Abstract
Demand side management (DSM) programs are an integral part of the modern grid. Most
of these DSM programs are designed to work at household and hour level. The optimization problems in
these DSM programs are guided by the forecasted load. An error in the hour ahead load forecasting results
in a suboptimal solution entailing economic cost to both the utility and the customers. Predicting loads at
a fine granularity (e.g., households) is challenging due to a large number of (known or unknown) factors
affecting power consumption. At larger scales (e.g., clusters of consumers), since the inherent stochasticity
and fluctuations are averaged out, the problem becomes substantially easier. Many techniques have been
proposed to predict loads of clusters of consumers in various localities with great accuracy. Also, these
techniques generally utilize sociological and weather information and work better on data from a particular
locality. In this paper, a new technique called Past Vector Similarity (PVS) has been proposed to predict
electricity load one hour ahead at the level of an individual household. The proposed strategy is based on
load information only and does not require calendar, weather or any other attributes. In fact, the idea is to
extract the exact load patterns of individual households corresponding to their routine and socio-economic
values. Consequently, the technique makes use of the recent past vector and generate similar patterns for the
prediction of future load profiles. Furthermore, the ensemble of these similar loads is an efficient prediction
of electricity. PVS has just two parameters due to which it can be applied to the smaller dataset without
overfitting issue. Moreover, due to the parallel nature of PVS, it can be scaled for a large number of customers
without computation burden. The proposed PVS has been assessed empirically for two distinct datasets from
Australia and Sweden. The simulation results demonstrate that the PVS significantly outperforms other state-
of-the-art forecasting methods in terms of accuracy.