energies Article Fifth-Generation District Heating and Cooling Substations: Demand Response with Artificial Neural Network-Based Model Predictive Control Simone Buffa 1,*, Anton Soppelsa 1 , Mauro Pipiciello 1, Gregor Henze 2,3,4 and Roberto Fedrizzi 1 1 Eurac Research, Institute for Renewable Energy, Viale Druso 1, 39100 Bolzano, Italy;
[email protected] (A.S.);
[email protected] (M.P.);
[email protected] (R.F.) 2 Department of Civil, Environmental and Architectural Engineering, University of Colorado Boulder, Boulder, CO 80309-0428, USA;
[email protected] 3 National Renewable Energy Laboratory, Golden, CO 80401, USA 4 Renewable and Sustainable Energy Institute, Boulder, CO 80309, USA * Correspondence: simone.buff
[email protected]; Tel.: +39-0471-055636 Received: 16 July 2020; Accepted: 11 August 2020; Published: 21 August 2020 Abstract: District heating and cooling (DHC) is considered one of the most sustainable technologies to meet the heating and cooling demands of buildings in urban areas. The fifth-generation district heating and cooling (5GDHC) concept, often referred to as ambient loops, is a novel solution emerging in Europe and has become a widely discussed topic in current energy system research. 5GDHC systems operate at a temperature close to the ground and include electrically driven heat pumps and associated thermal energy storage in a building-sited energy transfer station (ETS) to satisfy user comfort. This work presents new strategies for improving the operation of these energy transfer stations by means of a model predictive control (MPC) method based on recurrent artificial neural networks. The results show that, under simple time-of-use utility rates, the advanced controller outperforms a rule-based controller for smart charging of the domestic hot water (DHW) thermal energy storage under specific boundary conditions.