Comprehensive approach to modeling and simulation of dynamic soft-sensing design for real-time building energy consumption

Kai Liu,Zhan-Pei Li,Ping Fang,Ting-Zhang Liu

doi:10.1177/1550147717704933

Kai Liu, Zhan-Pei Li + Show 2 more

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https://doi.org/10.1177/1550147717704933

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Abstract

This research develops a multi-layer hybrid soft-sensor model to improve the accuracy of building thermal load prediction using integrated data. The multi-layer hybrid model (autoregressive and par...

Highlights

With the rapid worldwide urbanization progress, building load forecasting has received much attention from both academic researchers and governments.[1,2,3] There has been much research on climate change and comfort standards, among which Kwok and Rajkovich[4] showed that the building sector thermal load accounted for 38.92% of the total primary energy requirements (PER) of the United States, of which 34.9% was used for building energy consumption
The accuracy of the results of the hybrid APNN model hybridized with autoregressive model with exogenous inputs (ARX) and particle swarm optimization neural network (PNN) of 7.654%, 0.54, and 0.411 (July, cooling load (CL)) and 5.877%, 0.238, and 0.192 (January, heating load (HL)), respectively, had an even better overall performance compared to all of the other models with regard to building load forecasting
This article uses actual measured data of a winter day to validate the proposed hybrid model via two methods: (1) a typical 24-h day in winter was selected for study; building exterior meteorological data obtained from the TBS-YG5 meteorological monitoring station are shown in Table 3 in the supplemental materials; these measured meteorological data were input into the DeST building simulation to obtain the current building thermal load; (2) from the frequencydomain characteristics of the heating balance equation (14), a real-time soft-sensing technology for room thermal load is used based on room temperature response frequency-domain characteristics to validate the proposed hybrid APNN model

Summary

Introduction

With the rapid worldwide urbanization progress, building load forecasting has received much attention from both academic researchers and governments.[1,2,3] There has been much research on climate change and comfort standards, among which Kwok and Rajkovich[4] showed that the building sector thermal load accounted for 38.92% of the total primary energy requirements (PER) of the United States, of which 34.9% was used for building energy consumption. In China, building sector thermal load accounted for approximately 24.11% of national energy use in 1996, rising to 27.52% in 2001, and is estimated to increase to approximately 35.12% in 2020.5,6 carbon emissions per capita in China are lower than those in other developing countries, its total emissions are the second only to the United States. The increasing complexity of building designs and higher performance requirements for achieving sustainability, predicting the load of heating, ventilating, and airconditioning (HVAC) systems are important for energy management

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Conclusion