AHCI RESEARCH GROUP

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Papers published in international journals,
proceedings of conferences, workshops and books.

OUR RESEARCH

Scientific Publications

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2025

Wang, R. -G.; Tsai, C. H.; Tseng, M. C.; Hong, R. -C.; Syu, H.; Chou, C. -C.

Immersive Smart Meter Data Analytics: Leveraging eXtended Reality with LSTM and LLMs Proceedings Article

In: pp. 32–36, International Workshop on Computer Science and Engineering (WCSE), 2025.

Abstract | Links | BibTeX | Tags: Data Analytics, Data visualization, Decision making, Energy management, Energy-consumption, Exponential growth, Extended reality (XR), Forecasting, Human computer interaction, Immersive, Language Model, Large language model, large language models (LLMs), Long short-term memory, Long Short-Term Memory (LSTM), Short term memory, Smart Grid technologies, Smart Meters, Smart power grids, Visual analytics

@inproceedings{wang_immersive_2025,

title = {Immersive Smart Meter Data Analytics: Leveraging eXtended Reality with LSTM and LLMs},

author = {R. -G. Wang and C. H. Tsai and M. C. Tseng and R. -C. Hong and H. Syu and C. -C. Chou},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105017965008&doi=10.18178%2Fwcse.2025.06.006&partnerID=40&md5=866ab1ca8cdf0372c020f0131f1d68c1},

doi = {10.18178/wcse.2025.06.006},

year  = {2025},

date = {2025-01-01},

pages = {32–36},

publisher = {International Workshop on Computer Science and Engineering (WCSE)},

abstract = {The rapid advancement of smart grid technologies has led to an exponential growth in smart meter data, creating new opportunities for more accurate energy consumption forecasting and immersive data visualization. This study proposes an integrated framework that combines eXtended Reality (XR), Long Short-Term Memory (LSTM) networks, and Large Language Models (LLMs) to enhance smart meter data analytics. The process begins with the application of LSTM to capture temporal dependencies in historical electricity usage data. Subsequently, the Large Language Models (LLMs) are employed to refine these textual forecasts, offering better predictions and explanations that are easily understandable by end-users. Finally, the enriched insights are presented through an XR environment, enabling users to interact with smart meter analytics in an immersive and intuitive way. By visualizing data trends, predictions, and explanatory narratives in a spatial computing interface, users can explore complex information more effectively. This multi-modal approach facilitates better decision-making for energy management, promotes user engagement, and supports smart city initiatives aiming for sustainable energy consumption. The integration of XR, LSTM, and LLMs technologies demonstrates a promising direction for future research and practical applications in smart energy systems. © 2025 Elsevier B.V., All rights reserved.},

keywords = {Data Analytics, Data visualization, Decision making, Energy management, Energy-consumption, Exponential growth, Extended reality (XR), Forecasting, Human computer interaction, Immersive, Language Model, Large language model, large language models (LLMs), Long short-term memory, Long Short-Term Memory (LSTM), Short term memory, Smart Grid technologies, Smart Meters, Smart power grids, Visual analytics},

pubstate = {published},

tppubtype = {inproceedings}

}