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

Qiao Yan,Guiqing Zhang,Xiaoqian Liu,Wei Peng,Xiaoping Deng

doi:10.3390/a13010021

Abstract

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.

Highlights

Building energy consumption has increased dramatically due to population growth, increased demand for building functions, and global climate change in recent decades [1]
By mining the hidden patterns from office occupants’ equipment usage data and establishing a corresponding personnel energy use behavior model, the most preferred equipment control scheme according to the needs of users can be recommended and configured, and the experience of the office occupants can be improved and accurate demand response strategies for office buildings designed
The energy use behavior of the office occupants can be deduced from Table 3

Summary

Introduction

Building energy consumption has increased dramatically due to population growth, increased demand for building functions, and global climate change in recent decades [1]. A large number of studies show that building energy consumption is affected by meteorological parameters, building shape, envelope structure, window-to-wall ratio, etc., it is related to the behaviors of occupants [3]. By mining the hidden patterns from office occupants’ equipment usage data and establishing a corresponding personnel energy use behavior model, the most preferred equipment control scheme according to the needs of users can be recommended and configured, and the experience of the office occupants can be improved and accurate demand response strategies for office buildings designed . The traditional methods of personal behavior prediction mainly include naive Bayes (NB) [5], support vector machine (SVM) [6], k-nearest neighbor (KNN) [7], the hidden Markov model (HMM) [8], and convolutional neural networks (CNN) [9]. Withanage et al [10] used the depth cuboid similarity feature (DCSF)

Objectives

Methods

Results

Conclusion