Search Results (98)

Accurate heat load forecasting is essential for the efficiency of District Heating Systems (DHS). Still, it is challenged by the need to model long-term temporal dependencies and nonlinear relationships with weather and other factors. This study proposes a hybrid deep learning framework combining an attention-enhanced Neural Basis Expansion Analysis for Time Series (N-BEATS) model and eXtreme Gradient Boosting (XGBoost). The N-BEATS component, with a multi-head self-attention mechanism, captures temporal dynamics, while XGBoost models non-linear impacts of external variables. Predictions are integrated using an optimized weighted averaging strategy. Evaluated on a dataset from 103 heating units, the model outperformed 13 baselines, achieving an MSE of 0.4131, MAE of 0.3732, RMSE of 0.6427, and R² of 0.9664. This corresponds to a reduction of 32.6% in MSE, 32.0% in MAE, and 17.9% in RMSE, and an improvement of 5.1% in R² over the best baseline. Ablation studies and statistical tests confirmed the effectiveness of the attention mechanism and ensemble strategy. This model provides an efficient solution for DHS load forecasting, facilitating optimized energy dispatch and enhancing system performance. Full article

This study empirically delves into whether residential heating methods significantly affect apartment prices in Uiwang City, a suburban city near the Seoul Metropolitan area, South Korea. Using data from 1256 apartment sales, where both district heating systems (DHSs) and individual heating systems (IHSs) coexist, a hedonic price equation was estimated to analyze the impact of the heating method choices on housing values. Various housing attributes, including physical, locational, and environmental factors, were controlled, and multiple regression models were compared to identify the best-performing specification. The results show that apartments equipped with a DHS are priced, on average, KRW 92 million (USD 72 thousand) higher than those with an IHS. The price difference corresponds to KRW 849 thousand (USD 665) per m² and possesses the statistical significance at the 5% level. Moreover, it is quite meaningful, representing roughly 11.2% of the price of an average apartment. These findings suggest that the use of DHS has a positive effect on apartment prices that reflect consumers’ preferences, beyond its advantages in stable heat supply and energy cost savings. This article provides empirical evidence that DHS can serve as an important urban infrastructure contributing to asset value enhancement. Although this study is based on a specific geographic area and caution must be exercised in generalizing its findings, it reports the interesting finding that residential heating method significantly affects housing prices. Full article

This study explores modernization strategies for existing district heating (DH) networks to enhance their efficiency and sustainability, focusing on achieving net-zero emissions in urban heating systems. Building upon a literature review and expert interviews, we developed a strategic decision-support framework that outlines distinct strategies for retrofitting district heating grids and includes a portfolio analysis. This framework serves as a tool to guide DH operators and stakeholders in selecting well-founded modernization pathways by considering technical, economic, and social dimensions. The review identifies several promising measures, such as reducing operational temperatures at substations, implementing optimized substations, integrating renewable and waste heat sources, implementing thermal energy storage (TES), deploying smart metering and monitoring infrastructure, and expanding networks while addressing public concerns. Additionally, the review highlights the importance of stakeholder engagement and policy support in successfully implementing these strategies. The developed strategic decision-support framework helps practitioners select a tailored modernization strategy aligned with the local context. Furthermore, the findings show the necessity of adopting a comprehensive approach that combines technical upgrades with robust stakeholder involvement and supportive policy measures to facilitate the transition to sustainable urban heating solutions. For example, the development of decision-support tools enables stakeholders to systematically evaluate and select grid modernization strategies, directly helping to reduce transmission losses and lower greenhouse gas (GHG) emissions contributing to climate goals and enhancing energy security. Indeed, as shown in the reviewed literature, retrofitting high-temperature district heating networks with low-temperature distribution and integrating renewables can lead to near-complete decarbonization of the supplied heat. Additionally, integrating advanced digital technologies, such as smart grid systems, can enhance grid efficiency and enable a greater share of variable renewable energy thus supporting national decarbonization targets. Further investigation could point to the most determining context factors for best choices to improve the sustainability and efficiency of existing DH systems. Full article

