How to effectively manage the energy in residential systems: Development and comparison of methods

The transition from fossil fuels presents significant challenges in energy management. The era of simply activating gas power plants to meet electricity demand or refuelling vehicles with petrol in a matter of minutes is coming to an end. To ensure a sustainable energy future, it is crucial to reduce energy consumption when possible and to synchronize electricity consumption and storage with the production from sources with low greenhouse gas emissions. Effective energy management in residential settings can help achieve these goals. This thesis explores a variety of energy management methods used to manage batteries, heat pumps, and other energy-related systems. The thesis presents three simulation cases alongside one real-world experiment to compare energy management done through simple rules, optimization, and machine learning methods. Furthermore, this thesis introduces TreeC, a novel machine learning-based method that learns understandable yet efficient energy management strategies, applicable to many cases. The thesis concludes with a comprehensive analysis of the benefits of energy management methods in electrified residential settings.
The three key contributions of this thesis can be summarized as follows: 1) The developed TreeC method demonstrates competitive performance compared to a variety of energy management approaches, such as model predictive control and reinforcement learning, across a diverse set of energy management problems. The understandability TreeC’s energy management strategies greatly improves the transparency and trustworthiness of machine learning-based energy management. 2) The real-world experiment highlights the need to validate energy management methods on actual hardware and not solely on simulations. Both TreeC and model predictive control performed worse than in simulations, due to, respectively, insufficiently representative training data and the real-time input data not being reliable enough. 3) Energy management algorithms reduce costs in electrified Belgian dwellings, particularly in poorly insulated dwellings, electric vehicle chargers, and photovoltaic installations. However, on average, these cost savings do not yet outweigh the current costs and effort required to implement such algorithms.