A latent variable approach to heat load prediction in thermal grids
| Autor: | Wolfgang Birk, Johan Simonsson, Dave Zachariah, Khalid Tourkey Atta |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Signal Processing (eess.SP)
FOS: Computer and information sciences Computer Science - Machine Learning business.industry Machine Learning (stat.ML) Latent variable Space (mathematics) Energy engineering Machine Learning (cs.LG) Outdoor temperature Statistics - Machine Learning Thermal FOS: Electrical engineering electronic engineering information engineering Environmental science Electrical Engineering and Systems Science - Signal Processing Heat load Aerospace engineering business Energy (signal processing) |
| Zdroj: | ECC |
| DOI: | 10.23919/ecc51009.2020.9143860 |
| Popis: | In this paper a new method for heat load prediction in district energy systems is proposed. The method uses a nominal model for the prediction of the outdoor temperature dependent space heating load, and a data driven latent variable model to predict the time dependent residual heat load. The residual heat load arises mainly from time dependent operation of space heating and ventilation, and domestic hot water production. The resulting model is recursively updated on the basis of a hyper-parameter free implementation that results in a parsimonious model allowing for high computational performance. The approach is applied to a single multi-dwelling building in Lulea, Sweden, predicting the heat load using a relatively small number of model parameters and easily obtained measurements. The results are compared with predictions using an artificial neural network, showing that the proposed method achieves better prediction accuracy for the validation case. Additionally, the proposed methods exhibits explainable behavior through the use of an interpretable physical model. Comment: Paper submitted to 2020 European Control Conference, Saint Petersburg, Russia |
| Databáze: | OpenAIRE |
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