Optimization control technology for building energy conservation
Abstract
A simulation processor generates and stores a simulation model based on conditions associated with a physical structure, such as a building. A neural network processor implements a neural network, having an input layer coupled to receive sensor data from the structure and having an output layer coupled to supply control signals to the at least one electrically operable environmental control device. The neural network is trained using the simulation model. A particle swarm optimization processor programmed to receive the simulation results and perform particle swarm optimization, ascertains optimal parameters for controlling the at least one electrically operable environmental control device and supplies these optimal parameters to the neural network processor. The neural network processor uses the optimal parameters supplied by the particle swarm optimization processor to further train the neural network.
- Inventors:
- Issue Date:
- Research Org.:
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1924998
- Patent Number(s):
- 11416739
- Application Number:
- 15/882,527
- Assignee:
- Lawrence Livermore National Security, LLC (Livermore, CA)
- Patent Classifications (CPCs):
-
G - PHYSICS G05 - CONTROLLING G05B - CONTROL OR REGULATING SYSTEMS IN GENERAL
G - PHYSICS G06 - COMPUTING G06F - ELECTRIC DIGITAL DATA PROCESSING
- DOE Contract Number:
- AC52-07NA27344
- Resource Type:
- Patent
- Resource Relation:
- Patent File Date: 01/29/2018
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING; 32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION
Citation Formats
Qin, Yining. Optimization control technology for building energy conservation. United States: N. p., 2022.
Web.
Qin, Yining. Optimization control technology for building energy conservation. United States.
Qin, Yining. Tue .
"Optimization control technology for building energy conservation". United States. https://www.osti.gov/servlets/purl/1924998.
@article{osti_1924998,
title = {Optimization control technology for building energy conservation},
author = {Qin, Yining},
abstractNote = {A simulation processor generates and stores a simulation model based on conditions associated with a physical structure, such as a building. A neural network processor implements a neural network, having an input layer coupled to receive sensor data from the structure and having an output layer coupled to supply control signals to the at least one electrically operable environmental control device. The neural network is trained using the simulation model. A particle swarm optimization processor programmed to receive the simulation results and perform particle swarm optimization, ascertains optimal parameters for controlling the at least one electrically operable environmental control device and supplies these optimal parameters to the neural network processor. The neural network processor uses the optimal parameters supplied by the particle swarm optimization processor to further train the neural network.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2022},
month = {8}
}
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