Dwindling fossil fuels and the rising price of energy has meant that attitudes towards energy usage have changed in both domestic and commercial settings. This change in attitude has led to the development of smart metering technologies that are currently being rolled out across the world. The research has been developed to be able to add functionality to smart metering devices by providing information about energy usage within the premises through Non-Intrusive Load Monitoring (NILM). The thesis provides a detailed description of the work undertaken to develop a novel method of load disaggregation within NILM to aid in the monitoring of energy usage and the provision of consumer feedback which can be integrated into smart metering technologies. The research aims to provide a novel approach to NILM through the use of canopy clustering for its main process of load disaggregation. Canopy clustering provides the necessary tools for separating out appliances and groups of appliances for later classification into individual loads, which brings many benefits compared to other technologies. The research methodology has been developed with robust techniques of data gathering, model development and validation through a rigorous testing approach. Real world examples of loads have been used for the creation and development of the models. The use of contemporary appliances within the research has meant that the NILM algorithm developed is current and usable. In the final implementation it could be commercialised for use by the general public. The full procedures of the algorithm have been explained in detail with the addition of information on the final classification methods that could be used when implemented within smart metering devices. Further work and improvements to the research have also been included for consideration.