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This article presents a complex and exhaustive review of the integration of renewable energy sources (RES) (specifically solar, geothermal, and hydraulic energies and heat pumps (HPs)) and the improvement of water pumping in district heating systems (DHSs) focused on low-temperature systems, to increase energy efficiency and environmental protection. For this aim, the main components of a DHS and the primary RES with applications in DHSs were described briefly. Finally, several case studies regarding the DHS in Timisoara, Romania, were analysed. Thus, by integrating water source HP (WSHP) systems in cooperation with solar thermal and photovoltaic (PV) collectors and reducing the supply temperature from 110 °C to 30 °C in DHS, which supplies the water radiators to consumers in a district of this city in a 58/40 °C regime of temperatures and produces domestic hot water (DHW) required by consumers at 52 °C, a thermal energy saving of 75%, a reduction in heat losses on the transmission network of 90% and a diminution of CO2 emissions of 77% were obtained. Installed PV panels generate 1160 MWh/year of electricity that is utilised to balance the electricity consumption of HP systems. Additionally, mounting pumps as turbines (PATs) for the recovery of excess hydraulic energy in the entire heating network resulted in electricity production of 378 MW, and the variable frequency drive's (VFD) method for speed control for a heating station pump resulted in roughly 38% more energy savings than the throttle control valve technique. [ABSTRACT FROM AUTHOR]

Highlights: [•] It is provided a detailed literature review of GSHP systems and their recent advances. [•] Typical simulation and ground TRT models for vertical GHEs are summarized. [•] A new GWHP with a special heat exchanger tested in laboratory is described. [•] We reviewed various hybrid GCHP systems for cooling or heating-dominated buildings. [•] Energy, economic and environmental performance of a closed-loop HGCHP system is analyzed. [ABSTRACT FROM AUTHOR]

Developing accurate models is necessary to optimize the operation of heating systems. A large number of field measurements from monitored heat pumps have made it possible to evaluate different heat pump models and improve their accuracy. This study used measured data from a heating system consisting of three heat pumps to compare five regression and two artificial neural network (ANN) models. The models' performance was compared to determine which model was suitable during the design and operation stage by calibrating them using data provided by the manufacturer and the measured data. A method to refine the ANN model was also presented. The results indicate that simple regression models are more suitable when only manufacturers' data are available, while ANN models are more suited to utilize a large amount of measured data. The method to refine the ANN model is effective at increasing the accuracy of the model. The refined models have a relative root mean square error (RMSE) of less than 5%. [ABSTRACT FROM AUTHOR]