Smart Energy

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Energies energies	3.0	6.2	2008	17.5 Days	CHF 2600
Sustainability sustainability	3.3	6.8	2009	20 Days	CHF 2400
Applied Sciences applsci	2.5	5.3	2011	17.8 Days	CHF 2400
Sensors sensors	3.4	7.3	2001	16.8 Days	CHF 2600
Smart Cities smartcities	7.0	11.2	2018	25.8 Days	CHF 2000

41 pages, 2764 KiB

Open AccessArticle

A Robust Distributed Algorithm for Solving the Economic Dispatch Problem with the Penetration of Renewables and Battery Systems

by Karel Kubicek, Martin Cech and Martin Strelec

Appl. Sci. 2024, 14(5), 1991; https://doi.org/10.3390/app14051991 - 28 Feb 2024

Cited by 2 | Viewed by 1157

Abstract

In the field of energy networks, for their effective functioning, it is necessary to distribute the required load between all online generating units in a proper way to cover the demand. The load schedule is obtained by solving the so-called Economic Dispatch Problem [...] Read more.

In the field of energy networks, for their effective functioning, it is necessary to distribute the required load between all online generating units in a proper way to cover the demand. The load schedule is obtained by solving the so-called Economic Dispatch Problem (EDP). The EDP can be solved in many ways, resulting in a power distribution plan between online generating units in the network so that the resulting price per unit of energy is minimal. This article focuses on designing a distributed gradient algorithm for solving EDP, supplemented by models of renewable sources, Battery Energy Storage System (BESS), variable fuel prices, and consideration of multiple uncertainties at once. Specifically, these are: time-variable transport delays, noisy gradient calculation, line losses, and drop-off packet representations. The algorithm can thus be denoted as robust, which can work even in unfavorable conditions commonly found in real applications. The capabilities of the presented algorithm will be demonstrated and evaluated on six examples. Full article

(This article belongs to the Topic Smart Energy)

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17 pages, 2549 KiB

Open AccessArticle

Deep-Learning-Based Detection of Transmission Line Insulators

by Jian Zhang, Tian Xiao, Minhang Li and Yucai Zhou

Energies 2023, 16(14), 5560; https://doi.org/10.3390/en16145560 - 23 Jul 2023

Cited by 2 | Viewed by 2107

Abstract

At this stage, the inspection of transmission lines is dominated by UAV inspection. Insulators, as essential equipment for transmission line equipment, are susceptible to various factors during UAV detection, and their detection results often lead to leakages and false detection. Combining deep learning [...] Read more.

At this stage, the inspection of transmission lines is dominated by UAV inspection. Insulators, as essential equipment for transmission line equipment, are susceptible to various factors during UAV detection, and their detection results often lead to leakages and false detection. Combining deep learning detection algorithms with the UAV transmission line inspection system can effectively solve the current sensing problem. To improve the recognition accuracy of insulator detection, the MS-COCO pre-training strategy that combines the FPN module with a cascading R-CNN algorithm based on the ResNeXt-101 network is proposed. The purpose of this paper is to systematically and comprehensively analyze mainstream isolator detection algorithms at the current stage and to verify the effectiveness of the improved Cascade R-CNN X101 model by combining the mAP (mean Average Precision) value and other related evaluation indices. Compared with Faster R-CNN, Retina Net, and other detection algorithms, the model is highly accurate and can effectively deal with the false detection, leakage, and non-recognition of the environment in online special detection. The research in this paper provides a new idea for intelligent fault detection of transmission line insulators and has some reference value for engineering applications. Full article

(This article belongs to the Topic Smart Energy)

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18 pages, 295 KiB

Open AccessArticle

In Control or Being Controlled? Investigating the Control of Space Heating in Smart Homes

by Simon Peter Larsen, Kirsten Gram-Hanssen and Line Valdorff Madsen

Sustainability 2023, 15(12), 9489; https://doi.org/10.3390/su15129489 - 13 Jun 2023

Cited by 2 | Viewed by 1267

Abstract

Low-carbon scenarios for enabling heat demand flexibility in district heating networks include smart home technology (SHT), which can automate control of heating by responding to utility signals while considering household preferences. This study empirically explores how control of space heating using SHT is [...] Read more.

