An operational strategy for district heating networks: application of data-driven heat load forecasts

RT @TeamEnergy_KIT: In our study on city district #heating networks, we evaluate a control strategy for a DHN with heat pump and heat stora…

RT @TeamEnergy_KIT: In our study on city district #heating networks, we evaluate a control strategy for a DHN with heat pump and heat stora…

In our study on city district #heating networks, we evaluate a control strategy for a DHN with heat pump and heat storage that utilizes CNN forecast results in an online optimization and can be applied using varying objective functions. @SpringerEnergy h

RT @TeamEnergy_KIT: In our study on city district #heating networks, we apply, evaluate and compare supervised data analysis techniques to…

In our study on city district #heating networks, we apply, evaluate and compare supervised data analysis techniques to forecast hourly heat demand with a 24-hours rolling horizon on three datasets. CNN and FFN show the overall best results.@SpringerEnergy

RT @TeamEnergy_KIT: The heat sector represents more than 50% of the German energy consumption, yet the share of renewables is significantly…

RT @TeamEnergy_KIT: The heat sector represents more than 50% of the German energy consumption, yet the share of renewables is significantly…

RT @TeamEnergy_KIT: The heat sector represents more than 50% of the German energy consumption, yet the share of renewables is significantly…

RT @TeamEnergy_KIT: The heat sector represents more than 50% of the German energy consumption, yet the share of renewables is significantly…