A Methodology for Electricity Demand Forecasting Using a Hybrid Approach
Load forecasting (LF) plays a crucial role in energy production planning and scheduling, simplifying budgeting processes, and improving power supply reliability. The available integrated solutions are superior to conventional approaches while considering the uncertainties of weather conditions. The...
Saved in:
| Main Authors: | Fanidhar Dewangan, Monalisa Biswal, Nand Kishor |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11053796/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Hybrid Optimization based on Deep Learning Approach for Short-Term Load Forecast of Electricity Demand in Buildings
by: Charan Sekhar Makula, et al.
Published: (2024-06-01) -
Short-term Load Forecasting Based on CNN-LSTM with Quadratic Decomposition Combined
by: DENG Bowen, et al.
Published: (2023-08-01) -
Short-Term Load Forecasting for Electrical Power Distribution Systems Using Enhanced Deep Neural Networks
by: Shewit Tsegaye, et al.
Published: (2024-01-01) -
Forecasting temperature and rainfall using deep learning for the challenging climates of Northern India
by: Syed Nisar Hussain Bukhari, et al.
Published: (2025-08-01) -
Short-Term Water Demand Forecasting Based on LSTM Using Multi-Input Data
by: Dingtong Wang, et al.
Published: (2024-09-01)