[HTML][HTML] Interpretable machine learning for building energy management: A state-of-the-art review
Abstract Machine learning has been widely adopted for improving building energy efficiency
and flexibility in the past decade owing to the ever-increasing availability of massive building …
and flexibility in the past decade owing to the ever-increasing availability of massive building …
Long sequence time-series forecasting with deep learning: A survey
The development of deep learning technology has brought great improvements to the field
of time series forecasting. Short sequence time-series forecasting no longer satisfies the …
of time series forecasting. Short sequence time-series forecasting no longer satisfies the …
Long-range transformers for dynamic spatiotemporal forecasting
Multivariate time series forecasting focuses on predicting future values based on historical
context. State-of-the-art sequence-to-sequence models rely on neural attention between …
context. State-of-the-art sequence-to-sequence models rely on neural attention between …
A short-term wind power prediction method based on dynamic and static feature fusion mining
M Yang, D Wang, W Zhang - Energy, 2023 - Elsevier
Wind power is a kind of time-varying time series with fluctuation characteristics. To take full
advantage of the time-varying value provided by wind power fluctuations, a short-term wind …
advantage of the time-varying value provided by wind power fluctuations, a short-term wind …
One step forward for smart chemical process fault detection and diagnosis
X Bi, R Qin, D Wu, S Zheng, J Zhao - Computers & Chemical Engineering, 2022 - Elsevier
Process fault detection and diagnosis (FDD) is an essential tool to ensure safe production in
chemical industries. After decades of development, despite the promising performance of …
chemical industries. After decades of development, despite the promising performance of …
A novel orthogonal self-attentive variational autoencoder method for interpretable chemical process fault detection and identification
X Bi, J Zhao - Process Safety and Environmental Protection, 2021 - Elsevier
Industrial processes are becoming increasingly large and complex, thus introducing
potential safety risks and requiring an effective approach to maintain safe production …
potential safety risks and requiring an effective approach to maintain safe production …
Short-term weather forecasting using spatial feature attention based LSTM model
MAR Suleman, S Shridevi - IEEE Access, 2022 - ieeexplore.ieee.org
Weather prediction and meteorological analysis contribute significantly towards sustainable
development to reduce the damage from extreme events which could otherwise set-back the …
development to reduce the damage from extreme events which could otherwise set-back the …
Deep learning models for time series forecasting: a review
W Li, KLE Law - IEEE Access, 2024 - ieeexplore.ieee.org
Time series forecasting involves justifying assertions scientifically regarding potential states
or predicting future trends of an event based on historical data recorded at various time …
or predicting future trends of an event based on historical data recorded at various time …
Bridging the Gap: Enhancing storm surge prediction and decision support with bidirectional attention-based LSTM
VK Ian, R Tse, SK Tang, G Pau - Atmosphere, 2023 - mdpi.com
Accurate storm surge forecasting is vital for saving lives and avoiding economic and
infrastructural damage. Failure to accurately predict storm surge can have catastrophic …
infrastructural damage. Failure to accurately predict storm surge can have catastrophic …
Interpretation of time-series deep models: A survey
Deep learning models developed for time-series associated tasks have become more
widely researched nowadays. However, due to the unintuitive nature of time-series data, the …
widely researched nowadays. However, due to the unintuitive nature of time-series data, the …