Search Results (85)

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

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

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

With accelerating urbanization, the outdoor thermal environment has become a critical factor affecting the thermal comfort of public spaces, particularly in high-density commercial districts and pedestrian-concentrated areas. To enhance thermal comfort and livability in public outdoor space, this study proposes a thermal demand-responsive design approach that integrates thermal conditions with pedestrian flow dynamics. A commercial pedestrian mall featuring semi-open public spaces and air-conditioned interior retail areas was selected as a case study. Computational Fluid Dynamics (CFD) simulations were conducted based on design-phase documentation and field measurements to model the thermal environment. The Universal Thermal Climate Index (UTCI) was employed to assess thermal comfort levels, and thermal discomfort was further quantified using the Heat Discomfort Index (HI). Simultaneously, pedestrian density distribution (λ) was analyzed using the agent-based simulation software MassMotion (Version 11.0). A demand of thermal comfort (DTC) index was developed by coupling UTCI-based thermal conditions with pedestrian density, enabling the spatial quantification of thermal demand across the whole commercial pedestrian mall. For example, in a sidewalk area parallel to the main street, several points exhibited high discomfort levels (HI = 0.95) but low pedestrian volume, resulting in DTC values approximately 0.2 units lower than adjacent zones with lower discomfort levels (HI = 0.7) but higher foot traffic. Such differences demonstrate how DTC can reveal priority areas for intervention. Key zones requiring thermal improvement were identified based on DTC values, providing a quantitative foundation for outdoor thermal environment design. This method provides both a theoretical foundation and a practical tool for the sustainable planning and optimization of urban public spaces. 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

Under the dual imperatives of air pollution control and energy conservation, this study proposes an enhanced optimization framework for combined heat and power (CHP) district heating systems based on bypass thermal storage (BTS). In contrast to conventional centralized tank-based approaches, this method leverages the dynamic hydraulic characteristics of secondary network bypass pipelines to achieve direct sensible heat storage in circulating water, significantly improving system flexibility and energy efficiency. The core innovation lies in addressing the critical yet under-explored issue of control valve dynamic response, which profoundly impacts system operational stability and economic performance. A quality regulation strategy is systematically implemented to stabilize circulation flow rates through temperature modulation by establishing a supply–demand equilibrium model under bypass conditions. To overcome the limitations of traditional feedback control in handling hydraulic transients and heat transfer dynamics in the plate heat exchanger, a Model Predictive Control (MPC) framework is developed, integrating a data-driven valve impedance-opening degree correlation model. This model is rigorously validated against four flow characteristics (linear, equal percentage, quick-opening, and parabolic) and critical impedance parameters (maximum/minimum controllable impedance). This study provides theoretical foundations and technical guidance for optimizing secondary network heating systems, enhancing overall system performance and stability, and promoting energy-efficient development in the heating sector. Full article

About 50% of Italian households’ overall energy consumption is satisfied by natural gas, mainly for space heating, leading to substantial CO₂ emissions. In Italy’s mild climate, fourth-generation district heating (4GDH) networks coupled with renewable energy sources (RESs) could represent a viable option for reaching the ambitious space heating decarbonization objectives set by the EU. In this paper, such a decarbonization pathway, consisting in a centralized heat pump (HP)-powered 4GDH network, with and without the addition of a distributed PV plant, is assessed and compared with the individual natural gas boilers-based Italian reference scenario. A cluster of buildings, comprising 200 dwellings, representative of common households in Rome, has been chosen as the case study. Starting from the cluster’s hourly space heating demand, a semi-dynamic MATLAB/Simulink model has been developed to size the technological components and evaluate their performance with respect to outdoor environmental conditions. The scenario comparison is carried out by means of techno-economic and environmental indicators: the levelized cost of heat (LCOHE), CO₂ emissions, and carbon avoidance cost (CAC). Moreover, a sensitivity analysis has been carried out to address the uncertainty regarding the main economic parameters, namely the electricity and natural gas price and the HP and DH investment cost. The results show that 4GDH-based layouts significantly reduce CO₂ emissions, at the expense of the LCOHE. The sensitivity analysis highlights how a significant reduction in both the electricity price and the DH network capital cost are required for achieving price parity with the fossil-fuel based scenario. Full article

