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Search Results (165)

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Keywords = actual operational heating data

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19 pages, 4741 KiB  
Article
A Day-Ahead PV Power Forecasting Method Based on Irradiance Correction and Weather Mode Reliability Decision
by Haonan Dai, Yumo Zhang and Fei Wang
Energies 2025, 18(11), 2809; https://doi.org/10.3390/en18112809 - 28 May 2025
Viewed by 62
Abstract
Accurate day-ahead photovoltaics (PV) power forecasting results are significant for power grid operation. According to different weather modes, the existing research has established a classification forecast framework to improve the accuracy of day-ahead forecasts. However, the existing framework still has the following two [...] Read more.
Accurate day-ahead photovoltaics (PV) power forecasting results are significant for power grid operation. According to different weather modes, the existing research has established a classification forecast framework to improve the accuracy of day-ahead forecasts. However, the existing framework still has the following two problems: (1) weather mode prediction and power forecasting are highly dependent on the accuracy of numerical weather prediction (NWP), but the existing classification forecasting framework ignores the impact from NWP errors; (2) the validity of the classification forecasting framework comes from the accurate prediction of weather modes, but the existing framework lacks the analysis and decision-making mechanism of the reliability of weather mode prediction results, which will lead to a significant decline in the overall accuracy when weather modes are wrongly predicted. Therefore, this paper proposes a day-ahead PV power forecasting method based on irradiance correction and weather mode reliability decision. Firstly, based on the measured irradiance, K-means clustering method is used to obtain the daily actual weather mode labels; secondly, considering the coupling relationship of meteorological elements, the graph convolutional network (GCN) model is used to correct the predicted irradiance by using multiple meteorological elements of NWP data; thirdly, the weather mode label is converted into one-heat code, and a weather mode reliability prediction model based on a convolutional neural network (CNN) is constructed, and then the prediction strategy of the day to be forecasted is decided; finally, based on the weather mode reliability prediction results, transformer model are established for unreliable weather and credible weather respectively. The simulation results of the ablation experiments show that classification prediction is an effective strategy to improve the forecasting accuracy of day-ahead PV output, which can be further improved by adding irradiance correction and weather mode reliability prediction modules. Full article
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33 pages, 3466 KiB  
Article
Exergy Analysis of 500 MW Power Unit Based on Direct Measurement Data
by Michalina Kurkus-Gruszecka, Łukasz Szabłowski, Olaf Dybiński, Piotr Krawczyk, Krzysztof Badyda and Grzegorz Kotte
Energies 2025, 18(11), 2762; https://doi.org/10.3390/en18112762 - 26 May 2025
Viewed by 115
Abstract
This paper presents an exergy analysis of a 500 MW unit based on actual measurement data. The mathematical model of the system was built in the Aspen HYSYS 2.4 software. The analysis was carried out for two operating states of the unit, at [...] Read more.
This paper presents an exergy analysis of a 500 MW unit based on actual measurement data. The mathematical model of the system was built in the Aspen HYSYS 2.4 software. The analysis was carried out for two operating states of the unit, at nominal load and at minimum technical load, based on data from two measurement campaigns carried out specifically for this study. The use of measurement data allows an accurate representation of the unit’s current operating conditions, which is crucial for the accuracy of the analysis and the practical implementation of the results obtained. The results show that the dominant sources of exergy losses are the irreversibilities associated with combustion and boiler heat transfer, which account for more than 60% of total exergy losses. The article makes an important contribution to sustainability by identifying opportunities to increase the operating efficiency of the power unit and reduce CO2 emissions. Proposed technical modifications, such as the modernisation of air heaters, the use of inverters in ventilation systems, or the optimisation of heat exchangers in the turbine system, can significantly improve energy efficiency and reduce the unit’s environmental impact. The analysis provides a valuable resource for the development of energy technologies that promote efficiency and sustainable resource use. Full article
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41 pages, 4883 KiB  
Article
A Hybrid Approach Combining Scenario Deduction and Type-2 Fuzzy Set-Based Bayesian Network for Failure Risk Assessment in Solar Tower Power Plants
by Tao Li, Wei Wu, Xiufeng Li, Yongquan Li, Xueru Gong, Shuai Zhang, Ruijiao Ma, Xiaowei Liu and Meng Zou
Sustainability 2025, 17(11), 4774; https://doi.org/10.3390/su17114774 - 22 May 2025
Viewed by 215
Abstract
Under extreme operating conditions such as high temperatures, strong corrosion, and cyclic thermal shocks, key equipment in solar tower power plants (STPPs) is prone to severe accidents and results in significant losses. To systematically quantify potential failure risks and address the methodological gaps [...] Read more.
