Predicting Short-Term Electricity Demand by Combining the Advantages of ARMA and XGBoost in Fog Computing Environment.
Chuanbin LiXiaosen ZhengZikun YangLi KuangPublished in: Wirel. Commun. Mob. Comput. (2018)
Keyphrases
- short term
- computing environments
- long term
- load forecasting
- power generation
- short term and long term
- grid computing
- stock market
- long term memory
- electricity markets
- short and long term
- mobile agents
- service discovery
- pervasive computing
- forecasting model
- motion prediction
- demand forecasting
- distributed computing environment
- pervasive computing environments
- wind speed
- context awareness
- lead time
- fault diagnosis
- expert systems
- artificial intelligence