Abstract
An improved optimization adjustment strategy for building heating ventilation and air conditioning (Heating Ventilation and Air Conditioning, HVAC) is proposed. The energy consumption model of building heating/refrigeration is established by using the instantaneous energy balance of heat, and then the optimal operation strategy of building HVAC energy based on weather forecast data is constructed in the range of user temperature comfort. Finally, the MATLAB and TRNSYS simulation techniques are used to verify the example. Simulation results show that the optimal operation strategy of building HVAC energy based on weather forecast data can not only significantly reduce the cost of energy use, but also effectively improve the absorption capacity of renewable energy on the building side.
Highlights
With the rapid pace of urbanization in the world, the consumption of cities in energy consumption is soaring, and cities consume 75% of the world's energy
The energy consumption model of building heating/refrigeration is established by using the instantaneous energy balance of heat, and the dual-source switching strategy and the load migration strategy based on demand response are constructed in the range of user temperature comfort
In winter heating, air switching strategy proposed in this paper, a typical winter conditioning system selection of appropriate heating day temperature in Beijing is used for example analysis. channels is very important for intelligent buildings to
Summary
With the rapid pace of urbanization in the world, the consumption of cities in energy consumption is soaring, and cities consume 75% of the world's energy. In the aspect of modeling, the existing research can construct the virtual energy storage system model based on the building heat storage characteristics, and realize the charge and discharge management of the building virtual energy storage system[3-4].The dynamic model of heating / refrigeration energy consumption in intelligent buildings can be constructed by using the heat transient energy balance equation and the energy management strategy of intelligent buildings based on model predictive control can be constructed[5-6].In the aspect of control, the existing research solves the problem of the deviation between prediction and real-time control by proposing a flexible control strategy of intelligent building energy use based on model predictive control[1].The optimal control model can be established based on the energy cost and user comfort, and the improved fast particle swarm optimization method is proposed[7]. The energy consumption model of building heating/refrigeration is established by using the instantaneous energy balance of heat, and the dual-source switching strategy and the load migration strategy based on demand response are constructed in the range of user temperature comfort. The proposed strategy is verified by MATLAB and TRNSYS simulation technology
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