Article
Thermodynamics
Huijing Fan, Zhao Zhen, Nian Liu, Yiqian Sun, Xiqiang Chang, Yu Li, Fei Wang, Zengqiang Mi
Summary: This paper proposes a novel probabilistic forecasting method based on SDA, FCM, LSTM, and KDE, considering the correlation between wind power fluctuation patterns and forecasting errors. Simulation results show that introducing pattern recognition can improve the skill score of probabilistic forecasting by 36.50% on average.
Article
Engineering, Multidisciplinary
Yixiao Yu, Ming Yang, Xueshan Han, Yumin Zhang, Pingfeng Ye
Summary: This article introduces a nonparametric probabilistic method for regional wind power forecast, which uses quantile regression neural networks (QRNN) and deep quantile regression to handle massive data. The model's performance is enhanced by applying local-connected methods and a ramp function, with test results demonstrating its effectiveness.
IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS
(2021)
Article
Engineering, Electrical & Electronic
Mansoor Khan, Chuan He, Tianqi Liu, Farhan Ullah
Summary: Wind power forecasting is crucial for renewable energy production due to the dynamic and uncertain behavior of wind. This paper introduces a new hybrid approach to efficiently predict wind power by addressing the challenges of wind dynamics and variability.
JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY
(2021)
Article
Computer Science, Artificial Intelligence
Ayush K. Varshney, Pranab K. Muhuri, Q. M. Danish Lohani
Summary: Hierarchical clustering using probabilistic intuitionistic fuzzy sets is proposed in this paper to handle data uncertainty. The novel clustering algorithm, termed as Probabilistic Intuitionistic Fuzzy Hierarchical Clustering (PIFHC) Algorithm, utilizes the probabilistic Euclidean distance measure and achieves better cluster accuracies compared to existing counterparts. Experimental results on different datasets demonstrate the effectiveness of the PIFHC algorithm in improving clustering accuracy.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Cristina Tortora, Francesco Palumbo
Summary: This paper discusses a probabilistic distance clustering method adjusted for cluster size (PDQ) for handling mixed-type data, shows its advantages through a simulation design, and applies it to a real data set.
APPLIED SOFT COMPUTING
(2022)
Article
Green & Sustainable Science & Technology
Jiaqing Lv, Xiaodong Zheng, Miroslaw Pawlak, Weike Mo, Marek Miskowicz
Summary: This paper applies a sparse machine learning technique to predict next-hour wind power, considering forecast values, real-time observations, and neighboring power generators. The model outperforms other methods and improves upon broadcast values obtained from meteorological/physical methods. Additionally, a novel nonparametric density estimation approach is used to provide probabilistic prediction bands.
Article
Thermodynamics
Mao Yang, Chaoyu Shi, Huiyu Liu
Summary: An improved Fuzzy C-means clustering algorithm is proposed to classify turbines with similar power output characteristics into several categories and select a representative power curve as the equivalent curve of the wind farm, aiming to improve the accuracy of wind power prediction and reduce model complexity.
Article
Green & Sustainable Science & Technology
Jie Yan, Corinna Moehrlen, Tuhfe Goecmen, Mark Kelly, Arne Wessel, Gregor Giebel
Summary: This paper presents a qualitative review on wind power forecasting uncertainty, providing strategies for mitigating different uncertainty sources and discussing the importance of uncertainty validation.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2022)
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
Binghui Li, Cong Feng, Carlo Siebenschuh, Rui Zhang, Evangelia Spyrou, Venkat Krishnan, Benjamin F. Hobbs, Jie Zhang
Summary: This study proposes a weather-informed method for estimating ramping needs, using forecasts from multiple sites and numerical classifiers to consider system-level weather conditions. Compared to baseline methods, this approach reduces system ramping requirements, improving both system reliability and economics.
Article
Thermodynamics
Ghali Yakoub, Sathyajith Mathew, Joao Leal
Summary: This paper presents short- and medium-term wind power forecasting systems for the Nordic energy market. Multiple numerical weather prediction sources are integrated to predict power at the individual turbine level. Both direct and indirect forecasting approaches are considered and compared, using an automated machine-learning pipeline. The proposed forecasting schemes reduce forecasting errors by 8% to 22% when using inputs from multiple NWP sources, and the wind downscaling model significantly improves accuracy.
