Hybrid heat pump (HHP) represents a heating system (HS) that simultaneously utilises two or more energy sources. The key novelty is the integration of an optimised control strategy, effectively maximising the efficiency of both the heat pump and gas boiler segments. In this study, a HHP system that consists of an air-source heat pump (ASHP) and a condensing-type gas boiler (GB) for a two-story single-family house was calculated and validated. The results from three different calculation methods were obtained: the heat pump manufacturer’s calculator, the simulator programme, and the calculations based on real measured data. Two operation modes were tested: economic mode, where the cheapest energy resource becomes the priority, and ecological mode, where the system operates using the energy resource with lowest CO₂ emissions. When comparing consumed energy, there was a 15.36% variance between the manufacturer’s program and the actual measured data, while for produced energy, the difference amounted to 25.81%. It was found that the balance point temperature is −7 °C, where the heat pump unit of the HHP system can cover necessary heat losses until that point, while the gas boiler operates at lower temperatures. Changes in electricity price can significantly influence the point of the system switch, but the possibility to cover heat loads of the heat pump unit always has to be taken into account.

Accurate forecasting of heat demand in district heating networks is essential for their efficient and sustainable operation. This paper presents a novel approach using a random parameter linear regression model to forecast heat demand, distinguishing itself from classical linear regression models by its ability to address unobserved heterogeneity among parameters. Through a case study in Estonia and utilizing data from 2018 to 2023 and considering seasonality and consumption patterns, the study investigates determinants of heating demand in district heating networks. Two models were trained for heating and non-heating seasons. Results indicate significant impacts of weather conditions, energy prices, time of day, and network infrastructure on heat supply during the heating season, while only time of day and electricity prices were significant drivers during the non-heating season, with no notable influence of weather conditions. Prediction accuracy was slightly enhanced using the random parameter linear regression model, with a mean absolute percentage error of 9.66 % compared to 9.99 % for the Multi Linear Regression Model on the testing set.

Distribution systems are a main part of district heating (DH) networks, yet their environmental impact is seldom investigated. In this study, a detailed comparison of two main pipe types used in DH networks is conducted, namely pre-insulated steel pipes and cross-linked polyethylene (PEX) pipes. The study introduces the technical characteristics of the pipes and applies a life cycle assessment (LCA) approach to evaluate their environmental performance under a unified functional unit. In addition, scenario analyses were performed to assess the influence of PEX service lifetime and steel recycling rates. The results show that PEX pipes exhibit lower impacts in several categories, including freshwater ecotoxicity (986.35 vs. 2990.1 kg 1,4-DCB-eq) and particulate matter formation (39.0 vs. 69.2 kg PM2.5-eq), mainly due to lower material requirements. However, higher fossil energy use is observed for PEX (9750.7 vs. 7416.3 kg oil-eq). Climate change impacts are comparable between systems under baseline conditions. Scenario analysis indicates that extending the PEX service lifetime from 30 to 50 years reduces climate change impacts by approximately 40%, demonstrating the importance of long-term durability. For steel pipes, increasing recycling rates from 0 % to 80 % reduces freshwater ecotoxicity by ~80 % and material resource use by ~80 %, highlighting the importance of end-of-life modelling. Overall, the results demonstrate that the environmental performance of DH pipes is governed by the combined influence of material selection, service lifetime, and recycling strategy rather than by material choice alone.