Some practical applications of neural networks in the electricity industry
| dc.contributor.advisor | Batty, W. J. | |
| dc.contributor.advisor | Myddelton, D. | |
| dc.contributor.author | Brierley, Philip David | |
| dc.date.accessioned | 2023-01-05T14:25:12Z | |
| dc.date.available | 2023-01-05T14:25:12Z | |
| dc.date.issued | 1998-09 | |
| dc.description.abstract | The 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. | en_UK |
| dc.description.coursename | EngD | en_UK |
| dc.identifier.uri | https://dspace.lib.cranfield.ac.uk/handle/1826/18868 | |
| dc.language.iso | en | en_UK |
| dc.title | Some practical applications of neural networks in the electricity industry | en_UK |
| dc.type | Thesis | en_UK |
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