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

Xiaoyu Lin, Hang Yu, Meng Wang, Chaoen Li, Zi Wang and Yin Tang
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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

Energies, 2021, vol. 14, issue 16, 1-21

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.

Keywords: building electricity consumption prediction; meteorological parameters; long short-term memory (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: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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