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Open AccessArticle

Markov Chain Monte Carlo Based Energy Use Behaviors Prediction of Office Occupants

by Qiao Yan 1,2, Xiaoqian Liu 1,2, Xiaoping Deng 1,2,*, Wei Peng 1,2 and Guiqing Zhang 1,2,*
School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China
Shandong Provincial Key Laboratory of Intelligent Buildings Technology, Jinan 250101, China
Authors to whom correspondence should be addressed.
Algorithms 2020, 13(1), 21;
Received: 2 December 2019 / Revised: 30 December 2019 / Accepted: 8 January 2020 / Published: 9 January 2020
Prediction of energy use behaviors is a necessary prerequisite for designing personalized and scalable energy efficiency programs. The energy use behaviors of office occupants are different from those of residential occupants and have not yet been studied as intensively as residential occupants. This paper proposes a method based on Markov chain Monte Carlo (MCMC) to predict the energy use behaviors of office occupants. Firstly, an indoor electrical Internet of Things system (IEIoTS) for the office scenario is developed to collect the switching state time series data of selected user electrical equipment (desktop computer, water dispenser, light) and the historical environment parameters. Then, the Metropolis–Hastings (MH) algorithm is used to sample and obtain the optimal solution of the parameters for the office occupants’ behavior function, the model of which includes the energy action model, energy working hours model, and air-conditioner energy use behavior model. Finally, comparative experiments are carried out to evaluate the performance of the proposed method. The experimental results show that while the mean value performs similarly in estimating the energy use model, the proposed method outperforms the Maximum Likelihood Estimation (MLE) method on uncertainty quantification with relatively narrower confidence intervals.
Keywords: MCMC; energy use behavior; time series; electrical equipment MCMC; energy use behavior; time series; electrical equipment
MDPI and ACS Style

Yan, Q.; Liu, X.; Deng, X.; Peng, W.; Zhang, G. Markov Chain Monte Carlo Based Energy Use Behaviors Prediction of Office Occupants. Algorithms 2020, 13, 21.

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