Journal article
Online short-term forecast of greenhouse heat load using a weather forecast service
Aalborg University1
Department of Applied Mathematics and Computer Science, Technical University of Denmark2
Dynamical Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark3
CITIES - Centre for IT-Intelligent Energy Systems, Centers, Technical University of Denmark4
In some district heating systems, greenhouses represent a significant share of the total load, and can lead to operational challenges. Short term load forecast of such consumers has a strong potential to contribute to the improvement of the overall system efficiency. This work investigates the performance of recursive least squares for predicting the heat load of individual greenhouses in an online manner.
Predictor inputs (weekly curves terms and weather forecast inputs) are selected in an automated manner using a forward selection approach. Historical load measurements from 5 Danish greenhouses with different operational characteristics were used, together with weather measurements and a weather forecast service.
It was found that these predictors of reduced complexity and computational load performed well at capturing recurring load profiles, but not fast frequency random changes. Overall, the root mean square error of the prediction was within 8–20% of the peak load for the set of consumers over the 8 months period considered.
Language: | English |
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Year: | 2017 |
Pages: | 1298-1310 |
ISSN: | 18729118 and 03062619 |
Types: | Journal article |
DOI: | 10.1016/j.apenergy.2017.08.013 |
ORCIDs: | Bacher, P. and Madsen, Henrik |