Low-carbon scenarios for enabling heat demand flexibility in district heating networks include smart home technology (SHT), which can automate control of heating by responding to utility signals while considering household preferences. This study empirically explores how control of space heating using SHT is performed in heating practices by occupants. The study is based on in-depth interviews and home tours with occupants living in smart homes in Denmark. The results suggest that (1) practical knowledge, (2) notions of being in control, and (3) temporal aspects of everyday life are of specific importance for how occupants perform control of space heating using SHT. Furthermore, results show how occupants act when feeling out of control. The data illustrate that control of space heating using SHT is performed in a variety of different ways, displaying the dynamic relationships between the materiality of the home, the importance of practical knowledge that occupants draw upon, and the meaning they ascribe to ‘homely’ practices. As SHT limits people’s active engagement in controlling space heating by relying on automated features, the findings presented in this paper highlight how control of space heating is more than the ability to control but concerns the dynamics of social practices performed within and outside of the home. Based on the results, the paper recommends four specific design and policy implications for future SHT solutions. Full article

(This article belongs to the Topic Smart Energy)

19 pages, 4019 KiB

Open AccessArticle

Development of a Solar-Tracking System for Horizontal Single-Axis PV Arrays Using Spatial Projection Analysis

by Bin Huang, Jialiang Huang, Ke Xing, Lida Liao, Peiling Xie, Meng Xiao and Wei Zhao

Energies 2023, 16(10), 4008; https://doi.org/10.3390/en16104008 - 10 May 2023

Cited by 12 | Viewed by 3080

Abstract

Uniaxial trackers are widely employed as the frame for solar photovoltaic (PV) panel installation. However, when used in sloping terrain scenarios such as mountain and hill regions, it is essential to apply a solar-tracking strategy with the sloping factors considered, to eliminate the [...] Read more.

Uniaxial trackers are widely employed as the frame for solar photovoltaic (PV) panel installation. However, when used in sloping terrain scenarios such as mountain and hill regions, it is essential to apply a solar-tracking strategy with the sloping factors considered, to eliminate the shading effects between arrays and reduce the electricity production loss due to terrain changes. Based on a uniaxial tracker on the sloping terrain of a PV farm located in Ningxia, this study established a uniaxial solar-tracking strategy for sloping terrain by integrating a spatial projection model with a dynamic shadow assessment method. In the proposed strategy, the optimal tilt angle of the PV array and related desirable adjustment are identified taking into consideration major parameters such as the shadow area ratio S and the average solar irradiance intensity G. A tool underpinned by Matlab Simulink has also been developed to realize the proposed solar-tracking strategy. With the input of a simulated ramp signal β and the dynamically changed time parameters, the tracking angle of PV arrays over the simulated duration is accurately predicted, followed by a series of experimental validations conducted on the winter solstice and a typical sunny day (15 September). Moreover, the study also explored the terrain impacts on solar tracking by comparing the sloping terrain and flat terrain applications. The analytic and experimental results indicate that (a) the maximum value of the G(β) function could serve as the input to identify the optimal tracking angle; (b) the application of the flat terrain tracking (FTT) strategy in sloping terrain would result in a reduction of average solar irradiance intensity harvested by the PV arrays with varying degrees; (c) in the context of an east–west −7° sloping terrain, compared with the FTT strategy, the sloping terrain tracking (STT) strategy enabled anti-shading tracking, and then increased the daily PV electricity yield by 0.094 kWh/kWp, which is around 1.48% of the daily energy production; (d) given a measurement with annual scale, the STT strategy could cause a 1.26% increase in the energy harvesting with a flat uniaxial PV array on a −7° slope terrain, achieving an annual increase of 25.16 kWh/kWp. The experimental comparative analysis validated the precision of the proposed solar-tracking model, which has far-reaching significance for achieving automatic solar-tracking of PV modules, as well as improving the capacity and efficiency of PV systems. Full article

(This article belongs to the Topic Smart Energy)

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23 pages, 5543 KiB

Open AccessArticle

State Monitoring and Fault Diagnosis of HVDC System via KNN Algorithm with Knowledge Graph: A Practical China Power Grid Case

by Qian Chen, Qiang Li, Jiyang Wu, Jingsong He, Chizu Mao, Ziyou Li and Bo Yang

Sustainability 2023, 15(4), 3717; https://doi.org/10.3390/su15043717 - 17 Feb 2023

Cited by 9 | Viewed by 1971

Abstract

Based on the four sets of faults data measured in the practical LCC-HVDC transmission project of China Southern Power Grid Tianshengqiao (Guangxi Province, China)–Guangzhou (Guangdong Province, China) HVDC transmission project, a fault diagnosis method based on the K-nearest neighbor (KNN) algorithm is proposed [...] Read more.

