CityLearn Chalenge 2023. Control Track.

Buildings are responsible for 30% of greenhouse gas emissions. At the same time buildings are taking a more active role in the power system by providing benefits to the electrical grid. As such, buildings are an unexplored opportunity to address climate change. Advanced control systems such as model predictive control (MPC) and reinforcement learning control (RLC) are well-suited for automated building energy management in a multi-task environment, while adapting to individual characteristics of occupants and buildings. Model-free RLC is a data-driven solution and as more training data become available, it implicitly learns the model of the building or system under control thus, achieves comparable performance as MPC as model-based RLC. One of the strengths of RLC is its ability to adapt to disturbances in the building it controls as the thermal or occupant dynamics change.

The CityLearn Challenge makes use of the CityLearn Gym environment as an opportunity to compete in investigating the potential of artificial intelligence (AI) and distributed control systems to tackle multiple problems within the built-environment domain. It is designed to attract a multidisciplinary participation audience including researchers, industry experts, sustainability enthusiasts and AI hobbyists as a means of crowd-sourcing solutions to these problems.

The CityLearn Challenge 2023 addresses this multi-faceted nature of advanced control in buildings by blending the challenges of control algorithm design, forecast quality and grid-resilience.

The CityLearn Challenge 2023 presents a control track as done in previous challenges as well as introduces an independent forecasting track where, both tracks are run in parallel and utilize the same dataset.

In the control track, participants will develop energy management agent(s) and an optional custom reward function (in RLC solutions) to manage electrical and domestic hot water energy storage systems, and heat pump power in a synthetic single-family neighborhood under normal grid-operation and power outages.

This project will focus on the control track of the above challenge.

In the control track, your objective is to design your own single-agent or multi-agent reinforcement learning control (RLC) policy. You can also opt to develop a custom reward function. This is targeted for electrical (battery) and domestic hot water storage systems, as well as heat pump control within buildings. The overarching goals are to ensure thermal comfort, decrease carbon emissions, enhance energy efficiency, and provide resilience during power outages.