Details

Autor(en) / Beteiligte

• Wei, Yixuan
• Xia, Liang
• Pan, Song
• Wu, Jinshun
• Zhang, Xingxing
• Han, Mengjie
• Zhang, Weiya
• Xie, Jingchao
• Li, Qingping

Titel

Prediction of occupancy level and energy consumption in office building using blind system identification and neural networks

Ist Teil von

• Applied energy, 2019-04, Vol.240, p.276-294

Ort / Verlag

Elsevier Ltd

Erscheinungsjahr

2019

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• •Energy prediction model has been combined with estimated occupancy profile.•Blind system identification models give accurate estimation of occupancy.•Extra input of occupancy can improve the accuracy of electricity prediction models.•Best performance is obtained with ensemble model in all neural network structures. Occupancy behaviour plays an important role in energy consumption in buildings. Currently, the shallow understanding of occupancy has led to a considerable performance gap between predicted and measured energy use. This paper presents an approach to estimate the occupancy based on blind system identification (BSI), and a prediction model of electricity consumption by an air-conditioning system is developed and reported based on an artificial neural network with the BSI estimation of the number of occupants as an input. This starts from the identification of indoor CO2 dynamics derived from the mass-conservation law and venting levels. The unknown parameters, including the occupancy and model parameters, are estimated by using a frequentist maximum-likelihood algorithm and Bayesian estimation. The second phase is to establish the prediction model of the electricity consumption of the air-conditioning system by using a feed-forward neural network (FFNN) and extreme learning machine (ELM), as well as ensemble models. To analyse some aspects of the benchmark test for identifying the effect of structure parameters and input-selection alternatives, three studies are conducted on (1) the effect of predictor selection based on principal component analysis, (2) the effect of the estimated occupancy as the supplementary input, and (3) the effect of the neural network ensemble. The result shows that the occupancy number, as the input, is able to improve the accuracy in predicting energy consumption using a neural-network model.