Article
Thermodynamics
Sahra Khazaei, Mehdi Ehsan, Soodabeh Soleymani, Hosein Mohammadnezhad-Shourkaei
Summary: This article proposes a high-accuracy hybrid approach for short-term wind power forecasting using historical data of wind farm and Numerical Weather Prediction (NWP) data, including three stages: wind direction forecasting, wind speed forecasting, and wind power forecasting. The method involves outlier detection, decomposition of time series, feature selection, and prediction using Multilayer Perceptron (MLP) neural network, with evaluation showing very high accuracy when tested with data from the Sotavento wind farm in Spain.
Article
Engineering, Electrical & Electronic
Gholamreza Memarzadeh, Farshid Keynia
Summary: This paper presents a new hybrid forecast model for short-term electricity load and price prediction. By using wavelet transform, feature selection, and deep learning algorithm, the accuracy of predictions has been improved and successfully validated on actual data from multiple electricity markets.
ELECTRIC POWER SYSTEMS RESEARCH
(2021)
Article
Computer Science, Artificial Intelligence
Xiaobo Zhang
Summary: This paper develops a hybrid versatile forecasting framework that provides detailed information about future wind speed uncertainty and demonstrates its value in the decision-making process of wind power operation and management through experiments.
APPLIED INTELLIGENCE
(2023)
Article
Engineering, Civil
Yaodong Liu, Zidong Xu, Hao Wang, Yawei Wang, Jianxiao Mao, Yiming Zhang
Summary: This paper proposes an ensemble Quantile Regression Neural Network (QRNN) model based on Wavelet decomposition (WD) and least absolute shrinkage and selection operator (LASSO) for probabilistic short-term wind speed forecasting. Experimental results show that the proposed model can enhance the performance of short-term wind speed forecasting and reduce the uncertainty of the prediction results.
Article
Automation & Control Systems
Sivanagaraja Tatinati, Yubo Wang, Andy W. H. Khong
Summary: This article proposes a hybrid method combining elastic variational mode decomposition (eVMD) and forecasting random convolution nodes (fRCN) to forecast Gaussian heteroscedastic wind speed time-series. The eVMD algorithm assesses the nonstationary characteristics of the wind speed signal and decomposes it into intrinsic components (ICs). The fRCN method uses local receptive fields to extract features related to local variations and global trends in each IC, which are then learned using extreme learning machines theories. An ensemble unit is utilized to determine appropriate weightages for each forecasted IC before producing the final forecasting values.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Computer Science, Artificial Intelligence
Behrouz Samieiyan, Poorya MohammadiNasab, Mostafa Abbas Mollaei, Fahimeh Hajizadeh, Mohammadreza Kangavari
Summary: Feature selection techniques are crucial for simplifying problems, improving performance, and optimizing computational efficiency while ensuring interpretability. This study presents a novel feature selection algorithm based on the crow search algorithm, which optimizes the balance between global and local search processes and achieves significant feature reduction.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Energy & Fuels
Priya R. Kamath, Kedarnath Senapati
Summary: This paper introduces a modified S-transform (CBST) for wind speed prediction, which uses artificial neural network for predicting subseries at different frequencies, and compares it with methods based on wavelet transform and empirical mode decomposition.
Article
Computer Science, Artificial Intelligence
Jianzhou Wang, Mengzheng Lv, Zhiwu Li, Bo Zeng
Summary: As a renewable and environmentally friendly source of energy, wind energy is widely considered for power generation due to its emission-free and sustainable nature. Accurate wind speed prediction is crucial for effective wind power generation. However, existing forecasting models often overlook the influence of other variables on wind speed and lack optimization algorithms, which leads to lower accuracy and stability. To address this issue, we developed a comprehensive multivariate selection-combination short-term wind speed forecasting system, incorporating advanced feature selection methods, convolutional and recurrent neural networks, and a multi-objective chameleon swarm optimization algorithm. Our proposed system achieved significantly higher accuracy in one-step, two-step, and three-step wind speed prediction compared to single models and other combined models.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Thermodynamics
Huijuan Wu, Keqilao Meng, Daoerji Fan, Zhanqiang Zhang, Qing Liu
Summary: This paper proposes a multistep wind speed prediction model based on a transformer and shows through experiments that it achieves state-of-the-art performance in wind speed forecasting.