Given the widespread use of gas-fired boilers and combined heat and power (CHP) units in existing district heating (DH) systems, this study investigates the integration of medium-scale heat pumps (HPs) into such configurations. Fifteen DH system variants were analysed, differing in installed HP capacity, operational strategies, and the synchronisation of heat and electricity production with thermal demand. A dynamic simulation model incorporating real-world equipment performance was developed to assess energy efficiency, environmental impact, and economic viability under three distinct energy price scenarios. The results demonstrate that an HP sized to 17% of the total heating capacity of the DH system achieves a 54% decrease in primary energy consumption and a 68% decrease in emissions compared to the base system. Larger HP capacities enhance environmental performance and increase the share of renewable energy but also entail higher investment. An economic analysis reveals that electricity-to-gas price ratios strongly influence the cost-effectiveness of HP integration. Under favourable electricity pricing conditions, systems with HP operational priority achieve the lowest levelized cost of heating. The most economically viable configuration consists of 600 kW HP and achieves a payback period of 4.7 years. The findings highlight the potential for HPs to decarbonize DH systems while emphasising the importance of market conditions and system design in ensuring economic feasibility. Full article

District heating (DH) systems offer a sustainable solution to local energy needs by improving energy efficiency, reducing emissions, and fostering economic development. Despite their growing technological relevance, DH systems remain underexplored in the economics, business, and management literature. This study addresses this gap by conducting a bibliometric analysis of DH research at the intersection of these fields, using data extracted from the Web of Science. We identify major theoretical foundations, including the resource-based view, stakeholder theory, and institutional economics, and explore key themes such as economic viability, business model innovation, regulatory frameworks, and sustainability strategies. By framing DH systems within broader economic and managerial discourses, our findings highlight the interdisciplinary nature of DH research and suggest critical avenues for future investigation, including the role of emerging technologies, consumer behavior, and policy design, and contribute to low-carbon, sustainable development. Full article

This study aims to improve the operational efficiency of district heating (DH) systems by introducing a novel control method based on variable flow rate control, without compromising indoor comfort. The novelty of this work lies in its integrated analysis of flow control and substation configurations in DH networks, linking real-world operational strategies with mathematical modeling to improve energy efficiency and infrastructure costs. Using a case study from Omsk, Russia, where supply temperatures and energy demand profiles are traditionally rigid, the proposed approach utilizes operational data, including outdoor temperature, supply/return temperature, and hourly consumption patterns, to optimize heat delivery. A combination of flow rate adjustments, bypass line implementation, and selective control strategies for transitional seasons (fall and spring) was modeled and analyzed. The methodology integrates heat meter data, indoor temperature tracking, and Supervisory Control and Data Acquisition (SCADA)-like system inputs to dynamically adapt supply temperatures while avoiding overheating and reducing distribution losses. The results show a significant reduction in excess heat supply during warm days, with improvements in heat demand prediction accuracy (17.3% average error) compared to standard models. Notably, the optimized configuration led to a 21% reduction in total greenhouse gas (GHG) emissions (including 6537 tons of CO₂ annually), a 55.3% decrease in annualized operational costs, and a positive net present value (NPV) by year nine, with an internal rate of return (IRR) of 25.4%. Compared to conventional scenarios, the proposed solution offers better economic performance without requiring extensive infrastructure upgrades. These findings demonstrate that flexible, data-driven DH control is a feasible and sustainable alternative for aging networks in cold-climate regions. Full article

This study investigates improving district heating (DH) systems by analyzing the effects of low-temperature operation on network efficiency, heat losses, and indoor temperature stability. A mathematical model is developed to simulate building heat performance under different supply temperatures, substation connection types, and envelope materials. The methodology involves detailed hourly heat load simulations and optimization techniques to assess the impact of temperature flexibility and heat accumulation within buildings. The results reveal that a 10 °C reduction in supply temperature leads to a heat loss decrease of up to 20%, significantly improving system efficiency. Moreover, buildings with higher thermal inertia and indirect substation connections exhibit better resilience to short-term temperature fluctuations, ensuring more stable indoor conditions. The analysis also demonstrates that optimizing temperature control can reduce operational costs by 19%, primarily by minimizing excessive heat supply and utilizing stored thermal energy effectively. Despite slight temperature fluctuations in extreme conditions, the system maintains indoor comfort levels within acceptable limits. This study concludes that transitioning to a lower-temperature DH system is feasible without compromising reliability, provided heat accumulation effects and supply flexibility are carefully managed. These findings offer a replicable approach for improving DH efficiency in networks with diverse building configurations. Full article

