The journal Sustainable Cities and Society has published our paper Fusing TensorFlow with building energy simulation for intelligent energy management in smart cities led by our awesome PhD student Jose. We have developed CitySim, a framework to study multi-agent reinforcement learning using state-of-the art machine learning tools (TensorFlow) integrated with urban energy simulation (CitySim).
Vázquez-Canteli, J., Ulyanin, S., Kämpf, J., and Nagy, Z. “Fusing TensorFlow with building energy simulation for intelligent energy management in smart cities” Sustainable Cities and Society, 2019
Read more about our research here.
- Simulation environment for intelligent energy management in smart cities.
- Fused urban energy simulator CitySim with machine learning library TensorFlow.
- Applied deep reinforcement learning controller in two case studies.
- Controllers first learn off-line from simple controller, then improve on-line.
Buildings account for 35% of the global final energy demand. Efficiency improvements and advanced control strategies have a significant impact in the reduction of energy costs and CO2 emissions. Building energy simulation is widely used to help planners, contractors, and building owners analyse diverse options regarding the planning and management of energy consumption in buildings. Furthermore, recent advances in data processing and computing have led to the development of sophisticated machine learning algorithms that can learn from large datasets, e.g., sensor data from buildings, and use them to develop building-specific adaptive and automatic energy controllers. Control algorithms, such as deep reinforcement learning can tune themselves, are model-free, and economical to implement. In this paper, we introduce an integrated simulation environment that combines CitySim, a fast building energy simulator, and TensorFlow, a platform for efficient implementation of advanced machine learning algorithms. The integration is achieved via Keras—an API for TensorFlow—and a set of text and csv files for data transfer between the applications. This new environment will allow researchers to investigate novel learning control algorithms, and demonstrate their robustness and potential for diverse applications in the built environment. We present two case studies for energy savings and demand response, respectively.