Hyperparameter Tuning of Load-Forecasting Models Using Metaheuristic Optimization Algorithms—A Systematic Review
Load forecasting is an integral part of the power industries. Load-forecasting techniques should minimize the percentage error while prediction future demand. This will inherently help utilities have an uninterrupted power supply. In addition to that, accurate load forecasting can result in saving l...
Saved in:
Main Authors: | Umme Mumtahina, Sanath Alahakoon, Peter Wolfs |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2024-10-01
|
Series: | Mathematics |
Subjects: | |
Online Access: | https://www.mdpi.com/2227-7390/12/21/3353 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Metaheuristics Approach for Hyperparameter Tuning of Convolutional Neural Network
by: Hindriyanto Purnomo, et al.
Published: (2024-06-01) -
Optimizing a Machine Learning Algorithm by a Novel Metaheuristic Approach: A Case Study in Forecasting
by: Bahadır Gülsün, et al.
Published: (2024-12-01) -
Short-Term Load Forecasting in Power Systems Based on the Prophet–BO–XGBoost Model
by: Shuang Zeng, et al.
Published: (2025-01-01) -
Using crafted features and polar bear optimization algorithm for short-term electric load forecast system
by: Mansi Bhatnagar, et al.
Published: (2025-01-01) -
Sales Forecasting with LSTM, Custom Loss Function, and Hyperparameter Optimization: A Case Study
by: Hyasseliny A. Hurtado-Mora, et al.
Published: (2024-10-01)