# Bottom-Up Model of Random Daily Electrical Load Curve for Office Building
## 1 Introduction  
The energy consumption of public buildings is about 20% of the total consumption of the building sector in China and 32.5% of it is due to office buildings [1]. In 2021, China's two sessions put forward the \"carbon peak\" and \"carbon neutral\" goals for the first time. In this context, the use of renewable energy is an effective way to reduce fossil fuels' energy consumption. When designing a building energy system, three main questions usually arise: (i) Which energy technology should be used? (ii) What is the optimal capacity? (iii) Which control strategy should be adopted? The answers to these questions are usually highly dependent on load boundary. Accurate design boundary is the premise and foundation to ensure reliable and economic operation of the energy system. Traditional building energy system design methods are basically based on the prerequisite of meeting the basic energy consumption requirements of the building in accordance with relevant regulations and appropriately equipped with the necessary equipment to meet the national energy-saving requirements [2]. The load demand of the whole building is calculated according to the cold, heat, and electrical load indexes designed per unit area of the building. Or using the coefficient method, according to historical statistical data, one can calculate the relevant proportional coefficient, and then analyze and establish the prediction model. For traditional methods, they are generally static models and do notconsider their changing laws over time [3]. With the development of renewable energy, renewable energy carriers such as integrated energy systems and distributed energy systems have become new design forms [4]. When the design perspective is from the wide-area scale such as the city to the local scale of the region and the building, the random feature of the building energy demand becomes a significant feature [5]. If the load changes with time are not considered in the design stage, it is easy to cause unreasonable system equipment configuration [6]. How to construct a design load considering random characteristics is a key link in the reliability design of building renewable energy systems. In addition, constructing the operating load with random characteristics has become the key to realizing the matching of energy supply and demand and the efficient design of the system [7]. In this case, scholars use pre-made standardized load curves or load curves based on statistical analysis of field measurements [8; 9; 10; 11]. This method can cause the results to over-adap to the input load curve, especially when considering larger buildings. The combination of some available measured load curves may lead to statistically uncorrelated results [2]. In addition, the level of time resolution plays an important role in the scale and controller design of the energy system [12]. Measurement data with high time resolution is difficult to retrieve at first and may suffer from measurement errors or be limited to a few data sets. On the other hand, electrical load is more random than heating and cooling loads. In addition to the lack of data mentioned above, this purely descriptive method also lacks insight into the presence of people in the room and the use of appliances. Using a random model to generate a load curve can overcome these shortcomings, because the load curve of each output will definitely be different from the load curve that has been generated. Therefore, it is necessary to establish a random electrical load curve model. At present, there are two commonly used stochastic load curve modeling methods: one is top-down method, and the other is bottom-up method. In the top-down method, the research object is based on a set of measured load curves, which is used to describe the characteristics of input data, and then the load model is formulated according to the extracted characteristics [13].