# Bottom-Up Model of Random Daily Electrical Load Curve for Office Building
## 1 Introduction
It takes into account the number of residents and their attitudes towards electrical appliance use and combines Danish ownership statistics, power consumption data, daily use patterns of electrical appliances, and empirical data of household electricity consumption to build a model and finally generates 1 min resolution of household electricity demand. [PERSON] [24] developed a residential electricity demand model. The model classifies families according to the number of family members, as well as their differences in behavior and energy consumption. And the activity-based modeling method was used to model and categorize the loads of each terminal appliances so that the working patterns of different appliances can be reflected. [PERSON] et al. [25] determined the probability of family members moving between different states in a day based on the inhomogeneous Markov chain, and then established a model of various energy-consuming activities at home based on individual behaviors to predict the Swedish family's electrical load distribution. In the subsequent research by [PERSON] et al. [26], Markov chain was used to simulate the state of office workers in the room, and the behavior of office workers was linked with the electricity consumption of electrical appliances; a bottom-up model was established to generate a representative office building power load curve in Northern Europe. About the description of appliances, [PERSON] et al. [27] modeled the start and duration of a single electrical appliance based on the use time survey in Italy, and then attached the rated load to each electrical appliance. Finally, the load curve of the house was established by summarizing the consumption of each electrical appliance. [PERSON] and [PERSON] [28] modeled the startup probability of a single electrical appliance as a stochastic process according to seasonal and social factors. After defining household appliance and overall load fluctuation trend for each household based on statistical data, the electrical load is simulated by adding rated power according to startup probability. [PERSON] et al. [29] combined the rated power of the appliance with the behavioral activities of the occupants. Based on the Danish Time Use Survey (DTU), they proposed two methods for modeling household load curves. The first method, DTU's overview of occupant activity, is directly used to determine activity at 10-min intervals. The second method uses the probability of the start time and duration of the occupant activity to determine the activity. Basically, the idea of the bottom-up method is to establish a total load curve starting from electrical appliance. However, in the research of existing scholars, the focus of appliance modeling considerations is almost always on the modeling of the appliance startup process, and then assigning the rated power and operating cycle of the electrical appliances. Few people consider the different power usage after the appliance is started. However, for most appliances, the power used has a certain randomness. In addition, almost all of the appliance models involved in the existing literature are a single model for all types of electrical appliances, but the actual use of different types of electrical appliances is somewhat different. Therefore, this article aims to establish different random power simulation models for different types of electrical appliances based on the presence of personnel in the room to reflect their actual use and then generate electrical load distribution of the office building. This paper proposes a bottom-up model for generating electrical load in office building, using a combination of personnel model and appliance model. First, Markov chain is used to obtain the personnel in the room, and the electrical appliances are classified according to the personnel's driving of electrical appliances.