Based on the four sets of faults data measured in the practical LCC-HVDC transmission project of China Southern Power Grid Tianshengqiao (Guangxi Province, China)–Guangzhou (Guangdong Province, China) HVDC transmission project, a fault diagnosis method based on the K-nearest neighbor (KNN) algorithm is proposed for an HVDC system. This method can effectively and accurately identify four different fault types, aiming to contribute to construction of a future HVDC system knowledge graph (KG). First, function and significance of fault diagnosis for KG are introduced, along with four specific fault scenarios. Then, the fault data are normalized, classified into a training set and a test set, and labeled. Based on this, the KNN fault diagnosis model is established and Euclidean distance (ED) is selected as the metric function of the KNN algorithm. Finally, the training data are conveyed to the model for training and testing, upon which the diagnosis result obtained by the KNN algorithm with a knowledge graph is compared with that of the support vector machine (SVM) algorithm and Bayesian classifier (BC). The simulation results show that the KNN algorithm can achieve the highest diagnosis accuracy, with more than 83.3% diagnostic accuracy under multiple test sets among all three diagnosis methods. Full article

(This article belongs to the Topic Smart Energy)

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25 pages, 4966 KiB

Open AccessArticle

Research on Comprehensive Evaluation and Early Warning of Transmission Lines’ Operation Status Based on Dynamic Cloud Computing

by Minzhen Wang, Cheng Li, Xinheng Wang, Zheyong Piao, Yongsheng Yang, Wentao Dai and Qi Zhang

Sensors 2023, 23(3), 1469; https://doi.org/10.3390/s23031469 - 28 Jan 2023

Cited by 3 | Viewed by 1779

Abstract

The current methods for evaluating the operating condition of electricity transmission lines (ETLs) and providing early warning have several problems, such as the low correlation of data, ignoring the influence of seasonal factors, and strong subjectivity. This paper analyses the sensitive factors that [...] Read more.

The current methods for evaluating the operating condition of electricity transmission lines (ETLs) and providing early warning have several problems, such as the low correlation of data, ignoring the influence of seasonal factors, and strong subjectivity. This paper analyses the sensitive factors that influence dynamic key evaluation indices such as grounding resistance, sag, and wire corrosion, establishes the evaluation criteria of the ETL operation state, and proposes five ETL status levels and seven principles for selecting evaluation indices. Nine grade I evaluation indices and twenty-nine grade II evaluation indices, including passageway and meteorological environments, are determined. The cloud model theory is embedded and used to propose a warning technology for the operation state of ETLs based on inspection defect parameters and the cloud model. Combined with the inspection defect parameters of a line in the Baicheng district of Jilin Province and the critical evaluation index data such as grounding resistance, sag, and wire corrosion, which are used to calculate the timeliness of the data, the solid line is evaluated. The research shows that the dynamic evaluation model is correct and that the ETL status evaluation and early warning method have reasonable practicability. Full article

(This article belongs to the Topic Smart Energy)

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16 pages, 1419 KiB

Open AccessArticle

Design of a Standard and Programmatically Accessible Interface for Smart Meters to Allow Monitoring Automation of the Energy Consumed by the Execution of Computer Software

by Alberto Ortega, Abel Miguel Cano-Delgado, Beatriz Prieto and Jesús González

Sustainability 2023, 15(3), 1900; https://doi.org/10.3390/su15031900 - 19 Jan 2023

Cited by 1 | Viewed by 1943

Abstract

Software has become more computationally demanding nowadays, turning out high-performance software in many cases, implying higher energy and economic expenditure. Indeed, many studies have arisen within the IT community to mitigate the environmental impact of software. Collecting and measuring software’s power consumption has [...] Read more.

