Abstract
This paper focuses on parametrization of one-day time series of electricity consumption. In order to parametrize such time series data mining technique was elaborated. The technique is based on the multivariate linear regression and is self-configurable, in other words a user does not need to set any model parameters upfront. The model finds the most essential data points whose values allow to model the electricity consumptions for remaining hours in the same day. The number of data points required to describe the whole time series depends on the demanded precision which is up to the user. We showed that the model with only four describing variables, describes 20 remaining hours very well, exhibiting dominant relative error about 1.5%. It is characterized by a high precision and allows finding non-typical days from the electricity demand point of view.
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Karpio, K., Łukasiewicz, P., Nafkha, R., Orłowski, A. (2021). Description of Electricity Consumption by Using Leading Hours Intra-day Model. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science(), vol 12745. Springer, Cham. https://doi.org/10.1007/978-3-030-77970-2_30
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