Abstract
The aim of this study is to develop a model that can accurately calculate building loads and demand for predictive control. Thus, the building energy model needs to be combined with weather prediction models operated by a model predictive controller to forecast indoor temperatures for specified rates of supplied energy. In this study, a resistance–capacitance (RC) building model is proposed where the parameters of the models are determined by learning. Particle swarm optimization is used as a learning scheme to search for the optimal parameters. Weather prediction models are proposed that use a limited amount of forecasting information fed by local meteorological centers. Assuming that weather forecasting was perfect, hourly outdoor temperatures were accurately predicted; meanwhile, differences were observed in the predicted solar irradiances values. In investigations to verify the proposed method, a seven-resistance, five-capacitance (7R5C) model was tested against a reference model in EnergyPlus using the predicted weather data. The root-mean-square errors of the 7R5C model in the prediction of indoor temperatures on all the specified days were within 0.5 °C when learning was performed using reference data obtained from the previous five days and weather prediction was included. This level of deviation in predictive control is acceptable considering the magnitudes of the loads and demand of the tested building.
Highlights
Heating, ventilation and air conditioning (HVAC) accounts for approximately 60% of the total energy consumption in buildings
A building energy model is required that can accurately calculate them for the target prediction days
Weather prediction is required, and the building model should be simple for controller implementation
Summary
Ventilation and air conditioning (HVAC) accounts for approximately 60% of the total energy consumption in buildings. An optimal control of HVAC systems can reduce energy consumption and achieve higher energy efficiency [1]. When the thermal mass of structural elements, such as walls, foundations, and floors in buildings, is used to store energy to regulate future heat flow [2], systems can be operated with high efficiency [3]. As thermal mass has a high time constant, adequate predictive control is required. Under MPC, optimal HVAC operating schedules are planned to reduce HVAC energy consumption and guarantee the thermal comfort of the occupants. The prediction of building energy loads and demand in the near future is important [7,8]
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