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Accelerating Energy Forecasting with Data Dimensionality Reduction in a Residential Environment

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Listed:
  • 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
    Departamento de Eletrónica, Telecomunicações e Informática, University of Aveiro, 3810-193 Aveiro, Portugal)

  • Diogo Gomes

    (Instituto de Telecomunicações, 3810-193 Aveiro, Portugal
    Departamento de Eletrónica, Telecomunicações e Informática, University of Aveiro, 3810-193 Aveiro, Portugal)

  • Rui L. Aguiar

    (Instituto de Telecomunicações, 3810-193 Aveiro, Portugal
    Departamento de Eletrónica, Telecomunicações e Informática, University of Aveiro, 3810-193 Aveiro, Portugal)

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%.

Suggested Citation

  • Rafael Gonçalves & Diogo Magalhães & Rafael Teixeira & Mário Antunes & Diogo Gomes & Rui L. Aguiar, 2025. "Accelerating Energy Forecasting with Data Dimensionality Reduction in a Residential Environment," Energies, MDPI, vol. 18(7), pages 1-18, March.
  • Handle: RePEc:gam:jeners:v:18:y:2025:i:7:p:1637-:d:1619711
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    References listed on IDEAS

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    1. Yinghui Meng & Sultan Noman Qasem & Manouchehr Shokri & Shahab S, 2020. "Dimension Reduction of Machine Learning-Based Forecasting Models Employing Principal Component Analysis," Mathematics, MDPI, vol. 8(8), pages 1-15, July.
    2. Hong, Tao & Fan, Shu, 2016. "Probabilistic electric load forecasting: A tutorial review," International Journal of Forecasting, Elsevier, vol. 32(3), pages 914-938.
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