Accelerating Energy Forecasting with Data Dimensionality Reduction in a Residential Environment

Rafael Gonçalves (), Diogo Magalhães, Rafael Teixeira, Mário Antunes, Diogo Gomes and Rui L. Aguiar
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Rafael Gonçalves: Instituto de Telecomunicações, 3810-193 Aveiro, Portugal
Diogo Magalhães: Instituto de Telecomunicações, 3810-193 Aveiro, Portugal
Rafael Teixeira: Instituto de Telecomunicações, 3810-193 Aveiro, Portugal
Mário Antunes: Instituto de Telecomunicações, 3810-193 Aveiro, Portugal
Diogo Gomes: Instituto de Telecomunicações, 3810-193 Aveiro, Portugal
Rui L. Aguiar: Instituto de Telecomunicações, 3810-193 Aveiro, Portugal

Energies, 2025, vol. 18, issue 7, 1-18

Abstract: 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 is computationally expensive, as increasing the number of features leads to longer training times. To address this problem, we propose an approach that guarantees fast convergence through dimensionality reduction. Using a synthetic neighborhood dataset, we first validate three deep learning models—an artificial neural network (ANN), a 1D convolutional neural network (1D-CNN), and a long short-term memory (LSTM) network. Then, in order to mitigate the long training time, we apply principal component analysis (PCA) and a variational autoencoder (VAE) for feature reduction. As a way to ensure the suitability of the proposed models for a residential context, we also explore the trade-off between low error and training speed by considering three test scenarios: a global model, a local model for each building, and a global model that is fine-tuned for each building. Our results demonstrate that by selecting the optimal dimensionality reduction method and model architecture, it is possible to decrease the mean squared error (MSE) by up to 63% and accelerate training by up to 80%.

Keywords: residential energy forecasting; machine learning; deep learning; artificial neural network; convolutional neural network; long short-term memory; dimensionality reduction; principal component analysis; variational autoencoder (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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