The International Energy Agency (IEA) 2023 report highlights that global energy losses have persisted over the years, with 32% of the energy supply lost in 2022 alone. To mitigate this, this research adopts optimisation to enhance the efficiency of district heating networks (DHNs), a key global energy supply technology. Given the dynamic nature of DHNs and the challenges in predicting disturbances, a dynamic real-time optimisation (DRTO) approach is proposed. However, this research does not implement DRTO; instead, it develops a fast grey-box linear parameter varying (LPV) model for future integration into the DRTO algorithm. A high-fidelity physical model replicating theoretical time delays in pipes serves as a reference for model validation. For a single pipe, the grey-box model achieved a 91.5% fit with an $R^2$ value of 0.993 and operated 5 times faster than the reference model. At the DHN scale, it captured 98.64% of the reference model’s dynamics, corresponding to an $R^2$ value of 0.9997, while operating 52 times faster. Low-fidelity physical models (LFPMs) were also developed and validated, proving to be more precise and faster than the grey-box models. This research recommends performing dynamic optimisation with both models to determine which better identifies local minima. 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 recent rise in temperatures in urban areas has raised concerns about various health problems, such as heat-related illnesses. This study quantified the number of individuals exposed to outdoor heat during the daytime in the summertime waterfront area of Tokyo. Conventional meteorological observation and administrative data are insufficient for high-resolution analyses of people flow and heat conditions in urban environments. Therefore, this study introduced a new methodology combining urban computational fluid dynamics (CFD) and mobile phone global positioning system (GPS) data. A numerical simulation was performed to estimate the wet-bulb globe temperature (WBGT) by analyzing fluid dynamics and radiation models. The WBGT in parks was determined to be approximately 27 °C, while the on-road temperature exceeded 29 °C. Simultaneously, pedestrian density was assessed by collecting high-resolution mobile phone GPS data, revealing that pedestrians concentrated near stations, office areas, and shopping districts within a 5 km × 5 km area. Furthermore, a review of heat stroke cases (2010–2020) indicated that combining heat and people flow yielded stronger correlations with the number of heat stroke cases than considering heat alone. Finally, a new heat risk index was established, integrating heat, people flow, and aging rate, which more accurately predicted the heat stroke cases. Full article

This paper introduces an innovative optimization framework that integrates Long Short-Term Memory (LSTM) networks to enhance the synchronization and stability of urban integrated multi-energy systems (MESs), which include electricity, gas, and heating networks. The need for a holistic approach to manage these interconnected systems is driven by the increasing complexity of urban energy demands and the imperative to adhere to stringent environmental standards. The proposed methodology leverages LSTM networks for dynamic state estimation, enabling real-time and accurate predictions of energy demands and operational states across the different energy networks. This approach allows for the optimization of energy flows by adapting to fluctuations in demand and supply with high precision, which traditional static models are unable to do. By comprehensively modeling the unique operational characteristics and interdependencies of the electricity, gas, and heating networks, the framework ensures that the integrated system operates efficiently, remains stable under varying loads, and meets regulatory compliance for emissions. A synthesized case study simulating the operation of an integrated MES—including the IEEE 123-bus system for electricity, a modeled Belgian high-caloric gas network, and a Danish district heating system—illustrates the effectiveness of the proposed model. The study results indicate significant improvements in operational efficiency, reductions in emissions, and enhanced system stability. Key contributions of this paper include the development of a multi-layered optimization framework that addresses the dynamics of MESs, integration of environmental and regulatory compliance within the operational strategy, and a robust validation of the LSTM-based model against simulated anomalies and real-world scenarios. Full article

With China’s socio-economic growth, the demand for enhanced residential comfort in northern urban areas has surged. Traditional district heating systems often fail to meet modern users’ diverse needs, leading to inefficiencies and significant heat loss. This paper investigates optimization and transformation methods for demand-side-oriented heating systems. We propose key design parameters that facilitate a shift from source-end to demand-end dominance and develop a bi-level planning model for operational scheduling. The model integrates building thermal storage and adjustable user temperature ranges to optimize multi-thermal source systems. Key contributions include identifying critical renovation parameters and establishing the relationship between temperature control range and system capacity. Results demonstrate that the optimized system provides interval temperature control for 96.02% of the heating season and increases the full-load duration ratio of heat source equipment by 29.54% compared to traditional systems. These improvements enhance operational efficiency, reduce heat loss, and better align heating provision with users’ dynamic thermal demands. This research offers a robust theoretical foundation and practical guidelines for transitioning to demand-end dominated district heating systems, contributing to more sustainable and responsive heating solutions. Full article

This research aims to assess the socio-economic and environmental impacts of the Lithuanian long-term renovation strategy, focusing on improvements in the energy performance of renovated multi-apartment buildings in the country. The methodology used in the study is centred on the CleanProd general equilibrium model, which is based on publicly available data from the FIGARO database and Eurostat’s non-financial transaction statistics. The four renovation financing scenarios analysed are represented in the model by changes in the demand for energy resources and construction and other transactions related to the renovation programme. To reflect the dynamic nature of the renovation programme, counterfactual equilibria are sought for each year of the renovation programme. The results revealed that renovation of multi-apartment buildings brings net benefits, including long-term increases in gross domestic products (GDPs) and employment, as well as a decrease in economy-wide greenhouse gas (GHG) emissions, and is aligned with the binding European Union’s energy efficiency target to reduce energy consumption at least by 11.7% in 2030 (in comparison to 2020). The Increase in Taxes on Products scenario is modelled as the most favourable scenario. It assures annual GDP growth by 0.37%, employment growth by 2170 jobs a year, including 606 jobs for young people, and an annual decrease in GHG emissions by 929–1043 ktCO_2eq. It is found that the most considerable benefits are received during the renovation of medium-size buildings when construction demand increases by EUR 600,000–800,000 per year and natural gas and district heating demand are reduced by EUR 59,000–187,000 per year. Other scenarios demonstrating different building renovation and energy efficiency support practices, including Costless, Reallocation of Governmental Expenditure, and Governmental Loan, show relevant but slightly lower benefits. Full article