Article
Engineering, Electrical & Electronic
Ping Jiang, Zhenkun Liu, Jianzhou Wang, Lifang Zhang
Summary: In this study, a novel wind speed prediction system is proposed, which can conduct point and interval prediction simultaneously. The system successfully integrates the merits of component models and effectively overcomes the disadvantages of traditional prediction methods. Simulation results demonstrate its important application value in the scheduling and management of power systems.
ELECTRIC POWER SYSTEMS RESEARCH
(2022)
Article
Thermodynamics
Fei Wang, Shuang Tong, Yiqian Sun, Yongsheng Xie, Zhao Zhen, Guoqing Li, Chunmei Cao, Neven Duic, Dagui Liu
Summary: This paper proposes an ultra-short-term wind speed hybrid prediction method based on wind process pattern forecasting. By dividing the wind process into different patterns and selecting the corresponding prediction model based on the pattern, the proposed method can reliably forecast future wind speeds.
Article
Energy & Fuels
Ping Fang, Wenlong Fu, Kai Wang, Dongzhen Xiong, Kai Zhang
Summary: Short-term wind speed forecasting plays a positive role in power system dispatch and wind energy utilization. This study proposes an innovative approach that incorporates data preprocessing, multiple individual predictors, and Volterra multi-model fusion to improve accuracy and stability. Experimental results validate the effectiveness of the proposed method.
Article
Green & Sustainable Science & Technology
Jianzhou Wang, Qiwei Li, Bo Zeng
Summary: This study proposes an efficient combined forecasting system that adopts a more advanced multi-layer cooperative combined strategy, which successfully overcomes some drawbacks of current models and achieves higher forecasting accuracy and stability compared to other methods.
SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS
(2021)
Article
Computer Science, Artificial Intelligence
Qiwei Li, Jianzhou Wang, Haipeng Zhang
Summary: Wind speed interval forecasting is increasingly considered important for addressing uncertainties in wind power generation and ensuring power quality and optimal power dispatching. The proposed novel LUBE-based system with innovative feature selection module and unique training algorithm has demonstrated better performance compared to other models, enhancing coverage width criterion by at least 1.8% and 6.8% for the two datasets used in experiments.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Thermodynamics
K. U. Jaseena, Binsu C. Kovoor
Summary: The research investigates various data decomposition techniques, such as Wavelet Transform, Empirical Mode Decomposition, Ensemble Empirical Mode Decomposition, and Empirical Wavelet Transform, for denoising the signal, and forecasts the low and high-frequency sub-series separately using BiDLSTM networks. The empirical results show that the proposed EWT-based hybrid model outperforms other decomposition-based models in accuracy and stability.
ENERGY CONVERSION AND MANAGEMENT
(2021)
Article
Computer Science, Artificial Intelligence
Morteza Karimzadeh Parizi, Farshid Keynia, Amid Khatibi bardsiri
Summary: This paper presents an improved version of a metaheuristic algorithm designed to balance exploration and exploitation, and evaluates its performance on multiple optimization problems, proving its superiority and promising potential.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2021)
Article
Engineering, Electrical & Electronic
Gholamreza Memarzadeh, Farshid Keynia
Summary: This paper presents a new hybrid forecast model for short-term electricity load and price prediction. By using wavelet transform, feature selection, and deep learning algorithm, the accuracy of predictions has been improved and successfully validated on actual data from multiple electricity markets.
ELECTRIC POWER SYSTEMS RESEARCH
(2021)
Article
Green & Sustainable Science & Technology
Azim Heydari, Meysam Majidi Nezhad, Davide Astiaso Garcia, Farshid Keynia, Livio De Santoli
Summary: The study developed a new hybrid intelligent model based on LSTM and MVO algorithms to predict and analyze air pollution from Combined Cycle Power Plants. Applying real data from a plant in Iran, the model showed higher accuracy compared to other combined forecasting benchmark models when considering different network input variables.
CLEAN TECHNOLOGIES AND ENVIRONMENTAL POLICY
(2022)
Review
Engineering, Electrical & Electronic
Mina Mirhosseini, Farshid Keynia
Summary: This article highlights the importance of maintenance planning for the security and proper functioning of the power system, emphasizing the impact of asset management and regular maintenance activities on ensuring system reliability and reducing failures.
