Batty, W. J.Myddelton, D.Brierley, Philip David2023-01-052023-01-051998-09https://dspace.lib.cranfield.ac.uk/handle/1826/18868The developm ent of an optimising model predictive controller for dom estic storage radiators w as the ultimate goal of this research project. Neural networks are used to create empirical m odels that are used to predict the likely temperature response of a room to the charging of a storage radiator. The charging strategy can then be optimised based on the real-time price of electricity. Neural network modelling is investigated by looking at the load forecasting problem. It is shown how accurate neural m odels can be created and demonstrated exactly how they process the data. Very specific rules are extracted from the neural network that can model the load to a reasonable accuracy. An efficient optimisation technique is sought by optimising the charging of a dom estic hot water tank based on actual consumption data and the pool price of electricity. Initially genetic algorithms were tried but their w ea k n esses are demonstrated. A stochastic hill climbing method w as found to be more suitable. Monetary saving of 40% over the existing E7 tariff w as common. The modelling and optimisation are brought together in a storage radiator simulation. There are improvements in cost and electricity consumption over E7 primarily due to the ability to look ahead and avoid overheating. A prototype neural controller is developed and tested in a real house. The results are very encouraging.enThesis