Abstract
Accurately predicting values for dynamic data streams is a challenging task in decision and expert systems, due to high data flow rates, limited storage and a requirement to quickly adapt a model to new data. We propose an approach for correcting predictions for data streams which is based on a reliability estimate for individual regression predictions. In our work, we implement the proposed technique and test it on a real-world problem: prediction of the electricity load for a selected European geographical region. For predicting the electricity load values we implement two regression models: the neural network and the k nearest neighbors algorithm. The results show that our method performs better than the referential method (i.e. the Kalman filter), significantly improving the original streaming predictions to more accurate values.
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Bosnić, Z., Rodrigues, P.P., Kononenko, I., Gama, J. (2011). Correcting Streaming Predictions of an Electricity Load Forecast System Using a Prediction Reliability Estimate. In: Czachórski, T., Kozielski, S., Stańczyk, U. (eds) Man-Machine Interactions 2. Advances in Intelligent and Soft Computing, vol 103. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23169-8_37
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DOI: https://doi.org/10.1007/978-3-642-23169-8_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23168-1
Online ISBN: 978-3-642-23169-8
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