Development of a statistical model for household electrical appliances

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonMany studies have conducted in the past that related to the domestic energy sector and households’ appliances. These previous studies have explained the energy trends in the United Kingdom. In addition to this, the past studies have also provided wealth of information. However, all of these studies had some limitations. In addition, there were many gaps in the past studies regarding to the timing of usage the household’s appliances and their daily contribution to the daily and peak demand. In this study, the researcher intended to overcome the limitations and gaps regarding the appliances time of use in the UK. In the present study, the data collected from Hillingdon Borough of London to ensure the study use the most reliable and valid data. Most importantly, suitable sampling and data collection technique applied in this study, which helped to obtain the appropriate data and outcome. All respondents were from the domestic sectors of the United Kingdom. Apart from this, to measure energy consumption in a more accurate manner, home appliances were categorised into several categories based on their functionality. Moreover, the household’s appliances were categorised into time categories based on the time of use the appliances in order to determine the contribution of individual appliances at a certain time slot of the day to the total household consumption. Finally, the recommendations that have suggested in this study based on the current study as well as past studies. This means that the recommendations are a combination of all the major studies conducted. Additionally, based on the time a category of the household’s appliances, a model was introduced that helped to determine how much of electrical appliances energy consuming in the UK households. Based on this model, the appliances consumption can managed and controlled. Thus, the model will help in mitigating the chances of the energy peak demand and will contribute towards energy and cost savings. Further, this study provides a valuable contribution to the field of smart homes as through the developed model, people can design a more efficient smart home. This specific method of determining energy demand has made the study more appropriate to forecast the 24h electricity demand and electricity price