The demonstration project has implemented, tested and demonstrated a fluidborne heating system with a heat pump and a solar collector in a domestic building that makes optimal use of renewable energy from the grid.
This implied a test of model predictive control technologies that were able to benefit from probabilistic forecasts of heat load and energy prices. The controllers should be able to benefit from energy forecast services.
The final model structure has been established using statistical greybox methods, and the parameters of the embedded model have been found using estimation techniques. The model has been formulated as a stochastic statespace model in continuous time, e.g. dynamics have been described as a set of stochastic differential equations.
Cookie | Duration | Description |
---|---|---|
cookielawinfo-checbox-analytics | 11 months | This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Analytics". |
cookielawinfo-checbox-functional | 11 months | The cookie is set by GDPR cookie consent to record the user consent for the cookies in the category "Functional". |
cookielawinfo-checbox-others | 11 months | This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Other. |
cookielawinfo-checkbox-necessary | 11 months | This cookie is set by GDPR Cookie Consent plugin. The cookies is used to store the user consent for the cookies in the category "Necessary". |
cookielawinfo-checkbox-performance | 11 months | This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Performance". |
viewed_cookie_policy | 11 months | The cookie is set by the GDPR Cookie Consent plugin and is used to store whether or not user has consented to the use of cookies. It does not store any personal data. |