IET GENERATION TRANSMISSION & DISTRIBUTION
(2021)
Article
Computer Science, Interdisciplinary Applications
Iraj Naruei, Farshid Keynia
Summary: Nowadays, optimization algorithms inspired by the natural behavior of agents, such as humans, animals, or plants, have become popular in solving various scientific problems. The wild horse optimizer algorithm is inspired by the social behavior of wild horses, particularly their decency behavior where foals leave groups to prevent mating with relatives. The proposed algorithm has shown competitive results compared to other optimization methods in testing.
ENGINEERING WITH COMPUTERS
(2022)
Article
Computer Science, Hardware & Architecture
Saeed Roohollahi, Amid Khatibi Bardsiri, Farshid Keynia
Summary: The existing network measures and sampling algorithms are designed for deterministic binary graphs with fixed weights, which leads to the loss of information contained in the time-varying edge weights of networks. Researchers propose using stochastic graphs with random variables associated with edge weights as a suitable model for analyzing complex social networks. Experimental evaluations are conducted to study the performance of the proposed sampling algorithms based on relative cost, Kendall correlation coefficient, Kolmogorov-Smirnov D statistics, and relative error.
JOURNAL OF SUPERCOMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Iraj Naruei, Farshid Keynia
Summary: The Coot algorithm is a new meta-heuristic method inspired by bird behavior, capable of finding optimal solutions for complex engineering problems. It mimics both irregular and regular movements of birds on the water surface and has shown superior performance compared to other optimization algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Engineering, Electrical & Electronic
Farshid Keynia, Gholamreza Memarzadeh
Summary: The paper proposes a method based on forecasting wind power production, electricity price, and Financial Loss/Gain (FLG) in coordination with energy storage to improve the participation and profit of wind power producers in the electricity energy market.
IET GENERATION TRANSMISSION & DISTRIBUTION
(2022)
Article
Computer Science, Artificial Intelligence
Iraj Naruei, Farshid Keynia, Amir Sabbagh Molahosseini
Summary: This paper introduces a new population-based optimization algorithm called HPO, inspired by the behavior of predator animals. The algorithm shows effective performance in solving test functions and engineering problems.
Article
Energy & Fuels
Gholamreza Memarzadeh, Farshid Keynia
Summary: This research develops a new model to determine the optimal size of energy storage systems for wind power producers, aiming to enhance their profitability in the electricity market. Utilizing historical data and a novel optimization method, the study finds that compressed air energy storage shows promising potential with increased profitability for wind power producers.
JOURNAL OF ENERGY STORAGE
(2021)
Article
Green & Sustainable Science & Technology
Meysam Majidi Nezhad, Azim Heydari, Mehdi Neshat, Farshid Keynia, Giuseppe Piras, Davide Astiaso Garcia
Summary: This paper uses MERRA-2 re-analysis data to study the offshore wind energy potential in the Mediterranean region. The results show that the Aegean Sea, Gulf of Lyon, and the Northern Morocco and Tunisia regions have attractive potential. Additionally, the paper provides Weibull fitting algorithms for wind energy analysis.
Article
Computer Science, Software Engineering
Mohammad Reza Hasanzadeh, Farshid Keynia, Maliheh Hashemipour
Summary: A new combination search algorithm based on indexing its constituent processes is proposed to solve global optimization problems. Proper combination of optimization algorithm processes can be used as a technique to design more powerful algorithms for solving complex real-world problems.
Article
Engineering, Multidisciplinary
Mina Mirhosseini, Azim Heydari, Davide Astiaso Garcia, Francesco Mancini, Farshid Keynia
Summary: This paper proposes a novel analytical method for prioritizing components in distribution systems by introducing a weighted cumulative Reliability-based diagnostic importance factor. By allocating resources to the most critical components, the reliability of the system can be improved and maintenance costs can be reduced.
OPTIMIZATION AND ENGINEERING
(2022)
Article
Energy & Fuels
Farshid Keynia, Mina Mirhosseini, Azim Heydari, Afef Fekih
Summary: This study introduces a new RMC approach that allocates maintenance budgets based on component criticality and importance. A new diagnostic importance factor is introduced to prioritize components for maintenance activities and budget allocation. The proposed RMC framework improves reliability indexes, reduces maintenance cost, and enables efficient management of limited resources.
