Search Results (52)

To improve the accuracy of heating load forecasting and effectively address the energy waste caused by supply–demand imbalances and uneven thermal distribution, this study innovatively proposes a hybrid prediction model incorporating seasonal adjustment strategies. The model establishes a dynamically adaptive forecasting framework through synergistic integration of the Sparrow Search Algorithm (SSA), Variational Mode Decomposition (VMD), and Long Short-Term Memory (LSTM) network. Specifically, VMD is first employed to decompose the historical heating load data from Arizona State University’s Tempe campus into multiple stationary modal components, aiming to reduce data complexity and suppress noise interference. Subsequently, the SSA is utilized to optimize the hyperparameters of the LSTM network, with targeted adjustments made according to the seasonal characteristics of the heating load, enabling the identification of optimal configurations for each season. Comprehensive experimental evaluations demonstrate that the proposed model achieves the lowest values across three key performance metrics—Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE)—under various seasonal conditions. Notably, the MAPE values are reduced to 1.3824%, 0.9549%, 6.4018%, and 1.3272%, with average error reductions of 9.4873%, 3.8451%, 6.6545%, and 6.5712% compared to alternative models. These results strongly confirm the superior predictive accuracy and fitting capability of the proposed model, highlighting its potential to support energy allocation optimization in district heating systems. Full article

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 article presents an analysis on natural gas heating in residential areas, focusing on two primary systems: (1) local heating, where piped gas is delivered directly to individual dwellings equipped with autonomous gas boilers, and (2) district heating, where gas or an alternative fuel powers a central heating plant, and the generated heat is distributed to buildings via a thermal network. The choice between these systems should first consider safety and environmental factors, followed by the urban characteristics of the settlement. In particular, building typology—such as size, function, and spatial configuration—and urban topology, referring to the relative positioning of buildings, play a crucial role. For example, very tall buildings often exclude the use of piped gas due to safety concerns, whereas in other cases, economic efficiency becomes the determining factor. To support decision-making, a comparative cost analysis is conducted, assessing the required infrastructure for both systems, including pipelines, boilers, and associated components. The study identifies representative residential building types in selected urban areas of Serbia and Czechia that are suitable for either heating approach. Additionally, the article examines the broader energy context in both countries, with emphasis on recent developments in the natural gas sector and their implications for urban heating strategies. Full article

Steam networks are widely used for industrial heat supply. High-temperature heat pumps (HTHPs) are an increasingly attractive low-emission solution to traditional steam generation, which could also improve the operational efficiency and energy demand flexibility of industrial processes. This work characterises 4-bar steam supply via HTHPs and aims to assess how variations in power input that result from flexible HTHP operation may affect steam flow and temperature, both with and without a downstream steam accumulator (SA). First, steady-state modelling is used for system design. Then, dynamic component models are developed and used to simulate the system response to HTHP power input variations. The performance of different SA integration layouts and sizes is evaluated. Results demonstrate that steam supply fluctuations closely follow changes in HTHP operation. A downstream SA is shown to mitigate these variations to an extent that depends on its capacity. Practical SA sizing recommendations are derived, which allow for the containment of steam supply fluctuations within acceptability. By providing a basis for evaluating the financial viability of flexible HTHP operation for steam provision, the results support clean technology’s development and uptake in industrial steam and district heating networks. Full article

The significant contribution of buildings to the global primary energy consumption necessitates the application of energy management methodologies at a building scale. Although dynamic simulation tools and decision-making algorithms are core components of energy management methodologies, they are often accompanied by excessive computational cost. As future controlling structures tend to become autonomized in building heating layouts, encouraging distributed heating services, the research scope calls for creating lightweight building energy system modeling as well monitoring and controlling methods. Following this notion, the proposed methodology turns a programmable controller into a smart thermostat that utilizes surrogate modeling formed by the ALAMO approach and is applied in a 4-m-by-4-m-by-2.85-m environmental chamber setup heated by a heat pump. The results indicate that the smart thermostat trained on the indoor environmental conditions of the chamber for a one-week period attained a predictive RMSE of 0.082–0.116 °C. Consequently, it preplans the heating hours and applies preheating controlling strategies in real time effectively, using only the computational power of a conventional controller, essentially managing to attain at least 97% thermal comfort on the test days. Finally, the methodology has the potential to meet the requirements of future building energy systems featured in urban-scale RES-based district heating networks. Full article

