## 1 Introduction  
Forecasting electric load demand plays a crucial role in power systems and power economics. The use of forecasting enables system operators to manage and plan energy usage, and it allows utilities to predict electricity prices more accurately. In the past, research on load forecasting was mainly performed at the grid level, and the number of studies on small-scale systems such as buildings is relatively low [1, 2]. As reported by the International Energy Agency (IEA), commercial buildings consume 32% of the final electricity produced in OECD countries [3]. Therefore, it is necessary to optimally manage building electricity consumption and reduce peak loads to achieve not only economic benefits from the perspective of a building owner by saving energy but also environmental benefits by reducing the CO\({}_{2}\) emission generated in the process of energy production. In addition, distributed energy resources such as photovoltaic (PV) generators, energy storage systems (ESSs), and electric vehicle (EV) charging stations are increasingly being integrated into buildings. Moreover, demand response (DR)has become an efficient way to balance the power system to improve its stability, such that the economic efficiency of the entire power system increases [4]. With the background of the deregulated electricity market, building owners may be able to sell their surplus electricity by adequately scheduling or controlling their energy resources and electricity loads. Sellers of surplus energy, who also consume energy, are known as prosumers. Therefore, the more accurate the forecast, the greater the benefits the building owners reap. On the contrary, uncertainty in the forecast reduces the room for efficient energy management, which in turn results in unnecessary costs. Consequently, it is necessary to optimize energy consumption and generation, and the most important part of this is future electricity consumption forecasting. Load forecasting tasks can be categorized by their forecasting horizons. Short-term load forecasting (STLF) is used to forecast an hour, a day, or a few weeks ahead. Mid-term load forecasting (MTLF) forecasts a few months ahead, and long-term load forecasting (LTLF) forecasts more than a year ahead. STLF is usually used for hour-ahead and day-ahead scheduling, or demand response applications. MTLF is typically used for unit commitment or energy trading, and LTLF is used for system planning and decision-making relating to energy policy [5]. In this study, we develop an STLF model, and conduct day-ahead hourly load forecasting of an institutional building, which would be applicable to perform next-day building energy scheduling. One of the strategies for forecasting the electricity load of a building is to conduct physical modeling depending on the thermodynamics of the building. This can be achieved by software such as \"EnergyPlus,\" which can be used to simulate building energy consumption given detailed environmental parameters such as construction details, operation schedules, climate data, and heating, ventilation, and air-conditioning (HVAC) design [6]. The disadvantage of this approach is that it is difficult to obtain such information and to effectively capture the complex characteristics of a building [7]. However, the problem mentioned above can be overcome. Because buildings are increasingly being equipped with advanced metering infrastructure (AMI), the electricity usage patterns of the building can be monitored and the data recorded. The availability of these data has made it possible to forecast future electricity consumption when used in combination with data pertaining to factors that affect the load patterns of a building. Meteorological data, such as the temperature, humidity, or solar radiation, and temporal parameters, such as the day of the week, month, or season, are well known to be associated with building electricity consumption, and many researchers have utilized these factors for forecasting [8; 9; 10; 11; 12].