Article
Green & Sustainable Science & Technology
Antonio Couto, Ana Estanqueiro
Summary: Accurately predicting the quantity of energy produced by wind power plants is crucial for optimal integration into power systems and markets. This study proposes a new feature identification method based on numerical weather prediction and incorporates a sequential forward feature selection algorithm to reduce wind power forecast errors. The results show that specific meteorological parameters are necessary for different wind parks to achieve the best performance.
Article
Engineering, Electrical & Electronic
Yunyi Li, Can Wan, Dawei Chen, Yonghua Song
Summary: This paper proposes a novel nonparametric probabilistic optimal power flow (N-POPF) model that uses quantiles to describe probabilistic information without making assumptions about the probability distribution of random variables. Additionally, a critical region integral method (CRIM) is introduced to efficiently solve the N-POPF problem by combining multiparametric programming theory and discrete integral. The experimental results demonstrate the superior performance of the proposed CRIM in terms of estimation accuracy and computational efficiency, and prove that the N-POPF model significantly improves the accuracy of uncertainty analysis.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2022)
Article
Energy & Fuels
Chenjia Hu, Yan Zhao, He Jiang, Mingkun Jiang, Fucai You, Qian Liu
Summary: This paper proposes a neural network model based on CEEMDAN-LSTM-TCN for predicting ultra-short term wind energy. By decomposing wind velocity data and establishing the model, it achieves real-time prediction of wind energy with good forecasting performance.
Article
Green & Sustainable Science & Technology
Zhong Zheng, Luoxiao Yang, Zijun Zhang
Summary: In this article, a conditional variational autoencoder based method is proposed for the probabilistic wind power curve modeling task. Experimental results show that the proposed method has high performance and reliability in simulating wind power curves.
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
(2023)
Article
Thermodynamics
Yong Cheng, Fukai Song, Lei Fu, Saishuai Dai, Zhiming Yuan, Atilla Incecik
Summary: This paper investigates the accessibility of wave energy absorption by a dual-pontoon floating breakwater integrated with hybrid-type wave energy converters (WECs) and proposes a hydraulic-pneumatic complementary energy extraction method. The performance of the system is validated through experiments and comparative analysis.
Article
Thermodynamics
Jing Gao, Chao Wang, Zhanwu Wang, Jin Lin, Runkai Zhang, Xin Wu, Guangyin Xu, Zhenfeng Wang
Summary: This study aims to establish a new integrated method for biomass cogeneration project site selection, with a focus on the application of the model in Henan Province. By integrating Geographic Information System and Multiple Criterion Decision Making methods, the study conducts site selection in two stages, providing a theoretical reference for the construction of biomass cogeneration projects.
Article
Thermodynamics
Mert Temiz, Ibrahim Dincer
Summary: The current study presents a hybrid small modular nuclear reactor and solar-based system for sustainable communities, integrating floating and bifacial photovoltaic arrays with a small modular reactor. The system efficiently generates power, hydrogen, ammonia, freshwater, and heat for residential, agricultural, and aquaculture facilities. Thermodynamic analysis shows high energy and exergy efficiencies, as well as large-scale ammonia production meeting the needs of metropolitan areas. The hybridization of nuclear and solar technologies offers advantages of reliability, environmental friendliness, and cost efficiency compared to renewable-alone and fossil-based systems.
Editorial Material
Thermodynamics
Wojciech Stanek, Wojciech Adamczyk
Article
Thermodynamics
Desheng Xu, Yanfeng Li, Tianmei Du, Hua Zhong, Youbo Huang, Lei Li, Xiangling Duanmu
Summary: This study investigates the optimization of hybrid mechanical-natural ventilation for smoke control in complex metro stations. The results show that atrium fires are more significantly impacted by outdoor temperature variations compared to concourse/platform fires. The gathered high-temperature smoke inside the atrium can reach up to 900 K under a 5 MW train fire energy release. The findings provide crucial engineering insights into integrating weather data and adaptable ventilation protocols for smoke prevention/mitigation.
Article
Thermodynamics
Da Guo, Heping Xie, Mingzhong Gao, Jianan Li, Zhiqiang He, Ling Chen, Cong Li, Le Zhao, Dingming Wang, Yiwei Zhang, Xin Fang, Guikang Liu, Zhongya Zhou, Lin Dai
Summary: This study proposes a new in-situ pressure-preserved coring tool and elaborates its pressure-preserving mechanism. The experimental and field test results demonstrate that this tool has a high pressure-preservation capability and can maintain a stable pressure in deep wells. This study provides a theoretical framework and design standards for the development of similar technologies.
