Identifying residential daily electricity-use profiles through time-segmented regression analysis

Abstract

In many countries the residential sector contributes significantly to peak demand. Some of the promising approaches to reduce these peaks, such as energy efficiency and demand-side management (DSM) programmes, currently lack the sophistication to target households with particular characteristics or that make the most contribution to peaks. We present an analytical approach ‘time-segmented regression analysis (TSRA)’ that is able to determine the household factors that dominate at different time-slots across the day and therefore, categorize houses by their daily usage profiles and identify houses with high demand during network peaks based on common household characteristics. The method is applied to an example dataset from New Zealand. From a range of possible factors, it identifies the presence or absence of electrical hot water and electric heating appliances as the dominant factors determining daily electricity variation. The initial findings suggest that DSM programmes in New Zealand should directly target households with these appliances; however, a nationally representative dataset is required to confirm these findings. The analytical approach could be applied to other countries, and used to design more effective, targeted energy efficiency and DSM strategies.