Model and data driven heating ventilation air conditioner optimal set temperature obtaining method and device
A technology for setting temperature and HVAC, applied in model and data-driven HVAC optimal setting temperature acquisition, HVAC temperature setting control field, can solve the hidden costs that do not consider the impact of buildings on work efficiency , Misjudgment of users' willingness to participate in demand response, failure to analyze response capabilities and other issues, to achieve the effect of solving unbalanced distribution, ensuring accuracy, and precisely designing temperature
Pending Publication Date: 2021-03-26
SHANGHAI MUNICIPAL ELECTRIC POWER CO +1
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Problems solved by technology
However, the existing technology focuses on analyzing the energy saving potential of HVAC, and cannot be used to analyze its responsiveness
For the evaluation of demand response potential, the main difficulty of the data-driven mechanism is that there are many external environments and internal heat load scenarios that affect the responsiveness of buildings, and it is difficult to obtain sufficient historical samples in practice
[0004] In addition, the existing literature generally only considers the electricity cost of the building, and does not consider the hidden cost that the building’s participation in demand response will affect work efficiency. Conducive to formulating a reasonable demand response mechanism
Method used
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Embodiment
[0126] 1) Simulation model
[0127] Taking the architectural structure of a smart grid building as a prototype, a three-story office building simulation model is established. Each floor of the building contains 6 offices with a length, width and height of 9m*6m*5m, windows with a length and width of 1m*1.5m, and a corridor with a width of 2m in the middle. The heat load and HVAC parameters in the building are set with reference to the EnergyPlus standard file, and the HVAC temperature adjustment resolution is 1°C. Using the meteorological data of June 13 in a typical year in a certain city, the research period Γ is from 9:00 to 17:00.
[0128] 2) ANN model verification
[0129] In this embodiment, a computer with a CPU of i7-6700HQ is used to obtain 1184 groups of samples in section 1.2 after 2.3 hours of simulation, and then the training is carried out according to the ratio of 70% training set, 15% verification set and 15% test set.
[0130] In order to evaluate the fitti...
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The invention relates to a model and data driven heating ventilation air conditioner optimal set temperature obtaining method and device, and the method comprises the following steps: constructing a building HVAC dynamic characteristic model through combining with a building physical simulation model and ANN-based data driving; and taking the building HVAC dynamic characteristic model as a constraint, constructing a set temperature optimization model considering energy consumption and personnel comfort, and solving by adopting a swarm intelligence algorithm to obtain the optimal set temperature of the heating ventilation air conditioner. Compared with the prior art, the method has the advantages of high accuracy, convenience, reliability and the like.
Description
technical field [0001] The invention belongs to the technical field of power system demand response and intelligent power consumption, and relates to a temperature setting control method for HVAC, in particular to a model and data-driven HVAC optimal setting temperature acquisition method and equipment. Background technique [0002] In the use of the existing power grid, the contradiction between the seasonal peak load and the time-period peak load of the power grid has become increasingly prominent. Among them, the contribution of air-conditioning load to the peak load is increasing, accounting for 30% to 50% of the peak load in summer. Modern buildings have good heat storage capacity, and can advance or postpone electricity consumption without affecting user comfort. Therefore, they can be used as important demand-side resources to provide services such as peak shaving and valley filling to the power grid. [0003] For the coordinated control of building air-conditioning ...
Claims
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IPC IPC(8): G06F30/13G06F30/27G06K9/62G06N3/00G06N3/04G06F111/04G06F113/04G06F119/08
CPCG06F30/13G06F30/27G06N3/006G06F2119/08G06F2111/04G06F2113/04G06N3/045G06F18/214
Inventor 赵建立赵本源张沛超郑庆荣盛明高志刚郭雁陆颖杰
Owner SHANGHAI MUNICIPAL ELECTRIC POWER CO