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

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

doi:10.3390/en14164785

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.

Highlights

Various algorithms predominantly use data-driven methods for forecasting building electricity consumption
For the black-box-based prediction model, various methods must be applied and compared, and we introduced the autoregressive integrated moving average model (ARIMA) algorithm and BP algorithm as benchmarks for validating the performance of the long and short-term memory (LSTM) algorithm used in this case
The air-conditioning electricity consumption of office buildings in the Shanghai area was used to verify the accuracy of this algorithm, and the variables that affect the prediction accuracy were selected, and the influence of these variables on the prediction accuracy was studied

Summary

Introduction

Various algorithms predominantly use data-driven methods for forecasting building electricity consumption. The LSTM algorithm still has certain limitations, e.g., the accuracy of forecasting the building air conditioning power consumption was not very high. It enables us to understand and optimize the energy use of buildings and explores potential energy-saving opportunities and proposes better strategies for sustainable urban development [2]. Research on the end-use of building consumption shows that it can be grouped into heating, ventilation, and air conditioning systems (HVAC); lighting and plug-ins; special uses, including elevators, kitchen equipment, etc.; and auxiliary equipment and electrical appliances [3]. Among them, heating, ventilation, and air conditioning systems account for the majority of building energy consumption. Because the building energy prediction problem is a multivariate time prediction problem, Academic Editor: Albert Smalcerz

Methods

Results

Conclusion