Load Probability Density Forecasting Under FDI Attacks Based on Double-Layer LSTM Quantile Regression
Accurate load prediction is critical for boosting high-quality electricity use, as well as safety in energy and power systems. However, the power system is fraught with uncertainty, and cyber-attacks on electrical loads result in inaccurate estimates. In this study, a probability density prediction...
Saved in:
| Main Authors: | Pei Zhao, Jie Zhang, Guang Ling |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
|
| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/17/24/6211 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Wind Speed Probability Distribution Based on Adaptive Bandwidth Kernel Density Estimation Model for Wind Farm Application
by: Tin Trung Chau, et al.
Published: (2025-02-01) -
The relationship between host country factors and FDI motivations: Evidence from Korean FDI in ASEAN countries
by: Thuy Thi Nguyen, et al.
Published: (2025-05-01) -
Comparison of Trivariate Copula-Based Conditional Quantile Regression Versus Machine Learning Methods for Estimating Copper Recovery
by: Heber Hernández, et al.
Published: (2025-02-01) -
FDI in the eec-10: a comparative analysis
by: Simona Moagar-Poladian, et al.
Published: (2013-05-01) -
Kernel density estimation of tensile strength and process capacity index calculation of fabric core conveyor belt
by: GU Qi-chun1, HE Wen-hui1, WANG Xiang1, ZHUANG Bing-jian2, ZHANG Jie2
Published: (2025-03-01)