Abstract
The control of modern buildings is a complex multi-loop problem due to the integration of renewable energy generation, storage devices, and electric vehicles (EVs). Additionally, it is a complex multi-criteria problem due to the need to optimize overall energy use while satisfying users’ comfort. Both conventional rule-based (RB) controllers, which are difficult to apply in multi-loop settings, and advanced model-based controllers, which require an accurate building model, cannot fulfil the requirements of the building automation industry to solve this problem optimally at low development and commissioning costs. This work presents a fully data-driven pipeline to obtain an optimal control policy from historical building and weather data, thus avoiding the need for complex physics-based modelling. We demonstrate the potential of this method by jointly controlling a room temperature and an EV to minimize the cost of electricity while retaining the comfort of the occupants. We model the room temperature with a recurrent neural network and use it as a simulation environment to learn a deep reinforcement learning (DRL) control policy. It achieves on average 17% energy savings and 19% better comfort satisfaction than a standard RB room temperature controller. When a bidirectional EV is connected additionally and a two-tariff electricity pricing is applied, it successfully leverages the battery and decreases the overall cost of electricity. Finally, we deployed it on a real building, where it achieved up to 30% energy savings while maintaining similar comfort levels compared to a conventional RB room temperature controller.
Highlights
Buildings account for one-third of global primary energy consumption and one-quarter of greenhouse gas (GHG) emissions
On average, when tested over 10’000 historical intervals, the MIMO deep deterministic policy gradient (DDPG) controller achieved 12% better comfort satisfaction, 11% energy savings, 63% less electric vehicles (EVs) charging at home, and 42% energy costs savings compared to two standard RB controllers, for the same weighting factor. 4.2
We demonstrated the method on the joint control of room temperature and bidirectional EVcharging to minimize the energy consumption and maximize occupants thermal comfort while ensuring enough energy stored in the EV upon leaving for the trip
Summary
Buildings account for one-third of global primary energy consumption and one-quarter of greenhouse gas (GHG) emissions. They have been identified as a critical element to enable climate change mitigation [1]. When looking at the energy use during a building’s lifecycle, about 80% of it stems from building operation [2]. Over the last two decades, buildings have become much more complex to operate optimally due to the integration of renewable energy generation, transformation, and storage devices [3]. Multiple energy-flows are possible within a single building, which gives rise to the need for system-wide optimal energy management. The users’ needs for comfort, such as indoor thermal and visual comfort, and having enough energy in the EV battery for the trip, shall be satisfied
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