(b) Chiller:

Figure 5 shows a scatter plot of the hourly electricity demand of the chiller as a function of the outdoor temperature. Considering the shape of the data, a linear regression model will be used in this case. Engineering expertise also tells us that measurements at the previous time step (t−1) may have the largest impact on the building cooling load at time t [42]. Therefore, one of the proposed models is a multi-variable linear regression model that uses a time series of *Toutdoor* as an input and provides a prediction for the chiller's energy demand at t = i (*Echiller,t* = *i*) as the output. The mathematical definition of this model is shown in Equation (2). By fitting the coefficients *ai* to the dataset, a single equation is obtained which is capable of providing predictions. The number of terms/variables that should be included in the model is determined through k-fold cross-validation [41] with k = 10. Note that k-fold cross-validation is used for the establishment of the chiller model.

$$\begin{aligned} E\_{\text{cilinear},t=i} &= a\_0 + a\_1 \cdot T\_{\text{outdoor},t=i} + a\_2 \cdot T\_{\text{outdoor},t=i-1} + a\_3 \cdot T\_{\text{outdown},t=i-2} + \dots + a\_{\text{ll}} \\ &\cdot T\_{\text{outdown},t=i-(n-1)} \left[ \text{kWh.h.h}^{-1} \right] \end{aligned} \tag{2}$$

**Figure 5.** Demand variation of the chiller with respect to the outdoor temperature.

(c) Plug loads and lighting:

Occupancy is known to be related to plug loads [43]. However, because the day-ahead occupancy cannot be predicted accurately, an approach is chosen wherein the future plug load and lighting demand are based on (recent) historic demands. The proposed model makes energy demand predictions for an hour *i* (*Epred*,*t*=*<sup>i</sup>*), based on the historic demands of the same clock hour. This model predicts hour *i* based on the power demands at hour *i* of the *N most recent workdays* when predicting workdays, likewise for weekend days. Equation (3) describes the model. In this equation, 24 describes the number of hours per day. This prediction model is hereby named the "recent day model".

$$E\_{prod, t = i} = \frac{\sum\_{k=1}^{N} (E\_{t = i - 24 \star k})}{N} \left[ \text{kWh.h}^{-1} \right] \tag{3}$$

(d) Photovoltaic panels:

Solargis® is a Slovakian company that provides solar, weather, and PV yield forecasts for almost any location on earth. The case study building has been using its services since May 2018. Solargis® provides both temperature and PV yield predictions on an hourly basis for every hour of the day and up to 48 hours ahead.
