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Forecast of Community Total Electric Load and HVAC Component Disaggregation through a New LSTM-Based Method

Author

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  • Huangjie Gong

    (SPARK Laboratory, ECE Department, University of Kentucky, Lexington, KY 40506, USA)

  • Rosemary E. Alden

    (SPARK Laboratory, ECE Department, University of Kentucky, Lexington, KY 40506, USA)

  • Aron Patrick

    (Louisville Gas and Electric and Kentucky Utilities, Louisville, KY 40202, USA)

  • Dan M. Ionel

    (SPARK Laboratory, ECE Department, University of Kentucky, Lexington, KY 40506, USA)

Abstract

The forecast and estimation of total electric power demand of a residential community, its baseload, and its heating ventilation and air-conditioning (HVAC) power component, which represents a very large portion of a community electricity usage, are important enablers for optimal energy controls and utility planning. This paper proposes a method that employs machine learning in a multi-step integrated approach. An LSTM model for total electric power at the main circuit feeder is trained using historic multi-year hourly data, outdoor temperature, and solar irradiance. New key temperature indicators, TmHAVC, corresponding to the standby zero-power operation for HVAC systems for summer cooling and winter heating are introduced using a V-shaped hourly total load curve. The trained LTSM model is additionally run with TmHVAC and zero irradiance inputs yielding an estimated baseload, which is representative of typical occupancy patterns. The HVAC power component is disaggregated as the difference between total and baseload power. Total power forecasts of an aggregated residential community as seen by major distribution lines are experimentally validated with a satisfactory MAPE error below 10% based on a 4-year dataset from a representative suburban community with more than 1800 homes in Kentucky, U.S. Discussions regarding the validity of the separation method based on combined considerations of fundamental physics, statistics, and human behavior are also included.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:9:p:2974-:d:796827
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    References listed on IDEAS

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    1. Hong, Tao & Pinson, Pierre & Fan, Shu & Zareipour, Hamidreza & Troccoli, Alberto & Hyndman, Rob J., 2016. "Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond," International Journal of Forecasting, Elsevier, vol. 32(3), pages 896-913.
    2. Bidong Liu & Jakub Nowotarski & Tao Hong & Rafal Weron, 2015. "Probabilistic load forecasting via Quantile Regression Averaging on sister forecasts," HSC Research Reports HSC/15/01, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    3. 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.
    4. Nick MacMackin, & Miller, Lindsay & Carriveau, Rupp, 2019. "Modeling and disaggregating hourly effects of weather on sectoral electricity demand," Energy, Elsevier, vol. 188(C).
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    Cited by:

    1. Massidda, Luca & Marrocu, Marino, 2023. "Total and thermal load forecasting in residential communities through probabilistic methods and causal machine learning," Applied Energy, Elsevier, vol. 351(C).
    2. Evan S. Jones & Rosemary E. Alden & Huangjie Gong & Dan M. Ionel, 2023. "Co-Simulation of Electric Power Distribution Systems and Buildings including Ultra-Fast HVAC Models and Optimal DER Control," Sustainability, MDPI, vol. 15(12), pages 1-20, June.

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