An integrated federated learning algorithm for short-term load forecasting
Y Yang, Z Wang, S Zhao, J Wu - Electric Power Systems Research, 2023 - Elsevier
Accurate power load forecasting plays an integral role in power systems. To achieve high
prediction accuracy, models need to extract effective features from raw data, and the training …
prediction accuracy, models need to extract effective features from raw data, and the training …
A multi-task learning model for building electrical load prediction
Buildings are one of the largest energy-consuming sectors in the world. Accurate forecasting
of building electricity loads can bring significant environmental and economic benefits by …
of building electricity loads can bring significant environmental and economic benefits by …
Improved sales time series predictions using deep neural networks with spatiotemporal dynamic pattern acquisition mechanism
D Li, K Lin, X Li, J Liao, R Du, D Chen… - Information Processing & …, 2022 - Elsevier
The ability to predict product sales is invaluable for improving many of the routine decisions
essential for the running of an enterprise. One significant challenge of sales prediction is that …
essential for the running of an enterprise. One significant challenge of sales prediction is that …
Landslide evolution state prediction and down-level control based on multi-task learning
S Sun, X Wang, J Li, C Lian - Knowledge-Based Systems, 2022 - Elsevier
In this paper, multi-task learning is introduced into the study of landslide evolution state
prediction and control. Firstly, we define two landslide evolution states and propose a …
prediction and control. Firstly, we define two landslide evolution states and propose a …
Google trends analysis of covid-19 pandemic
The World Health Organization (WHO) announced that COVID-19 was a pandemic disease
on the 11th of March as there were 118K cases in several countries and territories …
on the 11th of March as there were 118K cases in several countries and territories …
Livedi: An anti-theft model based on driving behavior
Anti-theft problem has been challenging since it mainly depends on the existence of external
devices to defend from thefts. Recently, driver behavior analysis using supervised learning …
devices to defend from thefts. Recently, driver behavior analysis using supervised learning …
Csdleeg: Identifying confused students based on eeg using multi-view deep learning
Distance learning has dramatically increased in recent years because of advanced
technology. In addition, numerous universities had to offer courses in online mode in 2020 …
technology. In addition, numerous universities had to offer courses in online mode in 2020 …
MTL-Deep-STF: A multitask learning based deep spatiotemporal fusion model for outdoor air temperature prediction in building HVAC systems
D Qiao, B Shen, X Dong, H Zheng, W Song… - Journal of Building …, 2022 - Elsevier
Buildings consume large quantities of energy. Reducing building energy consumption is
essential to achieving carbon neutrality goals. Building energy consumption is strongly …
essential to achieving carbon neutrality goals. Building energy consumption is strongly …
An integrated federated learning algorithm for short-term load forecasting
Y Yang, Z Wang, S Zhao, J Wu - Electric Power …, 2022 - acuresearchbank.acu.edu.au
Accurate power load forecasting plays an integral role in power systems. To achieve high
prediction accuracy, models need to extract effective features from raw data, and the training …
prediction accuracy, models need to extract effective features from raw data, and the training …
MDG: A Multi-Task Dynamic Graph Generation Framework for Multivariate Time Series Forecasting
L Huang, J Yuan, S Chen, X Li - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
For accurate forecasting of multivariate time series, it is essential to consider the relationship
between temporal and spatial dimensions. Graph neural networks (GNNs) have gained …
between temporal and spatial dimensions. Graph neural networks (GNNs) have gained …