This paper discusses the development of an Integrated Building Energy Model (IBEM) in MATLAB (R2024b) for a university campus building. In the general context of the development of integrated energy district models to guide the evolution and planning of smart energy grids for increased efficiency, resilience, and sustainability, this work describes in detail the development and use of an IBEM for a university campus building featuring a heat pump-based heating/cooling system and PV generation. The IBEM seamlessly integrates thermal and electrical aspects into a complete physical description of the energy performance of a smart building, thus distinguishing itself from co-simulation approaches in which different specialized tools are applied to the two aspects and connected at the level of data exchange. Also, the model, thanks to its physical, white-box nature, can be instanced repeatedly within the comprehensive electrical micro-grid model in which it belongs, with a straightforward change of case-specific parameter settings. The model incorporates a heat pump-based heating/cooling system and photovoltaic generation. The model’s components, including load modeling, heating/cooling system simulation, and heat pump implementation are described in detail. Simulation results illustrate the building’s detailed power consumption and thermal behavior throughout a sample year. Since the building model (along with the whole campus micro-grid model) is implemented in the MATLAB Simulink environment, it is fully portable and exploitable within a large, world-wide user community, including researchers, utility companies, and educational institutions. This aspect is particularly relevant considering that most studies in the literature employ co-simulation environments involving multiple simulation software, which increases the framework’s complexity and presents challenges in models’ synchronization and validation. 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

This study presents a novel framework for city-level energy planning and retrofitting, tailored to Danish cities and neighborhoods. The framework addresses the challenges of large-scale urban energy modeling by integrating automated processes for data collection, energy demand prediction, and renewable energy integration. It combines open-source simulation tools and validated datasets, enabling efficient and scalable predictions of energy performance across urban areas, including streets, districts, and entire cities, with minimal user input. The key components include data collection and demand modeling, energy resource estimation, performance gap evaluation, and the design of retrofitting strategies with renewable energy integration. The DanCTPlan energy planning tool, developed based on this framework, was applied to two case studies in Denmark: a single street with 101 buildings and a district comprising five streets with 1284 buildings. In the single-street case, retrofitting all buildings to meet current regulations resulted in a 60.8% reduction in heat demand and a 5.8% reduction in electricity demand, with significant decreases in peak energy demands. The district-level retrofitting measures led to a 29.5% reduction in heat demand and a 2.4% reduction in electricity demand. Renewable energy scenarios demonstrated that photovoltaic systems supplying 30% of electricity demand and solar thermal systems meeting 10% of heating demand would require capacities of 2218 kW and 3540 kW, respectively. The framework’s predictive capabilities and flexibility position it as a robust tool to support decision-makers in developing sustainable and cost-effective energy strategies, paving the way toward establishing energy-efficient and positive energy districts. Full article

The increasing demand for sustainable energy solutions has spurred significant interest in cogeneration technologies. This study introduces a novel integrated organic Rankine cycle (ORC) and vapor compression cycle (VCC) system, specifically designed to enhance energy efficiency and reduce greenhouse gas emissions in industrial applications and district heating systems. The key innovation lies in the development of an advanced coupling mechanism that seamlessly connects the ORC and VCC, enabling more efficient utilization of low-grade heat sources. By optimizing working fluid selection and implementing a shared shaft connection between the ORC turbine and VCC compressor, the system achieves dual functionality—simultaneous electricity generation and cooling—with higher efficiency than conventional methods. Thermodynamic analyses and experimental results demonstrate that the proposed ORC–VCC system can significantly reduce operational costs and decrease reliance on fossil fuels by leveraging renewable energy sources and industrial waste heat. Additionally, the study addresses integration challenges by introducing specialized components and a modular design approach that simplifies installation and maintenance. This innovative system not only enhances performance but also offers scalability for various industrial applications. By providing a detailed evaluation of the ORC–VCC integration and its practical implications, this work underscores the system’s potential to contribute substantially to a sustainable energy transition. The findings offer valuable insights for future research and development, highlighting pathways to overcome existing barriers in cogeneration technologies. 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

Outdated, oversized variable speed pump drives (VSDPs) in industry lead to sub-optimal energy efficiency and considerable energy losses. This paper proposes methods to develop 2D efficiency maps for motors, converters, and pumps using polynomial surface fitting, which enables efficiency evaluation in a wide operating range. The method was applied to an oversized VSDP in an industrial chilled water supply system, comparing the original system with five alternative VSDP combinations with high-efficiency motors and pumps. The five VSDP variants demonstrated average energy savings of around 30%, with the synchronous reluctance motor (SRM) configurations outperforming the induction motor (IM) configurations by up to 7 percentage points, particularly at low loads. The high-efficiency SRM-based 252-IE5 variant delivered the best overall energy performance, highlighting the benefits of optimised system sizing and motor selection for energy savings. The proposed method can be used in both industrial and residential applications and offers great advantages in process systems that require variable flow and pressure of water or other fluids during operation, such as HVAC, water supply and wastewater treatment, district heating, etc. The development of a VSDP drive with efficient energy optimisation is an interdisciplinary problem of mechanical and electrical engineering, and without the interaction of engineers from both fields the result will not be optimal. We try to present our method so that it can be a reliable tool for mechanical, electrical, and other engineers or researchers to assist them in finding possible energy savings, performing energy audits, and selecting the most suitable components when modernising existing or developing new systems. Full article

