The k-nearest neighbour (k-NN) algorithm is one of the most widely used benchmark algorithms in classification, supported by its simplicity and intuitiveness in finding similar instances in multivariate and large-dimensional feature spaces of arbitrary attribute scales. In contrast, only few scientific studies of k-NN exist in forecasting time series data, which have mainly assessed various distance metrics to identify similar univariate time series motifs in past data. In electricity load forecasting, k-NN studies are limited to identifying past motifs of the same dependent variable to match future realisations, in a non-causal approach to forecasting. However, causal information in the form of deterministic calendar information is readily available on past and future time series motifs, allowing the distinction between load profiles of working days, weekends and bank-holidays to be encoded as binary dummy variables, and to be efficiently included in the search for similar neighbours. In this paper, we propose a multivariate k-NN regression method for forecasting the electricity demand in the UK market which utilises binary dummy variables as a second feature to categorise the day being forecasted as a working day or a non-working day. We assess the efficacy of this approach in a robust empirical evaluation using UK electricity load data. The approach shows improvements beyond conventional k-NN approaches and accuracy beyond that of simple statistical benchmark methods.