Under extreme operating conditions such as high temperatures, strong corrosion, and cyclic thermal shocks, key equipment in solar tower power plants (STPPs) is prone to severe accidents and results in significant losses. To systematically quantify potential failure risks and address the methodological gaps in existing research, this study proposes a risk assessment framework combining a novel scenario propagation model covering triggering factors, precursor events, accident scenarios, and response measures with an interval type-2 fuzzy set (IT2FS) Bayesian network. This framework establishes equipment failure evolution pathways and consequence evaluation criteria. To address data scarcity, the methodology integrates actual case data and expert elicitation to obtain assessment parameters. Specifically, an IT2FS-based similarity aggregation method quantifies expert opinions for prior probability estimation. Additionally, to reduce computational complexity and enhance reliability in conditional probability acquisition, the IT2FS-integrated best–worst method evaluates the relative importance of parent nodes, combined with a leakage-weighted summation algorithm to generate conditional probability tables. The model was applied to a western Chinese STPP and the results show the probabilities of receiver blockage, pipeline blockage, tank leakage, and heat exchanger blockage are 0.061, 0.059, 0.04, and 0.08, respectively. Under normal operating conditions, the occurrence rates of level II accident consequences for all four equipment types remain below 6%, with response measures demonstrating significant suppression effects on accidents. The research results provide critical decision-making support for risk management and mitigation strategies in STPPs. Full article
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27 pages, 22320 KiB  
Article
A Real-World Case Study Towards Net Zero: EV Charger and Heat Pump Integration in End-User Residential Distribution Networks
by Thet Paing Tun, Oguzhan Ceylan and Ioana Pisica
Energies 2025, 18(10), 2510; https://doi.org/10.3390/en18102510 - 13 May 2025
Viewed by 204
Abstract
The electrification of energy systems is essential for carbon reduction and sustainable energy goals. However, current network asset ratings and the poor thermal efficiency of older buildings pose significant challenges. This study evaluates the impact of heat pump and electric vehicle (EV) penetration [...] Read more.
The electrification of energy systems is essential for carbon reduction and sustainable energy goals. However, current network asset ratings and the poor thermal efficiency of older buildings pose significant challenges. This study evaluates the impact of heat pump and electric vehicle (EV) penetration on a UK residential distribution network, considering the highest coincident electricity demand and worst weather conditions recorded over the past decade. The power flow calculation, based on Python, is performed using the pandapower library, leveraging the actual distribution network structure of the Hillingdon area by incorporating recent smart meter data from a distribution system operator alongside historical weather data from the past decade. Based on the outcome of power flow calculation, the transformer loadings and voltage levels were assessed for existing and projected heat pump and EV adoption rates, in line with national policy targets. Findings highlight that varied consumer density and diverse usage patterns significantly influence upgrade requirements. Full article
(This article belongs to the Special Issue The Networked Control and Optimization of the Smart Grid)
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25 pages, 2082 KiB  
Article
Optimizing Space Heating in Buildings: A Deep Learning Approach for Energy Efficiency
by Fernando Almeida, Mauro Castelli, Nadine Corte-Real and Luca Manzoni
Energies 2025, 18(10), 2471; https://doi.org/10.3390/en18102471 - 12 May 2025
Viewed by 283
Abstract
Building energy management is crucial in reducing energy consumption and maintaining occupant comfort, especially in heating systems. However, achieving optimal space heating efficiency while maintaining consistent comfort presents significant challenges. Traditional methods often fail to balance energy consumption with thermal comfort, especially across [...] Read more.
