Data-Driven Predictive Control, representing thebuilding as a cyber-physical system, shows promisingpotential in harnessing energy flexibility f o r demandside management, where the efforts in developing aphysics-based model can be significant. Here, predictivecontrol using random forests is applied in a casestudy closed-loop simulation of a large office buildingwith multiple energy flexibility s o urces, therebytesting the suitability of the technique for such buildings.Further, consideration is given to the featureselection and feature engineering process. The resultsshow that the data-driven predictive control, under adynamic grid signal, is capable of minimising energyconsumption or energy cost.
| File Size : | 1
file
, 9.9 MB |
| Note : | This product is unavailable in Russia, Belarus |
| Number of Pages : | 10 |
| Product Code(s) : | D-BSC20-C002 |
| Published : | 2020 |
| Units of Measure : | Dual |
