Adaptive fuzzy logic control for solar buildings
Significant progress has been made on maximising passive solar heating loads through the careful selection of glazing, orientation and internal mass within building spaces. Control of space heating in buildings of this type has become a complex problem. Additionally, and in common with most building control applications, there is a need to develop control solutions that permit simple and transparent set up and commissioning procedures. This work concerns the development and testing of an adaptive control method for space heating in buildings with significant solar input. A simulation model of a building space to assess the performance of different control strategies is developed. A lumped parameter model based on an optimisation technique has been proposed and validated. It is shown that this model gives an improvement over existing low order modelling methods. A detailed model of a hot water heating system and related control devices is developed and evaluated for the specific purpose of control simulation. A PI-based fuzzy logic controller is developed in which the error and change of error between the internal air temperature and the user set point temperature is used as the controller input. A conventional PD controller is also considered for comparison. The parameters of the controllers are set to values that result in the best performance under likely disturbances and changes in setpoint. In a further development of the fuzzy logic controller, the Predicted Mean Vote (PMV) is used to control the indoor temperature of a space by setting it at a point where the PMV index becomes zero and the predicted percentage of persons dissatisfied (PPD) achieves a maximum threshold of 5%. The controller then adjusts the air temperature set point in order to satisfy the required comfort level given the prevailing values of other comfort variables contributing to the comfort sensation. The resulting controller is free of the set up and tuning problems that hinder conventional HVAC controllers. The need to develop an adaptive capability in the fuzzy logic controller to account for lagging influence of solar heat gain is established and a new adaptive controller has therefore been proposed. The development of a "quasi-adaptive" fuzzy logic controller is developed in two steps. A feedforward neural network is used to predict the internal air temperature, in which a singular value decomposition (SVD) algorithm is used to remove the highly correlated data from the inputs of the neural network to reduce the network structure. The fuzzy controller is then modified to have two inputs: the first input being the error between the setpoint temperature and the internal air temperature and the second the predicted future internal air temperature. When compared with a conventional method of control the proposed controller is shown to give good tracking of the setpoint temperature, reduced energy consumption and improved thermal comfort for the occupants by reducing solar overheating. The proposed controller is tested in real time using a test cell equipped with an oil- filled electric radiator, temperature and solar sensors. Experimental results confirm earlier findings arrived at by simulations, in that the proposed controller achieves superior tracking and reduces afternoon solar overheating, when compared with a conventional method of control.