*2.1. Case Study Building*

The case study office building is located in The Netherlands, which is a European country with a temperate oceanic climate (Cfb type) according to the Köppen–Geiger climate classification [38]. The office was built in 1993 and can be described as a traditional office building. The building has an approximate floor area of 1500 m<sup>2</sup> and a practical maximum occupancy count of 35 [19]. The building is provided with a photovoltaic system and a Battery Electric Storage System. An impression of the building is shown in Figure 1. A general overview of the loads of the building is shown in Figure 2.

**Figure 1.** (**Left**) Impression of the building. (**Right**) A Battery Electric Storage System (BESS) installed inside the building.

**Figure 2.** Electrical load diagram of the building.

A detailed description of the subcomponents of the building follows:


(f) **Battery Electric Storage System:** The building is equipped with a Nilar NiMH Battery Electric Storage System (BESS) with 48 kWh of storage capacity. The power conversion system is formed by the combination of a bi-directional inverter and transformer in a single cabinet. The advantage of this configuration is that the equipment can be disconnected completely. This prevents unnecessary power loss when the battery is not utilized. Table 1 provides an overview of the specifications of the Nilar BESS and energy conversion system.

The method used to ultimately flatten the electricity demand profile for the case study building consists of three steps elaborated in Sections 2.2–2.4.



#### *2.2. Step 1: Establish Prediction Models*

Building electricity demand predictions are an essential part of developing a suitable control strategy. The total electricity consumption of the case study building consists of 5 major load groups and a BESS which were extensively monitored. Due to the di fferent behavior of all load groups, di fferent prediction methodologies are proposed depending on the group's characteristics. An advantage of this approach is that the cause of prediction errors is easier to trace back to the load groups which are inaccurately predicted, after which the model could be adjusted or optimized. Another argumen<sup>t</sup> for predicting each load group separately is that relatively simple prediction methods could be used which are specifically designed for predicting the loads of a particular load group. This also makes practical implementation of the predictions in the BMS more transparent. A priori knowledge obtained through inspection of the building's individual load groups and their characteristics enabled the construction of the prediction models as presented. On the other hand, large fluctuations in loads such as the chiller make it exceptionally di fficult to accurately predict electricity demands intra-hourly. Consequently, predictions in this study are calculated for all load groups on a 1 hour resolution. Figure 3 illustrates the overview of subloads and corresponding day-ahead prediction models. BMS data from 1 January 2017 to 31 December 2018 are used in the establishment of the prediction models except for Solargis ® predictions for the outdoor temperature and PV yields. The Solargis dataset contains data from 25 May 2018 to 4 April 2019 (~10 months).

(a) AHU and HVAC control unit:

For this load group, a parametric approach is chosen because of the characteristic S shape of the data (see Figure 4). Parametric modeling techniques involve two steps [41]: first identifying the function form, and then fitting the parameters of the mathematical model. In order to determine a better fit for the mathematical model, the natural logarithm of the dataset is taken, which is also known as a variance-stabilizing transformation.

**Figure 3.** Overview of the subloads and corresponding prediction models.

**Figure 4.** Demand variation in the air-handling unit (AHU) and the Heating Ventilation and Air Conditioning (HVAC) control unit with respect to the outdoor temperature (*Toutdoor*).

Data points with an energy demand < 4 kWh.h−<sup>1</sup> are considered outliers and were removed from the dataset. The data in Figure 4 are plotted in an S shape. This shape can be described mathematically by combining a logistic function and parabolic function. The transformation of this combined equation back to the scale of the original dataset is achieved by exponentiation. The final equation to predict the AHU and HVAC control unit energy demand (*EAHU&controls*) as a function of the ambient temperature (*Toutdoor*) is given by Equation (1).

$$\begin{array}{lcl}E\_{A\text{H}\text{I}\text{I}\&\text{controls},t=i} & = \exp\left(\left(A \cdot T\_{\text{outdoor}}\right)^{2} + B \cdot T\_{\text{outdoor}} + \mathcal{C}\right) \\ & \cdot \left[a + \frac{\beta}{1 + \exp\left(-\gamma \cdot \left(T\_{\text{outdown}} - \delta\right)\right)}\right] \left[\text{kWh} \cdot \text{h}^{-1}\right] \end{array} \tag{1}$$
