A Hybrid Feature Pyramid CNN-LSTM Model with Seasonal Inflection Month Correction for Medium- and Long-Term Power Load Forecasting
Accurate medium- and long-term power load forecasting is of great significance for the scientific planning and safe operation of power systems. Monthly power load has multiscale time series correlation and seasonality. The existing models face the problems of insufficient feature extraction and a la...
Main Authors: | Zizhen Cheng, Li Wang, Yumeng Yang |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2023-03-01
|
Series: | Energies |
Subjects: | |
Online Access: | https://www.mdpi.com/1996-1073/16/7/3081 |
Similar Items
-
Improving the Efficiency of Multistep Short-Term Electricity Load Forecasting via R-CNN with ML-LSTM
by: Mohammed F. Alsharekh, et al.
Published: (2022-09-01) -
A CNN–BiLSTM Architecture for Macroeconomic Time Series Forecasting
by: Alessio Staffini
Published: (2023-06-01) -
Accurate Deep Model for Electricity Consumption Forecasting Using Multi-Channel and Multi-Scale Feature Fusion CNN–LSTM
by: Xiaorui Shao, et al.
Published: (2020-04-01) -
The Discharge Forecasting of Multiple Monitoring Station for Humber River by Hybrid LSTM Models
by: Yue Zhang, et al.
Published: (2022-06-01) -
Solar Power Forecasting Using CNN-LSTM Hybrid Model
by: Su-Chang Lim, et al.
Published: (2022-11-01)