Flow Rate Control in Smart District Heating Systems Using Deep Reinforcement Learning

12/01/2019
by   Tinghao Zhang, et al.
0

At high latitudes, many cities adopt a centralized heating system to improve the energy generation efficiency and to reduce pollution. In multi-tier systems, so-called district heating, there are a few efficient approaches for the flow rate control during the heating process. In this paper, we describe the theoretical methods to solve this problem by deep reinforcement learning and propose a cloud-based heating control system for implementation. A real-world case study shows the effectiveness and practicability of the proposed system controlled by humans, and the simulated experiments for deep reinforcement learning show about 1985.01 gigajoules of heat quantity and 42276.45 tons of water are saved per hour compared with manual control.

READ FULL TEXT
research
11/27/2018

Distributed traffic light control at uncoupled intersections with real-world topology by deep reinforcement learning

This work examines the implications of uncoupled intersections with loca...
research
04/24/2023

Parallel bootstrap-based on-policy deep reinforcement learning for continuous flow control applications

The coupling of deep reinforcement learning to numerical flow control pr...
research
02/07/2018

Efficient collective swimming by harnessing vortices through deep reinforcement learning

Fish in schooling formations navigate complex flow-fields replete with m...
research
10/03/2020

Attractor Selection in Nonlinear Energy Harvesting Using Deep Reinforcement Learning

Recent research efforts demonstrate that the intentional use of nonlinea...
research
09/07/2017

A Deep Reinforcement Learning Chatbot

We present MILABOT: a deep reinforcement learning chatbot developed by t...
research
01/02/2023

Deep reinforcement learning for irrigation scheduling using high-dimensional sensor feedback

Deep reinforcement learning has considerable potential to improve irriga...
research
03/03/2020

Contention Window Optimization in IEEE 802.11ax Networks with Deep Reinforcement Learning

The proper setting of contention window (CW) values has a significant im...

Please sign up or login with your details

Forgot password? Click here to reset