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Heating and Lighting Load Disaggregation Using Frequency Components and Convolutional Bidirectional Long Short-Term Memory Method

Author

Listed:
  • Mingzhe Zou

    (School of Engineering, The University of Edinburgh, Edinburgh EH9 3DW, UK)

  • Shuyang Zhu

    (School of Engineering, The University of Edinburgh, Edinburgh EH9 3DW, UK)

  • Jiacheng Gu

    (School of Informatics, The University of Edinburgh, Edinburgh EH8 9AB, UK)

  • Lidija M. Korunovic

    (Faculty of Electronic Engineering, University of Nis, 18000 Niš, Serbia)

  • Sasa Z. Djokic

    (School of Engineering, The University of Edinburgh, Edinburgh EH9 3DW, UK)

Abstract

Load disaggregation for the identification of specific load types in the total demands (e.g., demand-manageable loads, such as heating or cooling loads) is becoming increasingly important for the operation of existing and future power supply systems. This paper introduces an approach in which periodical changes in the total demands (e.g., daily, weekly, and seasonal variations) are disaggregated into corresponding frequency components and correlated with the same frequency components in the meteorological variables (e.g., temperature and solar irradiance), allowing to select combinations of frequency components with the strongest correlations as the additional explanatory variables. The paper first presents a novel Fourier series regression method for obtaining target frequency components, which is illustrated on two household-level datasets and one substation-level dataset. These results show that correlations between selected disaggregated frequency components are stronger than the correlations between the original non-disaggregated data. Afterwards, convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) methods are used to represent dependencies among multiple dimensions and to output the estimated disaggregated time series of specific types of loads, where Bayesian optimisation is applied to select hyperparameters of CNN-BiLSTM model. The CNN-BiLSTM and other deep learning models are reported to have excellent performance in many regression problems, but they are often applied as “black box” models without further exploration or analysis of the modelled processes. Therefore, the paper compares CNN-BiLSTM model in which correlated frequency components are used as the additional explanatory variables with a naïve CNN-BiLSTM model (without frequency components). The presented case studies, related to the identification of electrical heating load and lighting load from the total demands, show that the accuracy of disaggregation improves after specific frequency components of the total demand are correlated with the corresponding frequency components of temperature and solar irradiance, i.e., that frequency component-based CNN-BiLSTM model provides a more accurate load disaggregation. Obtained results are also compared/benchmarked against the two other commonly used models, confirming the benefits of the presented load disaggregation methodology.

Suggested Citation

  • Mingzhe Zou & Shuyang Zhu & Jiacheng Gu & Lidija M. Korunovic & Sasa Z. Djokic, 2021. "Heating and Lighting Load Disaggregation Using Frequency Components and Convolutional Bidirectional Long Short-Term Memory Method," Energies, MDPI, vol. 14(16), pages 1-24, August.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:16:p:4831-:d:610558
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    References listed on IDEAS

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    1. Pascal A. Schirmer & Iosif Mporas, 2019. "Statistical and Electrical Features Evaluation for Electrical Appliances Energy Disaggregation," Sustainability, MDPI, vol. 11(11), pages 1-14, June.
    2. Hauke Jan & Kossowski Tomasz, 2011. "Comparison of Values of Pearson's and Spearman's Correlation Coefficients on the Same Sets of Data," Quaestiones Geographicae, Sciendo, vol. 30(2), pages 87-93, June.
    3. Hsueh-Hsien Chang, 2012. "Non-Intrusive Demand Monitoring and Load Identification for Energy Management Systems Based on Transient Feature Analyses," Energies, MDPI, vol. 5(11), pages 1-21, November.
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    1. Huangjie Gong & Rosemary E. Alden & Aron Patrick & Dan M. Ionel, 2022. "Forecast of Community Total Electric Load and HVAC Component Disaggregation through a New LSTM-Based Method," Energies, MDPI, vol. 15(9), pages 1-17, April.

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