## 3 Building Load Type
### Classify Based on Time Span
#### 3.1.3 Medium Term Load Forecasting  
With the continuous growth of energy demand, there is an increasingly urgent need for accurate and powerful demand predictions, which is crucial for formulating effective medium-term strategies and controlling energy usage in the construction sector [61]. However, compared with short-term load forecasting, medium-term forecasting has received relatively less attention [62]. Medium-term power load forecasting not only provides theoretical support for the reasonable maintenance of grid equipment but also plays a key role in the stable and efficient operation of the power system, generator shutdown planning, and supply and demand management [63]. The results of medium-term load forecasting can provide weekly fluctuation information, which plays an important role in planning energy maintenance, agreeing on energy matching mechanisms, and formulating energy strategies, thereby contributing to the optimization of energy system planning and scheduling [64]. Correct load forecasting can not only improve the utilization rate and reliability of electricity but also ensure that the energy system network responds quickly and fully meets user needs [65]. [PERSON] et al. [66] proposed a medium-term load forecasting method based on time-series regressions and neural networks, which takes into account the future weather sequence and improves the accuracy of the medium-term forecasting model. The basic framework of the forecasting method they proposed is shown in Figure 4.  
Figure 3: Block diagram of a stacked ensemble short-term forecasting framework [58].  
In addition, medium-term forecasting also provides valuable supporting information for system operation, maintenance planning, and long-term contract negotiations [67,68].  
At the same time, the time framework of the prediction time domain varies in different studies [69, 70, 71]. For ultra-short-term load prediction, some studies limit it to within a few minutes to an hour, which is used for real-time monitoring. However, some other studies also include the prediction within a day, such as in the scenarios of commercial buildings. For short-term load prediction, it usually ranges from one day to one week, but some studies have shortened it to a few hours to several days, which is related to the building production process or the living patterns of residents. Medium-term load prediction generally focuses on the load fluctuations from a quarter to a year. However, in the research of specific energy markets or building projects, the time span may be shortened to several weeks or months, such as in the scenarios of energy contract negotiations or energy planning for seasonal commercial buildings. Long-term load prediction generally covers several years or more. But considering the changes in the industry and policies,  
Figure 4: Diagram of medium term load forecasting [66].  
some studies also regard the prediction for three to five years as a long-term prediction, for example, in the planning of the emerging green building field.  
These differences stem from the characteristics of the research objects, the requirements of application scenarios, as well as the availability and quality of data. The choice of different time frameworks affects the prediction methods and model construction. For ultra-short-term and short-term load prediction, since the data changes rapidly, models that are sensitive to data changes, such as the LSTM network, are more suitable. For medium-term and long-term load prediction, as more macro factors need to be considered, statistical models such as the autoregressive integrated moving average (ARIMA) model or regression models combined with macroeconomic variables may be more applicable. In practical research and applications, it is necessary to comprehensively consider these factors to select an appropriate time framework so as to ensure the accuracy and practicality of the prediction and provide reliable support for building energy management.