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. By exploiting the available thermal energy storage capacity, the MPC controller is capable of shifting up to 14% of the electricity consumption of the ETS from on-peak to off-peak hours. Therefore, the advanced control implemented in 5GDHC networks promotes coupling between the thermal and the electric sector, producing flexibility on the electric grid.

Heating ventilating air-conditioning (HVAC) systems have been increasingly widespread in Italy: they can exploit renewable energies, are energy efficient systems, do not directly consume fossil fuels, and in the post-pandemic era, have also been subject to incentive processes by the Italian government. In South Tyrol, subject to harsh climates in both the winter and summer seasons, ground-source heat pump (GSHP) systems can be an excellent solution for the air conditioning of buildings. Unfortunately, too often, the design of HVAC systems with borehole heat exchangers (BHEs) is not adequate, and therefore, an innovative and expeditious numerical solution is proposed. A new numerical element (named Type285), written in Fortran code, was developed for TRNSYS 18 and able to implement the main features of BHEs and the surrounding aquifer. Type285 was compared with numerical models present in the literature (using hydrogeological software such as MODFLOW) and validated with the experimental data. The demonstration of the exchanged energy increase between the BHE and subsoil due to the increase in the groundwater flow velocity was carried out and evaluated. The choice to simulate BHE in TRNSYS using Type285 can be a fast and advantageous solution for HVAC system design.

This paper discusses the development of a coupled Q-learning/fuzzy control algorithm to be applied to the control of solar domestic hot water systems. The controller brings the benefit of showing performance in line with the best reference controllers without the need for devoting time to modelling and simulations to tune its parameters before deployment. The performance of the proposed control algorithm was analysed in detail concerning the input membership function defining the fuzzy controller. The algorithm was compared to four standard reference control cases using three performance figures: the seasonal performance factor of the solar collectors, the seasonal performance factor of the system and the number of on/off cycles of the primary circulator. The work shows that the reinforced learning controller can find the best performing fuzzy controller within a family of controllers. It also shows how to increase the speed of the learning process by loading the controller with partial pre-existing information. The new controller performed significantly better than the best reference case with regard to the collectors’ performance factor (between 15% and 115%), and at the same time, to the number of on/off cycles of the primary circulator (1.2 per day down from 30 per day). Regarding the domestic hot water performance factor, the new controller performed about 11% worse than the best reference controller but greatly improved its on/off cycle figure (425 from 11,046). The decrease in performance was due to the choice of reward function, which was not selected for that purpose and it was blind to some of the factors influencing the system performance factor.