IoT Based Electric Vehicle Application Using Boosting Algorithm for Smart Cities

Urooj, Shabana; Alrowais, Fadwa; Teekaraman, Yuvaraja; Manoharan, Hariprasath; Kuppusamy, Ramya

doi:10.3390/en14041072

Cited by 29 publications

(12 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Performance Measurements. The effectiveness of the integrated algorithm can be proved by simulating the perfor-mance of the proposed method and comparing it with existing models using the terms such as mean absolute percentage error (MAPE), mean absolute error (MAE), and mean square error (MSE) [21][22][23][24][25][26]. From Figure 6 and Table 3, it is perceived that total number of iterations are varied from 10 to 100, and for each iteration periods, the percentage errors are measured.…”

Section: Scenariomentioning

confidence: 99%

Diminution of Smart Grid with Renewable Sources Using Support Vector Machines for Identification of Regression Losses in Large-Scale Systems

Teekaraman

Kirpichnikova

Manoharan

et al. 2022

Wireless Communications and Mobile Computing

Self Cite

View full text Add to dashboard Cite

This article examines the effect of smart grid systems by implementing artificial intelligence (AI) technique with application of renewable energy sources (RES). The current state generation smart grid system follows a high demand on supply of equal energy load to all grid states. However, in conventional techniques, high demand is observed as manual operation is preformed and load problems are not solved within the stipulated time period due to lack of technological advancements. However, applications of AI in smart grid process reduces risk of operation as manual adjustments are converted to highly automated procedures. This type of automatic process identifies the fault location at stage 1 and diagnosis of identified faults will be processed at stage 2. The abovementioned two stage processes will be incorporated with two constant parameters as dummy load is produced to overcome high- to low-power flows. Additionally, a scrap model has been designed to reduce the wastage of power as 100 percent effective progress can be achieved for low- to high-power supplies. To detect the corresponding regression losses in the grid systems, support vector machine (SVM) which completely identifies the previous state loss in the system is integrated. Hence, to analyze the effectiveness of the SVM model, four different scenarios are evaluated and compared with heuristic algorithms, long short-term memory (LSTM), autoregressive indicated moving average (ARIMA), adaptive ARIMA, and linear regression models with distinct performance analysis that includes error in percentage values where a total efficiency of 81% is achieved for projected SVM in all power lines including large-scale systems as compared to existing approaches.

show abstract

Section: Scenariomentioning

confidence: 99%

Diminution of Smart Grid with Renewable Sources Using Support Vector Machines for Identification of Regression Losses in Large-Scale Systems

Teekaraman

Kirpichnikova

Manoharan

et al. 2022

Wireless Communications and Mobile Computing

Self Cite

View full text Add to dashboard Cite

show abstract

“…Urooj et al have presented an IoT‐based EV application using a booting algorithm for smart cities 8 . The IoT devices are employed to monitor the EV battery life through an online mode.…”

Section: Related Workmentioning

confidence: 99%

“…Urooj et al have presented an IoT-based EV application using a booting algorithm for smart cities. 8 The IoT devices are employed to monitor the EV battery life through an online mode. Based on the continuous monitoring, the battery life increases with lesser computational cost.…”

Section: Iot In Ev Energy Managementmentioning

confidence: 99%

“…Table 4 depicts the battery capacity comparison of the proposed system over the existing techniques: Reduced Grid Impact Charging (RGIC) and Extended Gradient Boosting Algorithm (EGBA). 31,8 When compared to the existing niques, the proposed SSS monitoring technique provides an exact level of the battery and also increases the battery charge lifetime. This is owing to the execution of the battery capacity monitoring process in the proposed SSS monitoring system.…”

Section: Battery Capacity Analysismentioning

confidence: 99%

See 1 more Smart Citation

An energy‐efficient blockchain approach for secure communication in IoT‐enabled electric vehicles

Bhaskar¹,

Prasanth

Saranya³

2022

Int J Communication

View full text Add to dashboard Cite

In today's world, electric vehicles (EVs) play a significant role in transportation automation systems, and these vehicles are the replacement for fossil fuel usage vehicles. An EV generally depends on electric charges where the appropriate usage, charging, and energy management are the key constraints in EVs. To overcome these issues, proper energy management is essential in current EV management. In this paper, a novel blockchain-based secure energy management has been proposed to provide efficient energy management in transportation automation. Primarily, the EVs have connected to the Internet of Things (IoT) sensors for collecting information like charging level, distance to be traveled, and location of the EVs. This information has been processed by an information center and transferred to the random forest classifier to identify the price of charging. Afterward, it can be transferred to the power scheduling algorithm for finding the nearest charging location (shortest distance) and time of charging to a specific EV. Finally, this information is stored in blocks to mitigate the misleading of EVs and to offer secure price transactions between the users and charging stations. The results manifest that the proposed scheme provides improved EV management with 94.5% of accuracy and maintains 10% lesser communication overhead as compared with existing state-of-the-art techniques.

show abstract

“…The most important indicators regarding GB and EVs that affect energy sustainability in smart cities are presented in Table 4, which shows the indicators and sub-indicators for GBs and EVs [82][83][84][85][86][87][88][89][90][91][92][93][94]. The implementation of these indicators presented leads to the improvement of the residential life quality of the smart cities in the future.…”

Section: Required Indicatorsmentioning

confidence: 99%

Investigating Smart City Development Based on Green Buildings, Electrical Vehicles and Feasible Indicators

Razmjoo,

Nezhad,

Kaigutha

et al. 2021

Sustainability

View full text Add to dashboard Cite

With a goal of achieving net-zero emissions by developing Smart Cities (SCs) and industrial decarbonization, there is a growing desire to decarbonize the renewable energy sector by accelerating green buildings (GBs) construction, electric vehicles (EVs), and ensuring long-term stability, with the expectation that emissions will need to be reduced by at least two thirds by 2035 and by at least 90% by 2050. Implementing GBs in urban areas and encouraging the use of EVs are cornerstones of transition towards SCs, and practical actions that governments can consider to help with improving the environment and develop SCs. This paper investigates different aspects of smart cities development and introduces new feasible indicators related to GBs and EVs in designing SCs, presenting existing barriers to smart cities development, and solutions to overcome them. The results demonstrate that feasible and achievable policies such as the development of the zero-energy, attention to design parameters, implementation of effective indicators for GBs and EVs, implementing strategies to reduce the cost of production of EVs whilst maintaining good quality standards, load management, and integrating EVs successfully into the electricity system, are important in smart cities development. Therefore, strategies to governments should consider the full dynamics and potential of socio-economic and climate change by implementing new energy policies on increasing investment in EVs, and GBs development by considering energy, energy, techno-economic, and environmental benefits.

show abstract

IoT Based Electric Vehicle Application Using Boosting Algorithm for Smart Cities

Cited by 29 publications

References 23 publications

Diminution of Smart Grid with Renewable Sources Using Support Vector Machines for Identification of Regression Losses in Large-Scale Systems

Diminution of Smart Grid with Renewable Sources Using Support Vector Machines for Identification of Regression Losses in Large-Scale Systems

An energy‐efficient blockchain approach for secure communication in IoT‐enabled electric vehicles

Investigating Smart City Development Based on Green Buildings, Electrical Vehicles and Feasible Indicators

Contact Info

Product

Resources

About