Article
Green & Sustainable Science & Technology
Azim Heydari, Meysam Majidi Nezhad, Farshid Keynia, Afef Fekih, Nasser Shahsavari-Pour, Davide Astiaso Garcia, Giuseppe Piras
Summary: This paper introduces a new optimization strategy for hybrid-renewable energy systems in microgrids. The strategy combines a multi-objective optimization algorithm, the Taguchi method, and a fuzzy decision-making approach to achieve the best utilization of renewable energy sources while minimizing the cost of energy and power supply probability loss. The strategy was implemented and tested on the design optimization of a hybrid renewable energy system for different scenarios in Sonderborg, Denmark.
JOURNAL OF CLEANER PRODUCTION
(2023)
Article
Thermodynamics
Pengcheng Zhao, Jingang Wang, Liming Sun, Yun Li, Haiting Xia, Wei He
Summary: The production of green hydrogen through water electrolysis is crucial for renewable energy utilization and decarbonization. This research explores the optimal electrode configuration and system design of compactly-assembled industrial electrolyzer. The findings provide valuable insights for industrial application of water electrolysis equipment.
ENERGY CONVERSION AND MANAGEMENT
(2024)
Article
Thermodynamics
V. Baiju, P. Abhishek, S. Harikrishnan
Summary: Thermally driven adsorption desalination systems (ADS) have gained attention as an eco-friendly solution for water scarcity. However, they face challenges related to low water productivity and scalability. To overcome these challenges, integrating ADS with other desalination technologies can create a small-scale hybrid system. This study proposes integrating ADS with a Thermo Electric Dehumidification (TED) unit to enhance its performance.
ENERGY CONVERSION AND MANAGEMENT
(2024)
Article
Thermodynamics
C. X. He, Y. H. Liu, X. Y. Huang, S. B. Wan, Q. Chen, J. Sun, T. S. Zhao
Summary: A decentralized centroid multi-path RC network model is constructed to improve the temperature prediction accuracy compared to traditional RC models. By incorporating multiple heat flow paths and decentralizing thermal capacity, a more accurate prediction is achieved.
ENERGY CONVERSION AND MANAGEMENT
(2024)
Article
Thermodynamics
Chaoying Li, Meng Wang, Nana Li, Di Gu, Chao Yan, Dandan Yuan, Hong Jiang, Baohui Wang, Xirui Wang
Summary: There is an urgent need to shift away from heavy dependence on fossil fuels and embrace renewable energy sources, particularly in the energy-intensive oil refining process. This study presents an innovative concept called the Solar Oil Refinery, which applies solar energy in oil refining. A solar multi-energies-driven hybrid chemical oil refining system that utilizes solar pyrolysis and electrolysis has been developed, significantly improving solar utilization efficiency, cracking rate, and hydrogen yield.
ENERGY CONVERSION AND MANAGEMENT
(2024)
Article
Thermodynamics
Chao Ma, Guanghui Wang, Dingbiao Wang, Xu Peng, Yushen Yang, Xinxin Liu, Chongrui Yang, Jiaheng Chen
Summary: This study proposes a bio-inspired fish-tail wind rotor to improve the wind power efficiency of the traditional Savonius rotor. Through transient simulations and orthogonal experiments, the key factors affecting the performance are identified. A response surface model is constructed to optimize the power coefficient, resulting in an improvement of 9.4% and 6.6% compared to the Savonius rotor.
ENERGY CONVERSION AND MANAGEMENT
(2024)
Article
Thermodynamics
Sina Bahmanziari, Abbas-Ali Zamani
Summary: This paper proposes a new framework for improving electrical energy harvesting from piezoelectric smart tiles through a combination of magnetic plucking, mechanical impact, and mechanical vibration force mechanisms. Experimental results demonstrate a significant increase in energy yield and average energy harvesting time compared to other mechanisms.
ENERGY CONVERSION AND MANAGEMENT
(2024)
Article
Thermodynamics
Nanjiang Dong, Tao Zhang, Rui Wang
Summary: This study establishes a multiobjective mixed-variable configuration optimization model for a comprehensive combined cooling, heating, and power energy system, and proposes an efficient generating operator to optimize this model. The experimental results show that the proposed algorithm performs better than other state-of-the-art algorithms.
