Abstract
A load estimation algorithm based on k-means cluster analysis was developed. The algorithm applies cluster centres – of previously clustered load profiles – and distance functions to estimate missing and future measurements. Canberra, Manhattan, Euclidean, and Pearson correlation distances were investigated. Several case studies were implemented using daily and segmented load profiles of aggregated smart meters. Segmented profiles cover a time window that is less than or equal to 24h. Simulation results show that Canberra distance outperforms the other distance functions. Results also show that the segmented cluster centres produce more accurate load estimates than daily cluster centres. Higher accuracy estimates were obtained with cluster centres in the range of 16–24h. The developed load estimation algorithm can be integrated with state estimation or other network operational tools to enable better monitoring and control of distribution networks.
Highlights
The installation of smart meters is usually considered as the starting point in the implementation of Smart Grids [1]
The solid red profile is the mean of all estimated profiles that were produced by the load estimation algorithm
A load estimation algorithm based on k-means cluster analysis method was developed
Summary
The installation of smart meters is usually considered as the starting point in the implementation of Smart Grids [1]. Examples of the operational challenges include planned or unplanned maintenance of the system, software and hardware faults or malfunction of the smart meters, and customers unwilling to communicate their energy consumption data. These challenges make smart meter measurements susceptible to time delays or even temporary loss when requested by the energy suppliers or network operators [9,10,11]. The k-means method [24,25] was applied to group similar load profiles and produce a number of cluster centres These centres were used to estimate the smart meter measurements using different distance functions
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