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Found 3 papers, 1 papers with code
DECODE: Data-driven Energy Consumption Prediction leveraging Historical Data and Environmental Factors in Buildings
In summary, our research contributes to energy prediction by offering a robust LSTM model that outperforms alternative methods and operates with remarkable efficiency, generalizability, and reliability.
Using LSTM for the Prediction of Disruption in ADITYA Tokamak
Major disruptions in tokamak pose a serious threat to the vessel and its surrounding pieces of equipment.
Robust Regression with Compositional Covariates
To this end, we propose a sparse robust statistical regression framework that considers compositional and non-compositional measurements as predictors and identifies outliers in continuous response variables.
Methodology Applications
