo Liu
Summary: This paper introduces the concept and model of offshore-onshore regional integrated energy system, and proposes a stochastic optimal dispatch model and an improved state-space approximate dynamic programming algorithm to solve the model. The case study demonstrates the effectiveness and high efficiency of the proposed method in improving economic and environmental benefits.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2024)
Article
Engineering, Electrical & Electronic
Mohammad Eydi, Reza Ghazi, Majid Oloomi Buygi
Summary: Proportional current sharing, voltage restoration, and SOCs balancing in DC microgrid control algorithms are the leading challenges. This paper proposes a novel communication-less control method using a capacitor and a DC/DC converter to stabilize the system and restore the DC bus voltage. The method includes injecting an AC signal into the DC bus, setting the current of energy storage units based on frequency and SOC, and incorporating droop control for system stability. Stability analysis and simulation results validate the effectiveness of the proposed method.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2024)
Article
Engineering, Electrical & Electronic
Xiangjian Meng, Xinyu Shi, Weiqi Wang, Yumin Zhang, Feng Gao
Summary: With the increasing penetration of photovoltaic power generation, regional power forecasting becomes critical for stable and economical operation of power systems. This paper proposes a minute-level regional PV power forecasting scheme using selected reference PV plants. The challenges include the lack of complete historical power data and the heavy computation burden. The proposed method incorporates a novel reference PV plant selection method and a flexible approach to decrease the accumulated error of rolling forecasting.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2024)
Article
Engineering, Electrical & Electronic
Huabo Shi, Yuhong Wang, Xinwei Sun, Gang Chen, Lijie Ding, Pengyu Pan, Qi Zeng
Summary: This article investigates the dynamic stability characteristics of the full size converter variable speed pumped storage unit and proposes improvements for the control strategy. The research is important for ensuring the safe and efficient operation of the unit.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2024)
Article
Engineering, Electrical & Electronic
Firmansyah Nur Budiman, Makbul A. M. Ramli, Houssem R. E. H. Bouchekara, Ahmad H. Milyani
Summary: This paper proposes an optimal harmonic power flow framework for the daily scheduling of a grid-connected microgrid, which addresses power quality issues and ensures effective control through demand side management.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2024)
Article
Engineering, Electrical & Electronic
Cong Zeng, Ziyu Chen, Jizhong Zhu, Fellew Ieee
Summary: This paper introduces a distributed solution method for the multi-objective OPF problem, using a coevolutionary multi-objective evolutionary algorithm and the idea of decomposition. The problem is alleviated by decomposing decision variables and objective functions, and a new distributed fitness evaluation method is proposed. The experimental results demonstrate the effectiveness of the method and its excellence in large-scale systems.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2024)