Software has become more computationally demanding nowadays, turning out high-performance software in many cases, implying higher energy and economic expenditure. Indeed, many studies have arisen within the IT community to mitigate the environmental impact of software. Collecting and measuring software’s power consumption has become an essential task. This paper proposes the design of a standard interface for any currently available smart meter, which is programmatically accessible from any software application and can collect consumption data transparently while a program is executed. This interface is structured into two layers. The former is a driver that provides an OS-level standard interface to the meter, while the latter is a proxy offering higher-level API for a concrete programming language. This design provides many benefits. It makes it possible to substitute the meter for a different device without affecting the proxy layer. It also allows the presence of multiple proxy implementations to offer a programmatic interface to the meter for several languages. A prototype of the proposed interface design has been implemented for a concrete smart meter and OS to demonstrate its feasibility. It has been tested with two experiments. Firstly, its correct functioning has been validated. Later, the prototype has been applied to monitor the execution of a high-performance program, a machine learning application to select the most relevant features of electroencephalogram data. Full article

(This article belongs to the Topic Smart Energy)

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14 pages, 2399 KiB

Open AccessArticle

Fault Identification Technology of 66 kV Transmission Lines Based on Fault Feature Matrix and IPSO-WNN

by Qi Zhang, Minzhen Wang, Yongsheng Yang, Xinheng Wang, Entie Qi and Cheng Li

Appl. Sci. 2023, 13(2), 1220; https://doi.org/10.3390/app13021220 - 16 Jan 2023

Viewed by 2065

Abstract

Due to the barely resonant earthed system used in the transmission line, it is more challenging to identify faults at a 66 kV voltage level because of insufficient fault identification techniques. In this paper, a 66 kV transmission line fault identification method based [...] Read more.

Due to the barely resonant earthed system used in the transmission line, it is more challenging to identify faults at a 66 kV voltage level because of insufficient fault identification techniques. In this paper, a 66 kV transmission line fault identification method based on a fault characteristic matrix and an improved particle swarm optimization (IPSO)-wavelet neural network (WNN) is proposed to address the difficulties in extracting and detecting characteristic parameters. The maximum matrix of the dbN wavelet was used to determine its decomposition scale and construct the fault characteristic matrix based on the energy values of frequency bands. The decomposition scale of the dbN wavelet was determined by the modulus maximum matrix to ensure the integrity of fault information. The fault feature matrix was then constructed based on the energy values of frequency bands and the fault feature was accurately extracted. In this research, aiming at the problems such as slow convergence speed and a tendency to fall into local minima, the WNN algorithm is enhanced with the IPSO algorithm. This significantly increased the convergence speed of the identification model and its ability to discover the global optimal solution. The simulation results demonstrate that this method can effectively and accurately identify the fault type with high identification accuracy, quick identification, and robust adaptability. Under challenging working conditions, it is capable of accurately identifying the fault type of 66 kV transmission lines. Full article

(This article belongs to the Topic Smart Energy)

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17 pages, 1688 KiB

Open AccessArticle

Fault Location Method of Multi-Terminal Transmission Line Based on Fault Branch Judgment Matrix

by Yongsheng Yang, Qi Zhang, Minzhen Wang, Xinheng Wang and Entie Qi

Appl. Sci. 2023, 13(2), 1174; https://doi.org/10.3390/app13021174 - 15 Jan 2023

Cited by 4 | Viewed by 2007

Abstract

Aiming at the difficulty of fault location of multi-source transmission lines, this paper proposes a fault location method for multi-terminal transmission lines based on a fault branch judgment matrix. The fault traveling wave signal is decomposed by Complete Ensemble Empirical Mode Decomposition with [...] Read more.

Aiming at the difficulty of fault location of multi-source transmission lines, this paper proposes a fault location method for multi-terminal transmission lines based on a fault branch judgment matrix. The fault traveling wave signal is decomposed by Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), and the IMFs sensitive components that can characterize the fault characteristics of the target signals are selected by constructing a correlation-rearrangement entropy function. The arrival time of fault signals at the endpoint has been accurately calibrated by combining them with the Teager Energy Operator (TEO). To eliminate the influence of wave velocity and fault time on the location results, this paper proposes a two-terminal location method based on the line mode component to improve the location accuracy. On this basis, combined with the fault branch judgment matrix, the accurate location of multi-terminal transmission line faults is realized. This method has been shown to have high accuracy in detecting traveling wave heads, accurately judging fault branches, and producing a small error in fault location results. Compared with the existing multi-terminal transmission line fault location algorithm, it has obvious advantages and meets the needs of actual working conditions. Full article

