Abstract
Load forecasting plays an important role in modern utilities. However, further improvements can be expected by predicting the load at a consumer level. The latter approach has become available with the advent of low-cost monitoring and transmission systems. Still, due to the limited number of monitored clients, the way groups of consumers should be identified and whether their data is sufficient for high quality prediction models remains an open issue.
The work summarises the results of building prediction models for different consumer groups of a district heating system. The way self-organising maps, multilayer perceptrons and simple prediction strategies can be applied to identify groups of consumers and build their prediction models has been proposed. The hypothesis that a billing database enables group identification has been verified. Significant improvements in prediction accuracy have been observed.
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Grzenda, M., Macukow, B. (2009). Heat Consumption Prediction with Multiple Hybrid Models. In: Omatu, S., et al. Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living. IWANN 2009. Lecture Notes in Computer Science, vol 5518. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02481-8_182
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DOI: https://doi.org/10.1007/978-3-642-02481-8_182
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