ENERGY CONVERSION AND MANAGEMENT
(2024)
Article
Thermodynamics
Ahmed E. Mansy, Eman A. El Desouky, Tarek H. Taha, M. A. Abu-Saied, Hamada El-Gendi, Ranya A. Amer, Zhen-Yu Tian
Summary: This study aims to convert office paper waste into bioethanol through a sustainable pathway. The results show that physiochemical and enzymatic hydrolysis of the waste can yield a high glucose concentration. The optimal conditions were determined using the Box-Behnken design, and a blended membrane was used for ethanol purification.
ENERGY CONVERSION AND MANAGEMENT
(2024)
Article
Thermodynamics
Sven Klute, Marcus Budt, Mathias van Beek, Christian Doetsch
Summary: Heat pumps are crucial for decarbonizing heat supply, and steam generating heat pumps have the potential to decarbonize the industrial sector. This paper presents the current state, technical and economic data, and modeling principles of steam generating heat pumps.
ENERGY CONVERSION AND MANAGEMENT
(2024)
Article
Thermodynamics
Le Zhang, To-Hung Tsui, Yen Wah Tong, Pruk Aggarangsi, Ronghou Liu
Summary: This study investigates the effectiveness of a current-carrying-coil-based magnetic field in promoting anaerobic digestion of chicken manure. The results show that the applied magnetic field increases methane yield, decreases carbon dioxide production, and reduces the concentration of ammonia nitrogen. Microbial community analysis reveals the enrichment of certain methanogenic genera and enhanced metabolic pathways. Pilot-scale experiments confirm the technical effectiveness of the magnetic field assistance in enhancing anaerobic digestion of chicken manure.
ENERGY CONVERSION AND MANAGEMENT
(2024)
Article
Thermodynamics
Bo Chen, Ruiqing Ma, Yang Zhou, Rui Ma, Wentao Jiang, Fan Yang
Summary: This paper presents an advanced energy management strategy for fuel cell hybrid electric heavy-duty vehicles, focusing on speed planning and energy allocation. By utilizing predictive co-optimization control, this strategy ensures safe inter-vehicle distance and minimizes energy demand. Simulation results demonstrate the effectiveness of the proposed method in reducing fuel cell degradation cost and overall operation cost.
ENERGY CONVERSION AND MANAGEMENT
(2024)
Article
Thermodynamics
Fabio Fatigati, Roberto Cipollone
Summary: Organic Rankine Cycle-based microcogeneration systems that use solar sources to generate electricity and hot water can help reduce CO2 emissions in residential energy-intensive sectors. The adoption of a recuperative heat exchanger in these systems improves efficiency, reduces thermal power requirements, and saves on electricity costs.
ENERGY CONVERSION AND MANAGEMENT
(2024)
Article
Thermodynamics
Lipeng He, Renwen Liu, Xuejin Liu, Xiaotian Zheng, Limin Zhang, Jieqiong Lin
Summary: This research proposes a piezoelectric-electromagnetic hybrid energy harvester (PEHEH) for low-frequency wave motion and self-sensing wave environment monitoring. The PEHEH shows promising power output and the ability to self-power and self-sense the wave environment.
ENERGY CONVERSION AND MANAGEMENT
(2024)
Article
Thermodynamics
Shangling Chu, Yang Liu, Zipeng Xu, Heng Zhang, Haiping Chen, Dan Gao
Summary: This paper studies a distributed energy system integrated with solar and natural gas, analyzes the impact of different parameters on its energy utilization and emissions reduction, and obtains the optimal solution through an optimization algorithm. The results show that compared to traditional separation production systems, this integrated system achieves higher energy utilization and greater reduction in carbon emissions.
ENERGY CONVERSION AND MANAGEMENT
(2024)
Article
Thermodynamics
Qingpu Li, Yaqi Ding, Guangming Chen, Yongmei Xuan, Neng Gao, Nian Li, Xinyue Hao
Summary: This paper proposes and studies a piston-type thermally-driven pump with a structure similar to a linear compressor, aiming to eliminate the high-quality energy consumption of existing pumps and replace mechanical pumps. The coupling mechanism of working fluid flow and element dimension is analyzed based on force analysis, and experimental data analysis is used to determine the pump operation stroke. Theoretical simulation is conducted to analyze the correlation mechanism of the piston assembly. The research shows that the thermally-driven pump can greatly reduce power consumption and has potential for industrial applications.
ENERGY CONVERSION AND MANAGEMENT
(2024)