Accelerating Energy Forecasting with Data Dimensionality Reduction in a Residential Environment

Accelerating Energy Forecasting with Data Dimensionality Reduction in a Residential Environment

The non-stationary nature of energy data is a serious challenge for energy forecasting methods. Frequent model updates are necessary to adapt to distribution shifts and avoid performance degradation. However, retraining regression models with lookback windows large enough to capture energy patterns...

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Bibliographic Details
Main Authors:	Rafael Gonçalves, Diogo Magalhães, Rafael Teixeira, Mário Antunes, Diogo Gomes, Rui L. Aguiar
Format:	Article
Language:	English
Published:	MDPI AG 2025-03-01
Series:	Energies
Subjects:	residential energy forecasting machine learning deep learning artificial neural network convolutional neural network long short-term memory
Online Access:	https://www.mdpi.com/1996-1073/18/7/1637
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