Building energy management is crucial in reducing energy consumption and maintaining occupant comfort, especially in heating systems. However, achieving optimal space heating efficiency while maintaining consistent comfort presents significant challenges. Traditional methods often fail to balance energy consumption with thermal comfort, especially across multiple zones in buildings with varying operational demands. This study investigates the role of deep learning models in optimizing space heating while maintaining thermal comfort across multiple building zones. It aims to enhance heating efficiency by developing predictive models for building temperature and heating consumption, evaluating the effectiveness of different deep learning architectures, and analyzing the impact of model-driven heating optimization on energy savings and occupant comfort. To address this challenge, this study employs Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Transformer models to forecast area temperatures and predict space heating consumption. The proposed methodology leverages historical building temperature data, weather station measurements such as atmospheric pressure, wind speed, wind direction, relative humidity, and solar radiation, along with other weather parameters, to develop accurate and reliable predictions. A two-stage deep learning process is utilized: first, temperature predictions are generated for different building zones, and second, these predictions are used to estimate global heating consumption. This study also employs grid search and cross-validation to optimize the model configurations and custom loss functions to ensure energy efficiency and occupant comfort. Results demonstrate that the Long Short-Term Memory and Transformer models outperform the Gated Recurrent Unit regarding heating reduction, with a 20.95% and 20.69% decrease, respectively, compared to actual consumption. This study contributes significantly to energy management by providing a deep learning-driven framework that enhances energy efficiency while maintaining thermal comfort across different building areas, thereby supporting sustainable and intelligent building operations. Full article
(This article belongs to the Section F5: Artificial Intelligence and Smart Energy)
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25 pages, 2250 KiB  
Article
Simulation of Heat Pump with Heat Storage and PV System—Increase in Self-Consumption in a Polish Household
by Jakub Szymiczek, Krzysztof Szczotka and Piotr Michalak
Energies 2025, 18(9), 2325; https://doi.org/10.3390/en18092325 - 2 May 2025
Viewed by 452
Abstract
The use of renewables in heat production requires methods to overcome the issue of asynchronous heat load and energy production. The most effective method for analyzing the intricate thermal dynamics of an existing building is through transient simulation, utilizing real-world weather data. This [...] Read more.
The use of renewables in heat production requires methods to overcome the issue of asynchronous heat load and energy production. The most effective method for analyzing the intricate thermal dynamics of an existing building is through transient simulation, utilizing real-world weather data. This approach offers a far more nuanced understanding than static calculations, which often fail to capture the dynamic interplay of environmental factors and building performance. Transient simulations, by their nature, model the building’s thermal behavior over time, reflecting the continuous fluctuations in temperature, solar radiation, and wind speed. Leveraging actual meteorological data enables the simulation model to faithfully capture system dynamics under realistic operational scenarios. This is crucial for evaluating the effectiveness of heating, ventilation, and air conditioning (HVAC) systems, identifying potential energy inefficiencies, and assessing the impact of various energy-saving measures. The simulation can reveal how the building’s thermal mass absorbs and releases heat, how solar gains influence indoor temperatures, and how ventilation patterns affect heat losses. In this paper, a household heating system consisting of an air source heat pump, PV, and buffer tank is simulated and analyzed. The 3D model accurately represents the building’s geometry and thermal properties. This virtual representation serves as the basis for calculating heat losses and gains, considering factors such as insulation levels, window characteristics, and building orientation. The approach is based on the calculation of building heat load based on a 3D model and EN ISO 52016-1 standard. The heat load is modeled based on air temperature and sun irradiance. The heating system is modeled in EBSILON professional 16.00 software for the calculation of transient 10 min time step heat production during the heating season. The results prove that a buffer tank with the right heat production control system can efficiently increase the auto consumption of self-produced PV electric energy, leading to a reduction in environmental effects and higher economic profitability. Full article
(This article belongs to the Special Issue Advances in Refrigeration and Heat Pump Technologies)
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17 pages, 3087 KiB  
Article
Coordinated Scheduling and Operational Characterization of Electricity and District Heating Systems: A Case Study
by Peng Yu, Dianyang Li, Dai Cui, Jing Xu, Chengcheng Li and Huiqing Cao
Energies 2025, 18(9), 2211; https://doi.org/10.3390/en18092211 - 26 Apr 2025
Viewed by 290
Abstract
With the increasing penetration of renewable energy generation in energy systems, power and district heating systems (PHSs) continue to encounter challenges with wind and solar curtailment during scheduling. Further integration of renewable energy generation can be achieved by exploring the flexibility of existing [...] Read more.
