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A Distributed Fusion LSTM Model to Forecast Temperature and Relative Humidity in Smart Buildings | IEEE Conference Publication | IEEE Xplore

A Distributed Fusion LSTM Model to Forecast Temperature and Relative Humidity in Smart Buildings


Abstract:

The Heating, Ventilation, and Air Conditioning (HVAC) systems in the commercial buildings, consume near half of total building energy. Temperature and relative humidity a...Show More

Abstract:

The Heating, Ventilation, and Air Conditioning (HVAC) systems in the commercial buildings, consume near half of total building energy. Temperature and relative humidity as important indexes to evaluate the dynamic operation effect of HVAC systems, where their accurate forecasting is crucial to energy efficient management and indoor thermal comfort. However, the existing forecasting methods are suffering the poor prediction performance due to the strong correlation between temperature and relative humidity. To solve this problem, a novel distributed fusion long short-term memory (LSTM) network (DFL) is proposed, which utilizes the distributed data-fusion technology to achieve the prediction of temperature and humidity simultaneously. To obtain the optimal parameters setting, hyper-parameters analysis on the proposed DFL is conducted. A real-world building dataset is used to validate the potency, and the results show the DFL outperforms other state-of-the-art forecasting methods including SVR, fusion LSTM (FL) and classical LSTM (CL).
Date of Conference: 01-04 August 2021
Date Added to IEEE Xplore: 30 August 2021
ISBN Information:

ISSN Information:

Conference Location: Chengdu, China

Funding Agency:


I. Introduction

The HVAC systems, maintaining residents' thermal comfort in the commercial buildings, consume near half of total building energy in US [1], [2] and have great energy-saving potential. To reduce this part of energy consumption, it is required to stress on the dynamic control of HVAC systems to achieve system-level performance monitoring and performance management. And temperature and humidity as important indexes to evaluate the dynamic operation effect of HVAC systems, where the accurate forecasting is crucial to energy efficient management and occupants' thermal comfort. Traditional forecasting methods, which general consider the accurate prediction on temperature variables only, cannot achieve the prediction of temperature and relative humidity because of the strong coupling.

References

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