Renewable Energy Systems Energy Modeling using Deep Learning Techniques

Sharma, Surya Prakash; Yadav, Dileep Kumar

doi:10.1109/inocon57975.2023.10101286

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…Particularly in the context of renewable energy, where output can be variable and unpredictable, this finding is significant. Sharma and Yadav's (2023) work on managing the intermittency of renewable energy sources further validates this trend, highlighting the growing need for robust and reliable energy infrastructures that can adapt to and balance these fluctuations.…”

Section: Benefit Perceptionmentioning

confidence: 87%

“…Environmentally, the adaptation of smart energy systems to the variable output of renewable energy sources aids in increasing renewable energy generation (Sharma and Yadav, 2023). This capability of smart energy systems contributes to the decarbonization of energy by reducing reliance on fossil fuel-based power generation (Global e-Sustainability Initiative, 2018).…”

Section: Types Of Perceived Benefits Of the Smart Energy Systemmentioning

confidence: 99%

“…IoT, which facilitates the interconnection of devices and individuals via the Internet, enables real-time data exchange, processing, and analysis through AI-applied big data analytics (Park and Jeong, 2018;Raja and Saraswathi, 2023). Recent advancements in machine learning, particularly deep learning techniques, have further enhanced the capabilities of smart energy systems (Sharma and Yadav, 2023).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Study on Consumers’ Perceived Benefits and Risks of Smart Energy System

Park,

Heo,

Lee

2024

IJEEP

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This study explores consumer perceptions of smart energy systems, delving into both the perceived benefits and risks associated with their adoption and usage. This study addresses a crucial gap in understanding the consumer side of smart energy system implementation. Through ordinal logistic regression analysis, the study examines the relationship between various independent variables and an ordinal dependent variable represented on a Likert scale. The findings highlight a significant consumer emphasis on 'Safe Energy System Construction' and 'Economic Benefits,' including 'Home Energy Saving' and 'New Profit Creation.' However, the perceived benefits and risks are influenced by these factors and individual propensities, such as sensitivity to environmental destruction and acceptance of new technology. The study uncovers new areas of concern, exceptionally high energy consumption and the 'Uncertainty of Electricity Rates,' which have not been extensively addressed in previous research. The conclusions drawn from this study suggest a need for balanced policy-making that fosters technological advancement while addressing consumer apprehensions about energy consumption, rate volatility, and privacy. This study contributes to the broader discourse on technology acceptance and the sustainable implementation of smart energy solutions by providing a nuanced understanding of consumer perceptions in the evolving landscape of smart energy systems.

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Section: Benefit Perceptionmentioning

confidence: 87%

Section: Types Of Perceived Benefits Of the Smart Energy Systemmentioning

confidence: 99%