Article
Thermodynamics
Aolin Lai, Qunwei Wang
Summary: This study assesses the impact of China's de-capacity policy on renewable energy development efficiency (REDE) using the Global-MSBM model and the difference-in-differences method. The findings indicate that the policy significantly enhances REDE, promoting technological advancements and marketization. Moreover, regions with stricter environmental regulations experience a higher impact.
Article
Thermodynamics
Mostafa Ghasemi, Hegazy Rezk
Summary: This study utilizes fuzzy modeling and optimization to enhance the performance of microbial fuel cells (MFCs). By simulating and analyzing experimental data sets, the ideal parameter values for increasing power density, COD elimination, and coulombic efficiency were determined. The results demonstrate that the fuzzy model and optimization methods can significantly improve the performance of MFCs.
Article
Thermodynamics
Zhang Ruan, Lianzhong Huang, Kai Wang, Ranqi Ma, Zhongyi Wang, Rui Zhang, Haoyang Zhao, Cong Wang
Summary: This paper proposes a grey box model for fuel consumption prediction of wing-diesel hybrid vessels based on feature construction. By using both parallel and series grey box modeling methods and six machine learning algorithms, twelve combinations of prediction models are established. A feature construction method based on the aerodynamic performance of the wing and the energy relationship of the hybrid system is introduced. The best combination is obtained by considering the root mean square error, and it shows improved accuracy compared to the white box model. The proposed grey box model can accurately predict the daily fuel consumption of wing-diesel hybrid vessels, contributing to operational optimization and the greenization and decarbonization of the shipping industry.
Article
Thermodynamics
Huayi Chang, Nico Heerink, Junbiao Zhang, Ke He
Summary: This study examines the interaction between off-farm employment decisions between couples and household clean energy consumption in rural China, and finds that two-paycheck households are more likely to consume clean energy. The off-farm employment of women is a key factor driving household clean energy consumption to a higher level, with wage-employed wives having a stronger influence on these decisions than self-employed ones.
Article
Thermodynamics
Hanguan Wen, Xiufeng Liu, Ming Yang, Bo Lei, Xu Cheng, Zhe Chen
Summary: Demand-side management is crucial to smart energy systems. This paper proposes a data-driven approach to understand the relationship between energy consumption patterns and household characteristics for better DSM services. The proposed method uses a clustering algorithm to generate optimal customer groups for DSM and a deep learning model for training. The model can predict the possibility of DSM membership for a given household. The results demonstrate the usefulness of weekly energy consumption data and household socio-demographic information for distinguishing consumer groups and the potential for targeted DSM strategies.
Article
Thermodynamics
Xinglan Hou, Xiuping Zhong, Shuaishuai Nie, Yafei Wang, Guigang Tu, Yingrui Ma, Kunyan Liu, Chen Chen
Summary: This study explores the feasibility of utilizing a multi-level horizontal branch well heat recovery system in the Qiabuqia geothermal field. The research systematically investigates the effects of various engineering parameters on production temperature, establishes mathematical models to describe their relationships, and evaluates the economic viability of the system. The findings demonstrate the significant economic feasibility of the multi-level branch well system.
Article
Thermodynamics
Longxin Zhang, Songtao Wang, Site Hu
Summary: This investigation reveals the influence of tip leakage flow on the modern transonic rotor and finds that the increase of tip clearance size leads to a decline in rotor performance. However, an optimal tip clearance size can extend the rotor's stall margin.
Article
Thermodynamics
Kristian Gjoka, Behzad Rismanchi, Robert H. Crawford
Summary: This paper proposes a framework for assessing the performance of 5GDHC systems and demonstrates it through a case study in a university campus in Melbourne, Australia. The results show that 5GDHC systems are a cost-effective and environmentally viable solution in mild climates, and their successful implementation in Australia can create new market opportunities and potential adoption in other countries with similar climatic conditions.
Article
Thermodynamics
Jianwei Li, Guotai Wang, Panpan Yang, Yongshuang Wen, Leian Zhang, Rujun Song, Chengwei Hou
Summary: This study proposes an orientation-adaptive electromagnetic energy harvester by introducing a rotatable bluff body, which allows for self-regulation to cater for changing wind flow direction. Experimental results show that the output power of the energy harvester can be greatly enhanced with increased rotatory inertia of the rotating bluff body, providing a promising solution for harnessing wind-induced vibration energy.