Regression model-based hourly aggregated electricity demand prediction
The ability to predict ggregated electricity demand of n electrical grid on an hourly basis is crucial for energy and demand management. In this study, demand and its categorical features data for three years are segregated into four seasons and then fed to an efficient Machine Learning Categorical...
Main Authors: | Radharani Panigrahi, Nita R. Patne, Sumanth Pemmada, Ashwini D. Manchalwar |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2022-12-01
|
Series: | Energy Reports |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484722019382 |
Similar Items
-
Early Detection of Stroke for Ensuring Health and Well-Being Based on Categorical Gradient Boosting Machine
by: Isaac Kofi Nti, et al.
Published: (2023-01-01) -
A comparative analysis of boosting algorithms for chronic liver disease prediction
by: Shahid Mohammad Ganie, et al.
Published: (2024-06-01) -
A machine learning model pipeline for detecting wet pavement condition from live scenes of traffic cameras
by: Clint Morris, et al.
Published: (2021-09-01) -
Potential of Remote Sensing Images for Soil Moisture Retrieving Using Ensemble Learning Methods in Vegetation-Covered Area
by: Ya Gao, et al.
Published: (2023-01-01) -
Solar Radiation Forecasting Based on the Hybrid CNN-CatBoost Model
by: Hyojeoung Kim, et al.
Published: (2023-01-01)