Stiimulõppel põhinev nutika kodu küttelahendus Energy2D simulatsioonitarkvaras

Abstract

During the course of this bachelor thesis, a machine learning based heating system was created. System is based on reinforcement learning, which learns and automates heating in simulations running on Energy2D software. To evaulate the system, solutions based on reinforcement learning are compared to a benchmark, which is thermostat based heating. Built reinforcement learning based solution is able to operate the heater in the room autonomously. By configuring the system before training in a way that it depends on electricity price, environment heating profile changes. With this reinforcement learning based system, a 5%-15% savings in money spent on heating was achieved compared to the benchmark, whilst being as good or even better at holding target temperature of the room. At the end of the thesis, shortcomings of this system are indentified and tips to fix them are given. Also, future works are propsed.