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Electricity Consumption Forecast of High-Rise Office Buildings Based on the Long Short-Term Memory Method

Author

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  • Xiaoyu Lin

    (School of Mechanical Engineering, Tongji University, Shanghai 201804, China)

  • Hang Yu

    (School of Mechanical Engineering, Tongji University, Shanghai 201804, China)

  • Meng Wang

    (School of Mechanical Engineering, Tongji University, Shanghai 201804, China)

  • Chaoen Li

    (School of Mechanical Engineering, Tongji University, Shanghai 201804, China)

  • Zi Wang

    (School of Mechanical Engineering, Tongji University, Shanghai 201804, China)

  • Yin Tang

    (School of Mechanical Engineering, Tongji University, Shanghai 201804, China)

Abstract

Various algorithms predominantly use data-driven methods for forecasting building electricity consumption. Among them, algorithms that use deep learning methods and, long and short-term memory (LSTM) have shown strong prediction accuracy in numerous fields. However, the LSTM algorithm still has certain limitations, e.g., the accuracy of forecasting the building air conditioning power consumption was not very high. To explore ways of improving the prediction accuracy, this study selects a high-rise office building in Shanghai to predict the air conditioning power consumption and lighting power consumption, respectively and discusses the influence of weather parameters and schedule parameters on the prediction accuracy. The results demonstrate that using the LSTM algorithm to accurately predict the electricity consumption of air conditioners is more challenging than predicting lighting electricity consumption. To improve the prediction accuracy of air conditioning power consumption, two parameters, relative humidity, and scheduling, must be added to the prediction model.

Suggested Citation

  • Xiaoyu Lin & Hang Yu & Meng Wang & Chaoen Li & Zi Wang & Yin Tang, 2021. "Electricity Consumption Forecast of High-Rise Office Buildings Based on the Long Short-Term Memory Method," Energies, MDPI, vol. 14(16), pages 1-21, August.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:16:p:4785-:d:609694
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    References listed on IDEAS

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    Cited by:

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    6. Razak Olu-Ajayi & Hafiz Alaka & Hakeem Owolabi & Lukman Akanbi & Sikiru Ganiyu, 2023. "Data-Driven Tools for Building Energy Consumption Prediction: A Review," Energies, MDPI, vol. 16(6), pages 1-20, March.
    7. Roozbeh Sadeghian Broujeny & Safa Ben Ayed & Mouadh Matalah, 2023. "Energy Consumption Forecasting in a University Office by Artificial Intelligence Techniques: An Analysis of the Exogenous Data Effect on the Modeling," Energies, MDPI, vol. 16(10), pages 1-21, May.

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