Analysis of customer profiles on an electrical distribution network.

It has become increasingly important for electrical distribution companies to understand the drivers of demand. The maximum demand at any given substation can vary materially on an annual basis which means it is difficult to create a load related investment plan that is robust and stable. Currently, forecasts are based only on historical demand with little understanding about contributions to load profiles. In particular, the unique diversity of customers on any particular substation can affect load profile shape and future forecasts. Domestic and commercial customers can have very different behaviours generally and within these groups there is room for variation due to economic conditions and building types. This paper analyses customer types associated to substations on a distribution network by way of principal component analysis and identification of substations which deviate from the national demand trend. By examining the variance spread of this deviation, data points can be labelled in the principal component space. Groups of substations can then be categorised as having typical or atypical load profiles. This will support the need for further investigation into particular customer types and highlight the key factors of customer categorisation

Analysis of clustering techniques on load profiles for electrical distribution

The classification of electrical load profiles has become increasingly important as a driver for distribution companies in understanding substation data. The daily load profile can often give great insight into the types of customers connected to the substation and can assist with developing a long-term forecast. The literature in this area often uses data mining and clustering techniques to determine a load diagram representative for a subset of customers or substations. The type of technique used can often lead to representative load diagrams of unique shapes with differing numbers of customers belonging to each group. This paper analyses clustering techniques on representative load diagrams for primary substations at the distribution level. In particular, this paper will analyse clustering techniques in terms of their performance and effect on load profile groupings. The results show that K-means clustering showed the best performance in generating unique, well-populated cluster groups. This gives a greater understanding of the divisions between substations which can be used for future forecasting

Analysis of customer profiles on an electrical distribution network

It has become increasingly important for electrical distribution companies to understand the drivers of demand. The maximum demand at any given substation can vary materially on an annual basis which means it is difficult to create a load related investment plan that is robust and stable. Currently, forecasts are based only on historical demand with little understanding about contributions to load profiles. In particular, the unique diversity of customers on any particular substation can affect load profile shape and future forecasts. Domestic and commercial customers can have very different behaviours generally and within these groups there is room for variation due to economic conditions and building types. This paper analyses customer types associated to substations on a distribution network by way of principal component analysis and identification of substations which deviate from the national demand trend. By examining the variance spread of this deviation, data points can be labelled in the principal component space. Groups of substations can then be categorised as having typical or atypical load profiles. This will support the need for further investigation into particular customer types and highlight the key factors of customer categorisation