This research focuses on exploring various techniques and models for simulating the random behavior of energy facilities or systems. These simulations are essential in identifying the likelihood of component failures within the studied facilities. By assessing the potential consequences of emergency scenarios, this analysis serves as a fundamental aspect of synthesizing and analyzing reliability in the cyber-physical system. Ultimately, the study aims to enhance the management and control of reliability and safety for these facilities. In this study, a unified heating source is considered as an energy facility (as part of district heating systems), for example, a combined heat and power plant. However, the developed methods and models have sufficient universality for their adaptation to other energy facilities without significant changes. The research methodology is based on the use of Markov random processes and laws of the probability theory. The basic model of the energy facilities is formulated for the conditions of the simplest events flow with appropriate assumptions and constraints, in particular, ordinary events and independence of events (failures and restorations). To take into account the non-ordinary events (failures) and dependences between some failures, corresponding modifications of the basic model are proposed. A computational experiment was carried out using the developed models, and graphical interpretations of the results are presented. The obtained results allow us to formulate some preliminary conclusions about the range of influence of the simulated factors on the reliability analysis of studied facilities and to outline conditions and areas of their admissible application. Full article

The decarbonization of the district heating (DH) sector is receiving attention worldwide. Solar energy and heat pump technologies are widely considered in existing and new DH networks. There is a need to understand the influence of solar energy on district heating experimentally. However, only a few university laboratories are focused on district heating aspects. Further, the concept of such laboratories is not adequately disseminated in the scientific literature. The main objective of this paper is to develop a conceptual design of a solar energy laboratory with a focus on district heating systems. The proposed concept forms part of the preliminary study carried out by a research group at the Tallinn University of Technology. First, a brief literature review on solar energy laboratory development is provided. Then, the conceptual design of such a laboratory is presented, along with a case study. Regardless of project size, the main components of a district heating-based solar energy laboratory are solar collectors, thermal energy storage (TES) tanks, and a control system. The proposed laboratory is expected to serve multiple roles, such as a practical laboratory to provide interdisciplinary courses for students, a research and experimental platform for researchers, and a cradle to achieve the campus green initiative. It is roughly estimated that the thermal energy output from the proposed laboratory would meet around 25% of the heat demand of the institutional building during the summer season (May, June, July, and August). It is expected that the present study will be a reference material for the development of innovative energy laboratories in educational institutions. Full article

The performance of the innovative configurations of the “efficient” thermal networks is a key topic in scientific research, focusing on distribution temperatures and integration with high-efficiency plants and renewable sources. As it already happens for the electricity prosumers, a thermal prosumer may feed the district heating network through a bidirectional exchange substation with the excess of the locally produced thermal energy (e.g., by means of solar thermal plant) or with the waste heat recovered in the industrial processes. The Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA) and the Alma Mater Studiorum University of Bologna (UNIBO) designed a bidirectional substation prototype, based on a return-to-supply configuration, and tested steady-state and dynamic conditions to evaluate performances and optimization measures. In this paper, the Modelica language and Dymola software were used to run a multi-domain simulation and model-based design of the substation, starting from a new heat exchanger model featuring variable efficiency, based on the thermal resistance scaling method. Control systems and components were customized from models in standard libraries in order to reproduce the substation behavior under defined operating settings, and the model was validated on the abovementioned experimental tests. Numerical results in terms of exchanged powers, temperatures and flow rates were systematically compared to experimental data, demonstrating a sufficient agreement. In particular, the absolute mean deviation—in terms of temperature—between experimental and numerical data assessed over the entire tests remains contained in +/−1 °C. As further step of the analysis, an optimized model could be included as a component in a district heating network for further investigations on the prosumers’ effects on an existing traditional grid (e.g., in case of deep renovation of urban areas connected to district heating and/or creation of micro energy communities). Full article

South African universities boast a remarkable solar photovoltaic (PV) resource as a primary renewable energy component. Due to high peak demand tariffs and inherent prominent heating and cooling loads, fast and granular demand response programs are well established within typical campus grids, with electrical networks adapted towards hosting centralized PV plants and emergency diesel generation. With unreliable utility supply and aging infrastructure comes a natural landscape and niche application for campus microgrids (MG) in South Africa. One such case, the University of the Free State’s QwaQwa satellite campus in the Phuthaditjhaba district, is no exception to this, as it has sufficient solar PV generation, but it also has an unreliable utility component. This paper investigates a possible MG for the UFS QwaQwa campus with an emphasis on Hybrid PV-Diesel dispatch strategies, specifically, to ensure uptime during the loss of grid supply and decrease fuel usage. The proposed centralized diesel-PV MG system achieves a diesel cost reduction of 21.55%, based on simulated results using actual campus load data from 2019. The approach improves electricity availability, supplying 100% of all campus demand, compared to 70% under a de-centralized approach. Full article