With the increasing penetration of renewable energy generation in energy systems, power and district heating systems (PHSs) continue to encounter challenges with wind and solar curtailment during scheduling. Further integration of renewable energy generation can be achieved by exploring the flexibility of existing systems. Therefore, this study systematically explores the deep transfer modifications of a specific thermal power plant based in Liaoning, China, and the operational characteristics of the heating supply system of a particular heating company. In addition, the overall PHS operational performance is analyzed. The results indicate that both absorption heat pumps and solid-state electric thermal storage technologies effectively improve system load regulation capabilities. The temperature decrease in the water medium in the primary network was proportional to the pipeline distance. When the pipeline lengths were 1175 and 14,665 m, the temperature decreased by 0.66 and 3.48 °C, respectively. The heat exchanger effectiveness and logarithmic mean temperature difference (LMTD) were positively correlated with the outdoor temperature. When the outdoor temperature dropped to −18 °C, the heat exchanger efficiency decreased to 60%, and the LMTD decreased to 17.5 °C. The study findings provide practical data analysis support to address the balance between power supply and heating demand. Full article
(This article belongs to the Section J1: Heat and Mass Transfer)
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16 pages, 6120 KiB  
Article
Numerical Investigation of Heat Transfer Characteristics in an Industrial-Scale Continuous Annular Cooler for Iron Ore Sintering Process
by Jingxuan Xie, Liang Wang, Jiayu Pi, Hongyuan Wei, Leping Dang and Hui Li
Processes 2025, 13(4), 1185; https://doi.org/10.3390/pr13041185 - 14 Apr 2025
Viewed by 233
Abstract
CFD simulations of annular coolers have often been performed on a single trolley, making it difficult for the method to provide reliable and accurate data for the optimum design of annular coolers. The present paper establishes a three-dimensional model of the entire annular [...] Read more.
CFD simulations of annular coolers have often been performed on a single trolley, making it difficult for the method to provide reliable and accurate data for the optimum design of annular coolers. The present paper establishes a three-dimensional model of the entire annular cooler, uses sliding mesh to approach the actual working conditions, and through UDF, realizes the simulations of the continuous feeding process of the annular cooler, and obtains complete data for one run of the annular cooler. By comparing the simulated data with the actual measured data, the reliability of the model was verified. The temperature distribution inside the annular cooler and the temperature variation at the outlet of the waste heat recovery as well as the flow rate are also explored in detail. Subsequently, the temperature distribution inside the annular cooler, the flue gas flow, and the changes in temperature at each outlet were studied under different material layer thicknesses, and the discharge temperature under different thicknesses was obtained. Based upon the proposed method, a lot of data that cannot be obtained by traditional calculation methods can be obtained, thus shortening the cycle of optimizing the design and development of the structure and operating parameters of annular coolers. Full article
(This article belongs to the Section Chemical Processes and Systems)
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25 pages, 5804 KiB  
Article
Physical Model for the Simulation of an Air Handling Unit Employed in an Automotive Production Process: Calibration Procedure and Potential Energy Saving
by Luca Viscito, Francesco Pelella, Andrea Rega, Federico Magnea, Gerardo Maria Mauro, Alessandro Zanella, Alfonso William Mauro and Nicola Bianco
Energies 2025, 18(7), 1842; https://doi.org/10.3390/en18071842 - 5 Apr 2025
Viewed by 389
Abstract
A meticulous thermo-hygrometric control is essential for various industrial production processes, particularly those involving the painting phases of body-in-white, in which the air temperature and relative humidity in production boots must be limited in strict intervals to ensure the high quality of the [...] Read more.