(This article belongs to the Topic Smart Energy)

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15 pages, 3232 KiB

Open AccessArticle

Unsafe Mining Behavior Identification Method Based on an Improved ST-GCN

by Xiangang Cao, Chiyu Zhang, Peng Wang, Hengyang Wei, Shikai Huang and Hu Li

Sustainability 2023, 15(2), 1041; https://doi.org/10.3390/su15021041 - 6 Jan 2023

Cited by 10 | Viewed by 2380

Abstract

Aiming to solve the problems of large environmental interference and complex types of personnel behavior that are difficult to identify in the current identification of unsafe behavior in mining areas, an improved spatial temporal graph convolutional network (ST-GCN) for miners’ unsafe behavior identification [...] Read more.

Aiming to solve the problems of large environmental interference and complex types of personnel behavior that are difficult to identify in the current identification of unsafe behavior in mining areas, an improved spatial temporal graph convolutional network (ST-GCN) for miners’ unsafe behavior identification network in a transportation roadway (NP-AGCN) was proposed. First, the skeleton spatial-temporal map constructed using multi-frame human key points was used for behavior recognition to reduce the interference caused by the complex environment of the coal mine. Second, aiming to solve the problem that the original graph structure cannot learn the association relationship between the non-naturally connected nodes, which leads to the low recognition rate of climbing belts, fighting and other behaviors, the graph structure was reconstructed and the original partitioning strategy was changed to improve the recognition ability of the model for multi-joint interaction behaviors. Finally, in order to alleviate the problem that the graph convolution network has difficulty learning global information due to the small receptive field, multiple self-attention mechanisms were introduced into the graph convolution to improve the recognition ability of the model for unsafe behaviors. In order to verify the detection ability of the model regarding identifying unsafe behaviors of personnel in a coal mine belt area, our model was tested on the public datasets NTU-RGB + D and the self-built datasets of unsafe behaviors in a coal mine belt area. The recognition accuracies of the proposed model in the above datasets were 94.7% and 94.1%, respectively, which were 6.4% and 7.4% higher than the original model, which verified that the proposed model had excellent recognition accuracies. Full article

(This article belongs to the Topic Smart Energy)

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15 pages, 5429 KiB

Open AccessArticle

A Novel Monocular Vision Technique for the Detection of Electric Transmission Tower Tilting Trend

by Yongsheng Yang, Minzhen Wang, Xinheng Wang, Cheng Li, Ziwen Shang and Liying Zhao

Appl. Sci. 2023, 13(1), 407; https://doi.org/10.3390/app13010407 - 28 Dec 2022

Cited by 6 | Viewed by 1654

Abstract

Transmission lines are primarily deployed overhead, and the transmission tower, acting as the fulcrum, can be affected by the unbalanced force of the wire and extreme weather, resulting in the transmission tower tilt, deformation, or collapse. This can jeopardize the safe operation of [...] Read more.

Transmission lines are primarily deployed overhead, and the transmission tower, acting as the fulcrum, can be affected by the unbalanced force of the wire and extreme weather, resulting in the transmission tower tilt, deformation, or collapse. This can jeopardize the safe operation of the power grid and even cause widespread failures, resulting in significant economic losses. Given the limitations of current tower tilt detection methods, this paper proposes a tower tilt detection and analysis method based on monocular vision images. The monocular camera collects the profile and contour features of the tower, and the tower tilt model is combined to realize the calculation and analysis of the tower tilt. Through this improved monocular visual monitoring method, the perception accuracy of the tower tilt is improved by 7.5%, and the axial eccentricity is accurate to ±2 mm. The method provides real-time reliability and simple operation for detecting tower inclination, significantly reducing staff inspection intensity and ensuring the power system operates safely and efficiently. Full article

(This article belongs to the Topic Smart Energy)

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10 pages, 1604 KiB

Open AccessArticle

Intelligent Blanking of Silicon Steel Coil in a Transformer Core Oriented to Green Manufacturing

by Shiqing Wu and Jiahai Wang

Appl. Sci. 2022, 12(23), 12117; https://doi.org/10.3390/app122312117 - 26 Nov 2022

Viewed by 2004

Abstract

For transformer enterprises, energy consumption and environmental pollution mainly occur in the manufacturing process. The conventional manufacturing mode does not conform to the current green manufacturing mode. The green manufacturing mode requires enterprises to improve the transformer production technology, the utilization rate of [...] Read more.

