The digital transformation of modern cities by integrating advanced information,
communication, and computing technologies has marked the epoch of data-driven smart city …

This study explores the applicability of data augmentation techniques for reconstructing
missing energy time-series in limited data regimes. In particular, multiple synthetic copies of …

This study explored methods for handling missing data in the development of machine
learning-based energy benchmarking models, assessing their training time, performance …

Wind turbine data collection often suffers from missing data due to network blockage and
sensor failure. Existing data imputation methods require complete datasets for training and …

Addressing data anomalies (eg, garbage data, outliers, redundant data, and missing data)
plays a vital role in performing accurate analytics (billing, forecasting, load profiling, etc.) on …

Various supervised machine-learning algorithms for wind power forecasting have been
developed in recent years to manage wind power fluctuations and effectively correlate to …

Missing data can pose severe consequences in critical contexts, such as clinical research
based on routinely collected healthcare data. This issue is usually handled with imputation …

This article proposes a generalized neural continual learning-based cybertwin (GNC)
modeling framework to realize develo** one wind turbine (WT) cybertwin serving multiple …

The widespread use of data across various fields has made missing data imputation
technology a crucial tool. High-quality data is essential for effective energy management in …

Smart homes are at the forefront of sustainable living, utilizing advanced monitoring systems
to optimize energy consumption. However, these systems frequently encounter issues with …