TY - GEN

T1 - Multivariate k-nearest neighbour regression for time series data - A novel algorithm for forecasting UK electricity demand

AU - Al-Qahtani, Fahad H.

AU - Crone, Sven F.

N1 - Copyright: Copyright 2014 Elsevier B.V., All rights reserved.

PY - 2013/8/9

Y1 - 2013/8/9

N2 - 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.

AB - 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.

U2 - 10.1109/IJCNN.2013.6706742

DO - 10.1109/IJCNN.2013.6706742

M3 - Conference contribution/Paper

AN - SCOPUS:84893586967

T3 - Proceedings of the International Joint Conference on Neural Networks

BT - 2013 International Joint Conference on Neural Networks, IJCNN 2013

PB - IEEE

T2 - 2013 International Joint Conference on Neural Networks, IJCNN 2013

Y2 - 4 August 2013 through 9 August 2013

ER -