For transformer enterprises, energy consumption and environmental pollution mainly occur in the manufacturing process. The conventional manufacturing mode does not conform to the current green manufacturing mode. The green manufacturing mode requires enterprises to improve the transformer production technology, the utilization rate of materials and equipment, and the production efficiency and to achieve clean production through energy conservation and consumption reduction. The main objective of this research is to schedule the blanking of multiple transformer cores together rather than the conventional calculation conducted one by one. An optimization model of the silicon steel coil blanking is established, an evaluation method for blanking schemes is proposed, algorithms to solve the optimization model are analyzed in detail, and the solving processes and results are compared through a case study. Compared with the current manual calculation, this paper is of practical significance for transformer manufacturers to improve the production efficiency, reduce material waste, meet the personalized market demand characterized by multiple varieties and small batches, and achieve the green manufacturing of transformers. Full article

(This article belongs to the Topic Smart Energy)

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19 pages, 3827 KiB

Open AccessArticle

A Scientometric Analysis of Energy Management in the Past Five Years (2018–2022)

by Soodabeh Ghalambaz and Christopher Neil Hulme

Sustainability 2022, 14(18), 11358; https://doi.org/10.3390/su141811358 - 10 Sep 2022

Cited by 1 | Viewed by 1619

Abstract

Energy management is an essential part of the integration of renewable energy in energy systems, electric vehicles, energy-saving strategies, waste-heat recovery, and building energy. Although many publications considered energy management, no study addressed the connection between scientists, organizations, and countries. The present study [...] Read more.

Energy management is an essential part of the integration of renewable energy in energy systems, electric vehicles, energy-saving strategies, waste-heat recovery, and building energy. Although many publications considered energy management, no study addressed the connection between scientists, organizations, and countries. The present study provides a scientometric analysis that addresses the trend of publications and worldwide dynamic maps of connectivity and scientists, organizations, and countries and their contribution to energy management. The results showed that Javaid Nadeem published the most papers in the field of energy management (90) while Xiao Hu received the most citations (1394). The university with the highest number of publications in energy management is the Islamic Azad University (144 papers), while the Beijing Institute of Technology has received the most citations (2061 citations) and the largest h-index (28). China and the United States are in the first and second rank in terms of total publications, citations, and h-index. Pakistan has the most publications relative to the country’s research and development investment level. The maps of co-authorship show islands of isolated groups of authors. This implies that the researchers in energy management are not well-connected. Bibliographic coupling of countries revealed China and USA are influential contributors in the field, and other countries were coupled mostly through these two countries. Full article

(This article belongs to the Topic Smart Energy)

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9 pages, 2573 KiB

Open AccessCommunication

A Data-Driven Model for Aerodynamic Loads on Road Vehicles Exposed to Gusty Bora-Like Winds

by Hrvoje Kozmar, Marko Jokić, Kyle Butler, Milenko Stegić and Ahsan Kareem

Appl. Sci. 2022, 12(15), 7625; https://doi.org/10.3390/app12157625 - 28 Jul 2022

Viewed by 1248

Abstract

Strong cross-wind gusts can cause the vehicle to overturn or slide off the road. This problem is particularly experienced on bridges, as vehicles are extremely sensitive to complex wind-bridge–vehicle interactions. While quasi-steady atmospheric winds create serious difficulties for vehicles, this fact is exacerbated [...] Read more.

Strong cross-wind gusts can cause the vehicle to overturn or slide off the road. This problem is particularly experienced on bridges, as vehicles are extremely sensitive to complex wind-bridge–vehicle interactions. While quasi-steady atmospheric winds create serious difficulties for vehicles, this fact is exacerbated by gusts of wind, as is the case with bora, where gusts of wind can reach velocities five times the average wind velocity. In the present study, experiments concerning aerodynamic loads experienced by vehicles exposed to gusty, bora-like winds are carried out. It is noted that the wind gusting and vortex shedding determine unsteady wind loads on vehicles. The experimental results are used as a basis for developing a simple data-driven modeling approach capable of predicting the time history of aerodynamic loads on vehicles exposed to cross-wind gusts. The modeling results indicate that a model using more than two-state variables is needed to capture the unsteady aerodynamic loads. Full article

(This article belongs to the Topic Smart Energy)

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