This review paper provides a comprehensive examination of current strategies and technical considerations for reducing return temperatures in district heating (DH) systems, aiming to enhance the utilization of available thermal energy. Return temperature, a parameter indirectly influenced by various system-level factors, cannot be adjusted directly but requires careful management throughout the design, commissioning, operation, and control phases. This paper explores several key factors affecting return temperature, including DH network, heat storage, and control strategies as well as the return temperature effect on the heat source. This paper also considers the influence of non-technical aspects, such as pricing strategies and maintenance practices, on system performance. The discussion extends to the complex interplay between low return temperatures and temperature differences, and between operational temperature schemes and economic considerations. Concluding remarks emphasize the importance of adopting a holistic approach that integrates technical, operational, and economic factors to improve DH system efficiency. This review highlights the need for comprehensive system-level optimization, effective management of system components, and consideration of unique heat production characteristics. By addressing these aspects, this study provides a framework for advancing DH system performance through optimized return temperature management. Full article

The relevance is introducing fourth generation district heating (4GDH), which decreases operation and maintenance costs by utilizing the efficiency of low temperature district heating (LTDH). The aim is to develop a methodology allowing for a more flexible heat demand model and accurate function describing the relationship between outdoor temperature and heat demand. It is represented by a black-box model based on historical data collected from heating, ventilation, and air conditioning (HVAC) systems. Energy delivery/consumption is analyzed with the help of a set of statistical and regression formulas. The analysis of operational data is then transformed to methodology to regulate heat supply with combined heat-and-power (CHP) generation. The key features are that the model takes into account thermal capacity and type of substation; the district heating (DH) plant is not assumed to have a fixed return temperature and generation profile. The novelty is an emphasis on DH operation and introduction of statistics into a dynamic simulation model. With no abnormal buildings, higher accuracy of modeling is achieved. Most of the consumers are pretty similar in thermal response, even though specific energy demand and heated volume may differ. Heat demand of an old building is better simulated with discrete regression, while those with pump-equipped substations are modeled with linear regression. Full article

The ongoing transformation of district heating systems (DHSs) aims to reduce emissions and increase renewable energy sources. The objective of this work is to integrate solar thermal (ST) and seasonal aquifer thermal energy storage (ATES) in various scenarios applied to a large DHS. Mixed-integer linear programming (MILP) is used to develop a comprehensive model that minimizes operating costs, including heat pumps (HPs), combined heat and power (CHP) units, electric heat boilers (EHBs), heat-only boilers (HOBs), short-term thermal energy storage (TES), and ATES. Different ATES scenarios are compared to a reference without seasonal TES (potential of 15.3 GWh of ST). An ATES system with an injection well temperature of about 55 °C has an overall efficiency of 49.8% (58.6% with additional HPs) and increases the integrable amount of ST by 178% (42.5 GWh). For the scenario with an injection well temperature of 20 °C and HPs, the efficiency is 86.6% and ST is increased by 276% (57.5 GWh). The HOB heat supply is reduced by 8.9% up to 36.6%. However, the integration of an ATES is not always economically or environmentally beneficial. There is a high dependency on the configurations, prices, or emissions allocated to electricity procurement. Further research is of interest to investigate the sensitivity of the correlations and to apply a multi-objective MILP optimization. Full article

To improve the energy, operational, and ecological efficiency of a district heating system (DHS) powered by a combined heat and power (CHP) plant or a heating plant, thermal energy storage (TES) should be used. The presented paper examines the impact of the use and operation of TES built in a CHP plant supplying a large DHS, based on the amount of particulates emitted into the atmosphere. Detailed research was carried out for the Siekierki–Warsaw and Białystok CHP plants in Poland. The analysis helped to determine the factors affecting the reduction in pollutant emissions and the volume of the energy effect of using TES in the CHP plant. In order to objectify the results of the comparative analysis of the impact of TES in the CHP plant on the emission of particulates, the so-called comparative index (CI) was introduced. The CI takes into account the volume of electricity and heat production and climatic conditions in the analyzed time periods. The CI for the analyzed years should have a similar value so that the results of the comparative analysis are fully representative. This condition is met for the CHP plant and DHS of Białystok, so the detailed results of the analysis are presented for this facility. As a result of the application of TES in the Białystok CHP plant, significant environmental effects related to the reduction in particulate emissions have been achieved; for example, the total amount of annual particulate matter (PM) emission (PM₁₀ and PM_2.5) has been reduced by 27% and the maximum emission by 29%. On the other hand, the average decrease in particulate emissions in the heating season varied in the range of 10–50%, while in the summer season, the values of particulate emissions were at a comparable level. A significant decrease in annual and one-hour average concentrations for PM₁₀ and PM_2.5 and particulate fallout for these two analyzed years was also found. The use of TES to reduce the occurrence and nuisance of the smog phenomenon, the main components of which are PM, is proposed, and selected models of forecasting concentrations of pollutants in the air, including particulate emissions, are presented in order to implement this type of activity. Full article

