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Commercial building HVAC control method based on multi-agent deep reinforcement learning

A reinforcement learning and multi-agent technology, applied in neural learning methods, instruments, biological neural network models, etc., can solve problems such as low scalability and low performance, and achieve wide applicability, high scalability, and lower average The effect of energy costs

Active Publication Date: 2020-05-12
NANJING UNIV OF POSTS & TELECOMM
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AI Technical Summary

Problems solved by technology

[0004] Aiming at the deficiencies of the prior art, the present invention provides a multi-zone commercial building HVAC system control method, which aims to solve the low scalability of existing learning-based HVAC system control methods applied to multi-zone commercial buildings sex and low performance issues

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  • Commercial building HVAC control method based on multi-agent deep reinforcement learning
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  • Commercial building HVAC control method based on multi-agent deep reinforcement learning

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Embodiment Construction

[0049] In order to make the purpose, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings. It should be understood that the specific embodiments described here are only used to illustrate the technical solution of the present invention more clearly, but not limit the protection scope of the present invention.

[0050] Such as figure 1 As shown, the design flowchart of the commercial building HVAC control method based on multi-agent deep reinforcement learning provided by the present invention includes the following steps:

[0051] Step 1: On the premise of maintaining the indoor temperature and air quality within the comfortable range, model the HVAC energy cost minimization problem of multi-regional commercial buildings as a Markov game, and design the corresponding environmental state, behavior, and reward function;

[0052] Step 2: Train a deep neura...

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Abstract

The invention discloses a commercial building HVAC control method based on multi-agent deep reinforcement learning. The method comprises the following steps: (1) on the premise of maintaining indoor temperature and air quality in a comfortable range, modeling a multi-region commercial building HVAC energy cost minimization problem into a Markov game, and designing a corresponding environment state, behavior and award function; (2) training a deep neural network by using a multi-agent actor-attention-reviewer reinforcement learning algorithm; and (3) in practical application, obtaining decisions about the position of an HVAC air valve and the air supply rate of each region according to the trained deep neural network and new environment state input. Compared with an existing method, the method provided by the invention does not need to know any priori information of a building thermodynamic model and uncertainty parameters, and has greater energy cost saving potential and higher expandability.

Description

technical field [0001] The invention relates to a commercial building HVAC control method based on multi-agent deep reinforcement learning, which belongs to the intersection field of commercial building HVAC systems and artificial intelligence. Background technique [0002] As large consumers of electricity in the smart grid, buildings account for a large percentage of a country's total electricity consumption. For example, in 2010 residential buildings and commercial buildings accounted for 38.7% and 35.5% of total US electricity consumption, respectively. In commercial buildings, about 40%-50% of the total electricity consumption is attributed to HVAC (Heating, Ventilation, and Air Conditioning, HVAC), which brings a great economic burden to building owners. Since the main purpose of the HVAC system is to maintain thermal comfort and air quality comfort, it is very important to minimize the energy cost of the HVAC system in commercial buildings without sacrificing the use...

Claims

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Application Information

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IPC IPC(8): G06Q10/06G06Q50/06G06N3/08
CPCG06Q10/067G06Q50/06G06N3/08
Inventor 余亮孙毅岳东邹玉龙
Owner NANJING UNIV OF POSTS & TELECOMM
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