A meticulous thermo-hygrometric control is essential for various industrial production processes, particularly those involving the painting phases of body-in-white, in which the air temperature and relative humidity in production boots must be limited in strict intervals to ensure the high quality of the final product. However, traditional proportional integrative derivative (PID) controllers may result in non-optimal control strategies, leading to energy wastage due to response delays and unnecessary superheatings. In this regard, predictive models designed for control can significantly aid in achieving all the targets set by the European Union. This paper focuses on the development of a predictive model for the energy consumption of an air handling unit (AHU) used in the paint-shop area of an automotive production process. The model, developed in MATLAB 2024b, is based on mass and energy balances within each component, and phenomenological equations for heat exchangers. It enables the evaluation of thermal powers and water mass flow rates required to process an inlet air flow rate to achieve a target condition for the temperature and relative humidity. The model was calibrated and validated using experimental data of a real case study of an automotive production process, obtaining mean errors of 16% and 31% for the hot and cold heat exchangers, respectively, in predicting the water mass flow rate. Additionally, a control logic based on six regulation thermo-hygrometric zones was developed, which depended on the external conditions of temperature and relative humidity. Finally, as the main outcome, several examples are provided to demonstrate both the applicability of the developed model and its potential in optimizing energy consumption, achieving energy savings of up to 46% compared to the actual baseline control strategy, and external boundary conditions, identifying an optimal trade-off between energy saving and operation feasibility. Full article
(This article belongs to the Section G: Energy and Buildings)
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22 pages, 5774 KiB  
Article
Research and Demonstration of Operation Optimization Method of Zero-Carbon Building’s Compound Energy System Based on Day-Ahead Planning and Intraday Rolling Optimization Algorithm
by Biao Qiao, Jiankai Dong, Wei Xu, Ji Li and Fei Lu
Buildings 2025, 15(5), 836; https://doi.org/10.3390/buildings15050836 - 6 Mar 2025
Viewed by 538
Abstract
The compound energy system is an important component of zero-carbon buildings. Due to the complex form of the system and the difficult-to-capture characteristics of thermo-electric coupling interactions, the operation control of the zero-carbon building’s energy system is difficult in practical engineering. Therefore, it [...] Read more.
The compound energy system is an important component of zero-carbon buildings. Due to the complex form of the system and the difficult-to-capture characteristics of thermo-electric coupling interactions, the operation control of the zero-carbon building’s energy system is difficult in practical engineering. Therefore, it is necessary to carry out relevant optimization methods. This paper investigated the current research status of the control and scheduling of compound energy systems in zero-carbon buildings at home and abroad, selected a typical zero-carbon building as the research object, analyzed its energy system’s operational data, and proposed an operation scheduling algorithm based on day-ahead flexible programming and intraday rolling optimization. The multi-energy flow control algorithm model was developed to optimize the operation strategy of heat pump, photovoltaic, and energy storage systems. Then, the paper applied the algorithm model to a typical zero-carbon building project, and verified the actual effect of the method through the actual operational data. After applying the method in this paper, the self-absorption rate of photovoltaic power generation in the building increased by 7.13%. The research results provide a theoretical model and data support for the operation control of the zero-carbon building’s compound energy system, and could promote the market application of the compound energy system. Full article
(This article belongs to the Special Issue Research on Solar Energy System and Storage for Sustainable Buildings)
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22 pages, 8515 KiB  
Article
Insulated Gate Bipolar Transistor Junction Temperature Estimation Technology for Traction Inverters Using a Thermal Model
by Kijung Kong, Junhwan Choi, Geonhyeong Park, Seungmin Baek, Sungeun Ju and Yongsu Han
Electronics 2025, 14(5), 999; https://doi.org/10.3390/electronics14050999 - 1 Mar 2025
Viewed by 724
Abstract
This study proposes a method for estimating the junction temperature of power semiconductors, particularly IGBTs (Insulated Gate Bipolar Transistors) and diodes. Traditional temperature measurement methods using NTC (Negative Temperature Coefficient) sensors have limitations in reflecting dynamic conditions in real time, as temperature changes [...] Read more.