Demand response (DR) enhances building energy flexibility, but its application in hybrid heating systems with dynamic pricings remains underexplored. This study applied DR via heating setpoint adjustments based on dynamic electricity and district heating (DH) prices to a building heated by a hybrid ground source heat pump (GSHP) system coupled to a DH network. A cost-effective control was implemented to optimize the usage of GSHP and DH with power limitations. Additionally, four DR control algorithms, including two single-price algorithms based on electricity and DH prices and two dual-price algorithms using minimum heating price and price signal summation methods, were tested for space heating under different marginal values. The impact of DR on ventilation heating was also evaluated. The results showed that applying the proposed DR algorithms to space heating improved electricity and DH flexibilities without compromising indoor comfort. A higher marginal value reduced the energy flexibility but increased cost savings. The dual price DR control algorithm using the price signal summation method achieved the highest cost savings. When combined with a cost-effective control strategy and power limitations, it reduced annual energy costs by up to 10.8%. However, applying the same DR to both space and ventilation heating reduced cost savings and significantly increased discomfort time. Full article

This review presents a comprehensive examination of recent advancements and findings related to return-temperature reduction in District Heating (DH) systems, with a focus on enhancing overall system efficiency at end-user sites. The review categorizes and clarifies various return-temperature reduction techniques, emphasizing aspects such as building energy performance, heat emitters, thermostatic radiator valves, and substation units. One shall note that return temperature is not a parameter that can be directly controlled within a DH system; instead, it is influenced indirectly by adjusting various system parameters throughout the design, commissioning, operation, and control phases. Key insights include the direct impact of heat demand on return temperatures; the pivotal role of indoor heating systems in optimizing thermal energy use in relation to heat demand; the significance of thermostatic radiator valves in regulating heat output and maintaining low return temperatures; the advantages of ventilation radiators and add-on fans in enhancing radiator efficiency; the necessity for effective substation operation to improve system cooling capacity; and the critical role of operational control strategies in achieving optimal system performance. These findings underscore the need for integrated approaches in DH system design and operation to achieve lower return temperatures and improve overall system efficiency. Full article

European Union (EU) energy markets are changing rapidly. After the recent turmoil, a new wave of EU legislation is once again reshaping the way energy should be used in the EU, emphasizing not only the increasing importance of using renewable and local energy sources but also highlighting the importance of energy efficiency and decarbonization of high to abate sectors (including aviation and marine fuels). Heating and cooling account for about half of the total gross final energy consumption in the EU. This article explores the novel concept of using waste heat from the flexible Fischer–Tropsch (FT) process (FLEXCHX) in the existing district heating network, resulting in tri-generation: FT C5+ liquids, heat, and electricity. FLEXCHX provides operation flexibility and combines advanced biomass gasification, catalytic liquefaction, electrolysis, and waste heat recovery, allowing use of biomass residues in a more sustainable way. Our results, based on the Kaunas district heating (DH) system, show that this process could be integrated into the existing district heating network in Northern Europe and successfully compete with existing heat-only boilers and CHPs using biomass or municipal waste, resulting in more efficient use of biomass and savings accumulated up to EUR 200 million over the study period in the analysis (2020–2050), supplying up to 30% of the heat in the Kaunas DH system. Enriching the FT process with hydrogen (using electrolysis) could result in additional FLEXCHX utilization benefits by creating demand for cheap excess electricity that might otherwise be curtailed. Full article

Artificial neural networks (ANNs) can be used for accurate heat load forecasting in district heating systems (DHSs). This paper presents an application of a shallow ANN with two hidden layers in the case of a local DHS. The developed model was used to write a simple application in Python 3.10 that can be used in the operation of a district heating plant to carry out a preliminary analysis of heat demand, taking into account the ambient temperature on a given day. The model was trained using the real data from the period 2019–2022. The training was sufficient for the number of 150 epochs. The prediction effectiveness indicator was proposed. In the considered case, the effectiveness of the trained network was 85% and was better in comparison to five different regression models. The developed tool was based on an open-source programming environment and proved its ability to predict heating load. Full article