Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

HVAC control system based on multi-step prediction deep reinforcement learning algorithm

A multi-step prediction and reinforcement learning technology, applied in neural learning methods, prediction, system integration technology, etc., can solve problems such as difficult parameter adjustment, affecting modeling accuracy, and not considering the delay of HVAC systems

Active Publication Date: 2021-07-13
TAIYUAN UNIV OF TECH
View PDF8 Cites 9 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] At present, the HVAC system mainly adopts the traditional control method closed-loop control and model predictive control algorithm. There is a temperature sensor inside the closed-loop control system. When it detects that the indoor temperature reaches the set value, the HVAC system will stop working. Based on the closed-loop control method The HVAC system is simple to operate and easy to implement, but in the environment of smart grid and corresponding demand strategy, it is difficult to perform power conversion according to the dynamic electricity price to meet the standards of energy saving and emission reduction; the model predictive control algorithm is based on the establishment of indoor temperature changes Accurate models can then control the HVAC system, however, the complexity of indoor ambient temperature changes will affect the accuracy of the modeling
With the development of intelligent algorithms, researchers have also proposed to use particle swarm optimization algorithm and genetic algorithm optimization to optimize the control of HVAC system. This type of algorithm optimizes the power output of HVAC system under the mechanism of real-time electricity price to reduce the cost of users. , the algorithm has the characteristics of difficult parameter adjustment, and does not consider the delay of the power output of the HVAC system to the indoor temperature change, and does not really guarantee the user's comfort

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • HVAC control system based on multi-step prediction deep reinforcement learning algorithm
  • HVAC control system based on multi-step prediction deep reinforcement learning algorithm
  • HVAC control system based on multi-step prediction deep reinforcement learning algorithm

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0035] The present invention takes the collected real ambient temperature data as the experimental object, and trains and tests the HVAC control system based on the multi-step prediction depth reinforcement learning algorithm

[0036] The HVAC control system based on multi-step prediction deep reinforcement learning algorithm includes two stages: multi-step prediction of outdoor ambient temperature and real-time control of indoor temperature. The prediction stage of outdoor ambient temperature includes the following steps:

[0037] Step 1: According to the actual data points of the outdoor environment, select the outdoor ambient temperature X=[T 1 ,...,T i ] as the input of the model, h=[h i+1 ,...,h i+n ] as the true output of the model, the sampling interval is every 30 minutes.

[0038] Step 2: Preprocess the collected data, correct the abnormal data, convert the time series data into supervised sequence data, and divide the data into 2500 sets of training sets and 1000 ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention relates to an intelligent control method of a control system of an HVAC (Heating, Ventilation, Air-conditioning and Cooling) control system, in particular to an HVAC control system based on an LSTM (Long Short Term Memory) neural network of a GC (generalized correntropy) loss function and a DRL (deep reinforcement learning) algorithm. The method comprises the following steps: acquiring outdoor environment temperature, indoor environment temperature and electricity price information of a power grid, preprocessing the acquired data, and predicting the outdoor environment temperature of multiple steps in the future by using historical data of the outdoor environment temperature; on the basis of the future outdoor temperature value, the indoor environment temperature and the power grid electricity price information, controlling the power output of the HVAC system by utilizing a DDPG (Deep Deterministic Policy Gradient) algorithm of the DRL. According to the invention, the HVAC system can be intelligently controlled in real time, so that the user cost is reduced, the satisfaction degree of the user is ensured, and the method has high practical engineering application value.

Description

technical field [0001] The invention relates to a method for intelligently optimizing and controlling an HVAC system, in particular to a research method for intelligently controlling an HVAC system based on a GC-LSTM neural network and a DRL algorithm. Background technique [0002] Household users are the end users of the power grid. The user's electricity consumption habits and the addition of distributed renewable energy will directly lead to the emergence of peaks and troughs in the power grid; it has brought serious impacts on the power grid and caused serious threats. With the development of the smart grid and the implementation of the "demand response" strategy in recent years, residential users have changed from being passive to actively joining the grid; Two-way exchange of information. Among household users, the power consumption of the air-conditioning system accounts for about 35% of the total power consumption of the entire user. Therefore, on the premise of sat...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
IPC IPC(8): G06Q10/04G06Q50/06H02J3/00G06N3/04G06N3/08
CPCG06Q10/04G06Q50/06H02J3/00G06N3/08H02J2203/20G06N3/044Y02E40/70
Inventor 任密蜂刘祥飞杨之乐张建华
Owner TAIYUAN UNIV OF TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products