An Intelligent Method for Day-Ahead Regional Load Demand Forecasting via Machine-Learning Analysis of Energy Consumption Patterns Across Daily, Weekly, and Annual Scales

An Intelligent Method for Day-Ahead Regional Load Demand Forecasting via Machine-Learning Analysis of Energy Consumption Patterns Across Daily, Weekly, and Annual Scales

Electric power load forecasting is essential for the efficient operation and strategic planning of utilities. Decisions regarding the electric market, power generation, load management, and infrastructure development all rely on accurate load predictions. This work presents a novel methodology for d...

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Bibliographic Details
Main Authors: Monica Borunda, Arturo Ortega Vega, Raul Garduno, Luis Conde, Manuel Adam Medina, Jeannete Ramírez Aparicio, Lorena Magallón Cacho, O. A. Jaramillo
Format: Article
Language:English
Published: MDPI AG 2025-04-01
Series:Applied Sciences
Subjects:
day-ahead forecasting
LSTM NN
hourly electrical load forecasting
similar days
clustering
K-means
Online Access:https://www.mdpi.com/2076-3417/15/9/4717
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