Abstract
This paper presents the improved method for 24 h ahead load forecasting applied to individual household data from a smart metering system. In this approach we decompose a set of individual forecasts into basis latent components with destructive or constructive impact on the prediction. The main research problem in such model aggregation is the proper identification of destructive components that can be treated as some noise factors. To assess the randomness of signals and thus their similarity to the noise, we used a new variability measure that helps to compare decomposed signals with some typical noise models. The experiments performed on individual household electricity consumption data with blind separation algorithms contributed to forecasts improvements.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Anda, M., Temmen, J.: Smart metering for residential energy efficiency: the use of community based social marketing for behavioural change and smart grid introduction. Renew. Energy 67, 119–127 (2014)
Carroll, J., Lyons, S., Denny, E.: Reducing household electricity demand through smart metering: the role of improved information about energy saving. Energy Econ. 45, 234–243 (2014)
Box, G.E.P., Jenkins, G.M.: Time Series Analysis. Forecasting and Control. Prentice-Hall, Englewood Cliffs (1994)
Hamilton, J.D.: Time Series Analysis. Princeton University Press, Princeton (1994)
Szupiluk, R., Za̧bkowski, T.: In: Rutkowski, et al. (eds.) Signal Randomness Measure for BSS Ensemble Predictors. Lecture Notes in Computer Science, vol. 8468, pp. 570–578. Springer, Berlin (2014)
Cichocki, A., Amari, S.: Adaptive Blind Signal and Image Processing. Wiley, Chichester (2002)
Comon, P., Jutten, C.: Handbook of Blind Source Separation: Independent Component Analysis and Applications. Academic Press, Boston (2010)
Hyvarinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. Wiley, New York (2001)
Szupiluk, R., Cichocki, A.: Blind signal separation using second order statistics. Proc. SPETO 2001, 485–488 (2001)
Dataport. https://dataport.pecanstreet.org/ (2014)
Gajowniczek, K., Za̧bkowski, T.: Short term electricity forecasting using individual smart meter data. Procedia Comput. Sci. 35, 589–597 (2014)
Ward, J.H.: Hierarchical grouping to optimize an objective function. J. Am. Stat. Assoc. 58(301), 236–244 (1963)
National Research Council: Estimating Population and Income for Small Places. National Academy Press, Washington (1980)
Moreno, J.J.M., Pol, A.P., Abad, A.S., Blasco, B.C.: Using the R-MAPE index as a resistant measure of forecast accuracy. Psicothema 25(4), 500–506 (2013)
Huber, P.J.: Robust Statistics. Wiley, New York (1981)
R Development Core Team R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, http://www.R-project.org/ (2011)
Kuhn, M.: Building predictive models in r using the caret package. J. Stat. Softw. 28(5), 1–26 (2008)
Dai, Y.D.: Convergence properties of the BFGS algorithm. SIAM J. Opt. 13(3), 693–701 (2002)
Acknowledgments
The study is cofounded by the European Union from resources of the European Social Fund. Project PO KL âInformation technologies: Research and their interdisciplinary applicationsâ, Agreement UDA-POKL.04.01.01-00-051/10-00.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Gajowniczek, K., Za̧bkowski, T., Szupiluk, R. (2016). Blind Source Separation for Improved Load Forecasting on Individual Household Level. In: Burduk, R., Jackowski, K., Kurzyński, M., Woźniak, M., Żołnierek, A. (eds) Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015. Advances in Intelligent Systems and Computing, vol 403. Springer, Cham. https://doi.org/10.1007/978-3-319-26227-7_17
Download citation
DOI: https://doi.org/10.1007/978-3-319-26227-7_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-26225-3
Online ISBN: 978-3-319-26227-7
eBook Packages: EngineeringEngineering (R0)