Implementation of a machine learning supported model predictive control for a 5th generation district heating and cooling energy hub

Energy and AI, vol. 24, pp. 100699

Abstract

Fifth-generation district heating and cooling (5GDHC) is a promising technology for supplying low-emission energy to buildings. However, realizing its full potential requires the central energy supply unit to operate optimally. Model predictive control (MPC) is a proven approach for this task. Although machine learning (ML)-based load prediction and MPC-based operational optimization are well-studied fields, their joint application to 5GDHC networks remains rare. Accordingly, this paper develops an MPC framework that integrates ML-based thermal load predictions with a mixed-integer linear program (MILP) to optimize the operation of a 5GDHC energy hub (EH). To ensure comparability, we use coefficient of variation of the root mean squared error (CV-RMSE) as an error metric for the evaluated ML models. XGBoost delivered the most accurate multi-step predictions 24 h ahead, with a 27.78 % error in predicting residual building loads. We also assessed a deep neural network (DNN) using both recursive and multiple-inputs-multiple-outputs (MIMO) forecasting strategies. The MILP incorporates an air-to-water heat pump (HP), a free-cooling unit (FCU), geothermal borehole field (BHF) and thermal energy storage (TES). In simulation, the MPC framework outperformed a rule-based control (RBC) baseline, achieving annual cost and carbon dioxide (CO 2 ) reductions 27.8 % and 17.0 %, respectively. Under perfect load forecast conditions, the MPC gained an additional 3.9 %-points in operational cost savings and 3.7 %-points in CO 2 reduction. Overall, combining ML-based load forecasts with MPC can improve the efficiency and sustainability of 5GDHC operation.

Authors 5

1. Kai Droste corresponding Aachen Institute for Energy Efficient Buildings and Indoor Climate

   RWTH Aachen University

   Affiliation as printed

   Institute for Energy Efficient Buildings and Indoor Climate, Mathieustraße 10, 52074 Aachen, Germany

2. Aron Schwartz Aachen Institute for Energy Efficient Buildings and Indoor Climate

   RWTH Aachen University

   Affiliation as printed

   Institute for Energy Efficient Buildings and Indoor Climate, Mathieustraße 10, 52074 Aachen, Germany

3. K Derzsi Aachen Institute for Energy Efficient Buildings and Indoor Climate

   RWTH Aachen University

   Affiliation as printed

   Institute for Energy Efficient Buildings and Indoor Climate, Mathieustraße 10, 52074 Aachen, Germany

4. Rahul Karuvingal Aachen Institute for Energy Efficient Buildings and Indoor Climate

   RWTH Aachen University

   Affiliation as printed

   Institute for Energy Efficient Buildings and Indoor Climate, Mathieustraße 10, 52074 Aachen, Germany

5. Dirk Müller Aachen Institute for Energy Efficient Buildings and Indoor Climate

   RWTH Aachen University

   Affiliation as printed

   Institute for Energy Efficient Buildings and Indoor Climate, Mathieustraße 10, 52074 Aachen, Germany