Energy Cost Driven Heating Control with Reinforcement Learning
نویسندگان
چکیده
The current energy crisis raised concern about the lack of electricity during wintertime, especially that consumption should be cut at peak hours. For building owners, this is visible as rising prices. Availability near real-time data on performance opening new opportunities to optimize flexibility capabilities buildings. This paper presents a reinforcement learning (RL)-based method control heating for minimizing cost and shifting usage away from demand Simulations are carried out with electrically heated single-family houses. results indicate RL, in case varying prices, it possible save money keep indoor thermal comfort an appropriate level.
منابع مشابه
Reinforcement Learning Based PID Control of Wind Energy Conversion Systems
In this paper an adaptive PID controller for Wind Energy Conversion Systems (WECS) has been developed. Theadaptation technique applied to this controller is based on Reinforcement Learning (RL) theory. Nonlinearcharacteristics of wind variations as plant input, wind turbine structure and generator operational behaviordemand for high quality adaptive controller to ensure both robust stability an...
متن کاملreinforcement learning based pid control of wind energy conversion systems
in this paper an adaptive pid controller for wind energy conversion systems (wecs) has been developed. theadaptation technique applied to this controller is based on reinforcement learning (rl) theory. nonlinearcharacteristics of wind variations as plant input, wind turbine structure and generator operational behaviordemand for high quality adaptive controller to ensure both robust stability an...
متن کاملEmotion-Driven Reinforcement Learning
Existing computational models of emotion are primarily concerned with creating more realistic agents, with recent efforts looking into matching human data, including qualitative emotional responses and dynamics. In this paper, our work focuses on the functional benefits of emotion in a cognitive system where emotional feedback helps drive reinforcement learning. Our system is an integration of ...
متن کاملCost-Sensitive Reinforcement Learning
We introduce cost-sensitive regression as a way to introduce information obtained by planning as background knowledge into a relational reinforcement learning algorithm. By offering a trade-off between using knowledge rich, but computationally expensive knowledge resulting from planning like approaches such as minimax search and computationally cheap, but possibly incorrect generalizations, the...
متن کاملCuriosity-driven reinforcement learning with homeostatic regulation
We propose a curiosity reward based on information theory principles and consistent with the animal instinct to maintain certain critical parameters within a bounded range. Our experimental validation shows the added value of the additional homeostatic drive to enhance the overall information gain of a reinforcement learning agent interacting with a complex environment using continuous actions....
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Buildings
سال: 2023
ISSN: ['2075-5309']
DOI: https://doi.org/10.3390/buildings13020427