# Markov Chain Monte Carlo Based Energy Use Behaviors Prediction of Office Occupants
## 5 Conclusions  
In this paper, a new method for predicting personal energy use behavior was proposed. Based on the data of electrical equipment used by an office occupant for half a year, the prediction of energy use behavior was realized by using the MCMC method. Firstly, the on-off state data of electrical equipment and temperature history data were obtained by building the IEloTS for office application, and the experimental data were obtained by data preprocessing. Secondly, the sampling parameters \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Energy Use Model**} & \multicolumn{3}{c}{MLE} & \multicolumn{3}{c}{**MCMC**} \\ \cline{2-7}  & \(v\) & \(L\) & \(d\) & \(v\) & \(L\) & \(d\) \\ \hline Desktop computer power-on & 33.2179 & [32.4142,34.0216] & 1.6074 & 33.2637 & [32.7747,33.7527] & 0.9780 \\ Desktop computer power-off & 25.7983 & [24.6318,26.9648] & 2.3330 & 25.4528 & [24.6492,26.2564] & 1.6072 \\ Light power-on & 28.3459 & [26.8734,29.8184] & 2.9450 & 27.1368 & [25.9037,28.3366] & 2.4329 \\ Light power-on & 23.4086 & [22.5467,24.2705] & 1.7238 & 23.8457 & [23.6375,24.0539] & 0.4164 \\ Water dispenser power-on & 40.4608 & [39.0547,41.8669] & 2.8122 & 39.7638 & [38.9513,40.5763] & 1.6250 \\ Water dispenser power-on & 26.0109 & [25.1376,26.8847] & 1.7466 & 26.5534 & [25.7375,27.3693] & 1.6318 \\ Air-conditioner heating-on & 17.9635 & [17.0346,18.8924] & 1.8578 & 18.2389 & [17.8934,18.5844] & 0.6910 \\ Air-conditioner heating-off & 26.0184 & [24.7342,27.3076] & 2.5684 & 25.7267 & [24.8763,26.5771] & 1.7008 \\ Air-conditioner cooling-on & 27.8423 & [26.3756,29.390] & 2.9334 & 27.5361 & [26.9108,28.1614] & 1.2506 \\ Air-conditioner cooling-off & 23.4861 & [22.0785,24.8937] & 2.8152 & 23.3462 & [22.7658,23.9216] & 1.1608 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison results of MLE and MCMC. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Device Prediction** & **On Period** & **Off Period** & **On Time** & **Off Time** & **Working Hours (h)** \\ \hline Desktop computer & 7:50–8:00 & 15:20–15:30 & 7:55 & 15:25 & 7.18 \\ Light & 7:40–7:50 & 15:10–15:20 & 7:47 & 15:13 & 7.16 \\ Water dispenser & 8:00–8:10 & 15:20-15:30 & 8:02 & 15:27 & 7.20 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Prediction results of energy use behaviors. were determined by selecting an a priori function model from observation data. Then, the optimal parameters were obtained through continuous sampling with the MH algorithm in MCMC, and the energy use action model, the energy use working hours model, and the air-conditioner energy use behavior model were fitted. Finally, comparative analysis of the experiments verified that the proposed method had high accuracy and stability. The method proposed in this paper was a generic statistical modeling framework. As long as the user behavior had certain statistical patterns, this method could be used for prediction. Therefore, the model could be applied to other types of buildings, such as residential buildings, industrial buildings, and so on. In some cases, equipment states and performance data may not be available. For example, considering the user's privacy, we may not be able to access equipment states of residential buildings, but it is possible to infer the working state information of the equipment by using the aggregated data from the household electricity meters, and then, the method in this paper could be used to predict the user behaviors. Although the proposed method could obtain the rules of personal energy use behaviors, it could not predict the energy use behavior rules with a high resolution. In the future study, we will further improve the accuracy of the algorithm, for example by acquiring longer electrical equipment state data. In addition, we will design energy saving strategies on the basis of office occupants' energy use behaviors. Conceptualization, G.Z. and X.L. ; Methodology, X.L. and Q.Y. ; Validation, Q.Y. ; Formal analysis, X.D. ; Resources, G.Z. ; Writing--original draft preparation, X.L.