Online short-term forecast of greenhouse heat load using a weather forecast service

Research output: Contribution to journalJournal article – Annual report year: 2017Researchpeer-review

View graph of relations

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.

Original languageEnglish
JournalApplied Energy
Pages (from-to)1298-1310
Publication statusPublished - 1 Nov 2017
CitationsWeb of Science® Times Cited: No match on DOI

    Research areas

  • Greenhouses, Heat demand, Load forecast, Model selection, Recursive least squares, Weather forecast service
Download as:
Download as PDF
Select render style:
Download as HTML
Select render style:
Download as Word
Select render style:

ID: 137904797