Journal: Operation scheduling in a solar thermal system: A reinforcement learning-based framework
Published in Applied Energy, 2020
Recommended citation: Camila Correa-Jullian, Enrique López Droguett, and José Miguel Cardemil. Operation scheduling in a solar thermal system: A reinforcement learning-based framework. Applied Energy, 268:114943, 2020. doi:https://doi.org/10.1016/j.apenergy.2020.114943
Highlights
- Condition-based decision-making framework with Reinforcement Learning.
- Framework used for scheduling the operation of a solar thermal system.
- Synthetic data generation from TRNSYS simulation.
- Sensitivity analysis based on energy-related KPI for selected actions.
- Beneficial alternative schedules found for July (low solar radiation) setting.
Abstract Reinforcement learning (RL) provides an alternative method for designing condition-based decision making in engineering systems. In this study, a simple and flexible RL tabular Q-learning framework is employed to identify the optimal operation schedules for a solar hot water system according to action–reward feedback. The system is simulated in TRNSYS software. Three energy sources must supply a building’s hot-water demand: low-cost heat from solar thermal collectors and a heat-recovery chiller, coupled to a conventional heat pump. Key performance indicators are used as rewards for balancing the system’s performance with regard to energy efficiency, heat-load delivery, and operational costs. A sensitivity analysis is performed for different reward functions and meteorological conditions. Optimal schedules are obtained for selected scenarios in January, April, July, and October, according to the dynamic conditions of the system. The results indicate that when solar radiation is widely available (October through April), the nominal operation schedule frequently yields the highest performance. However, the obtained schedule differs when the solar radiation is reduced, for instance, in July. On average, with prioritization of the efficient use of both low-cost energy sources, the performance in July can be on average 21% higher than under nominal schedule-based operation.
Keywords: Solar hot water systems; Reinforcement learning; Intelligent control systems; Condition-based decision-making; Q-learning; Machine learning
Recommended citation: Camila Correa-Jullian, Enrique López Droguett, and José Miguel Cardemil. Operation scheduling in a solar thermal system: A reinforcement learning-based framework. Applied Energy, 268:114943, 2020. doi:https://doi.org/10.1016/j.apenergy.2020.114943