**4. Discussion**

The objective of this study was to stabilize/flatten a building energy demand profile during office hours by means of peak shaving and valley filling using a Battery Electric Storage System. This was achieved by defining load shape objectives in the form of a baseline that is determined based on electricity demand forecasts for the building. Before doing so, predicting the electricity demand of the various load groups in the building was achieved through relatively simple models. All individual prediction models of each load group proved to be sufficiently accurate for use in the control strategy of the BESS. Finally, testing the operational strategy with BESS after the predictions resulted in meeting the flattened load shape objectives over 95% of the time in both simulations and practical implementation. The practical implementation was performed without compromising the thermal comfort of the building users. Peak loads, which increase the risk of congestion, were also successfully reduced both in magnitude and duration. Due to BESS losses, total energy consumption is shown to have increased marginally.

Total energy demand forecasting of the building was achieved by combining the separate predictions for each load group. The level of detail required to assess these separate models in order to determine the best performing algorithm makes this approach a labor-intensive process. Even though data-driven machine learning prediction methods are expected to increase prediction accuracy while allowing for higher levels of abstraction, with the current BMS structure of the case study building, the question remains whether that would be practically implementable. The prediction models that were developed in this work were constrained by practical considerations. Nevertheless, the relatively simple prediction models that were developed and optimized proved to be well capable of predicting the building's energy demands with sufficient accuracy within the practical setting.