This study proposes a method for estimating the junction temperature of power semiconductors, particularly IGBTs (Insulated Gate Bipolar Transistors) and diodes. Traditional temperature measurement methods using NTC (Negative Temperature Coefficient) sensors have limitations in reflecting dynamic conditions in real time, as temperature changes take time to reach the sensors. To address this, this study proposes a junction temperature estimation method using RC curve fitting and a thermal impedance model. This model represents the thermal behavior of IGBTs and diodes using a Foster thermal network that considers the resistance and capacitance of the heat transfer path. In particular, transient temperature estimation considering thermal coupling enables the prediction of temperature changes in IGBTs and diodes. To verify the proposed temperature estimation method, experiments were conducted to build the model based on data measured with an infrared thermal camera and NTC sensors. The model’s estimated results were compared with actual values across 25 operating regions, achieving a maximum MAE (Mean Absolute Error) of 2.26 °C. A comparative analysis of first-, second-, third-, and fourth-order Foster networks revealed that, while higher orders improve accuracy, gains beyond the second order are minimal relative to computational demands. This study contributes to enhancing not only the reliability of power semiconductor modules but also minimizing the temperature margin for inverters by estimating the junction temperature with better dynamic performance than that achieved by NTC sensors. Full article
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14 pages, 2202 KiB  
Article
Fault Diagnosis of Wire Disconnection in Heater Control System Using One-Dimensional Convolutional Neural Network
by Jiawei Guo, Linfeng Sun, Takahiro Kawaguchi and Seiji Hashimoto
Processes 2025, 13(2), 402; https://doi.org/10.3390/pr13020402 - 3 Feb 2025
Viewed by 986
Abstract
Heaters are critical components in various heating control systems, and their faults are often a primary cause of system failure, drawing significant attention from engineers and researchers. Early and accurate fault diagnosis is crucial to prevent cascading failures. Many diagnostic methods target faults [...] Read more.
Heaters are critical components in various heating control systems, and their faults are often a primary cause of system failure, drawing significant attention from engineers and researchers. Early and accurate fault diagnosis is crucial to prevent cascading failures. Many diagnostic methods target faults under generally stable and simple operating conditions, such as constant load or steady-state temperature. However, real-world scenarios are often complex and variable, involving dynamic loads, nonlinear temperature rises, and other challenges, which limit diagnostic accuracy. To address this issue, this paper proposes an intelligent fault diagnosis model based on a one-dimensional convolutional neural network (CNN), using the heater’s current and voltage as the input to the neural network. The effectiveness and accuracy of the proposed model were validated through experimental data under two different conditions, achieving an average accuracy rate of 98%. The disconnection faults were generated during actual operation and occurred in the early stages, differing significantly from artificially simulated faults, thereby increasing the difficulty of accurate diagnosis. Analysis and comparison of the experimental results demonstrate the feasibility of the intelligent diagnostic model and its high diagnostic accuracy. Full article
(This article belongs to the Special Issue Research on Intelligent Fault Diagnosis Based on Neural Network)
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32 pages, 6487 KiB  
Article
FS-DDPG: Optimal Control of a Fan Coil Unit System Based on Safe Reinforcement Learning
by Chenyang Li, Qiming Fu, Jianping Chen, You Lu, Yunzhe Wang and Hongjie Wu
Buildings 2025, 15(2), 226; https://doi.org/10.3390/buildings15020226 - 14 Jan 2025
Viewed by 841
Abstract
To optimize the control of fan coil unit (FCU) systems under model-free conditions, researchers have integrated reinforcement learning (RL) into the control processes of system pumps and fans. However, traditional RL methods can lead to significant fluctuations in the flow of pumps and [...] Read more.
To optimize the control of fan coil unit (FCU) systems under model-free conditions, researchers have integrated reinforcement learning (RL) into the control processes of system pumps and fans. However, traditional RL methods can lead to significant fluctuations in the flow of pumps and fans, posing a safety risk. To address this issue, we propose a novel FCU control method, Fluctuation Suppression–Deep Deterministic Policy Gradient (FS-DDPG). The key innovation lies in applying a constrained Markov decision process to model the FCU control problem, where a penalty term for process constraints is incorporated into the reward function, and constraint tightening is introduced to limit the action space. In addition, to validate the performance of the proposed method, we established a variable operating conditions FCU simulation platform based on the parameters of the actual FCU system and ten years of historical weather data. The platform’s correctness and effectiveness were verified from three aspects: heat transfer, the air side and the water side, under different dry and wet operating conditions. The experimental results show that compared with DDPG, FS-DDPG avoids 98.20% of the pump flow and 95.82% of the fan flow fluctuations, ensuring the safety of the equipment. Compared with DDPG and RBC, FS-DDPG achieves 11.9% and 51.76% energy saving rates, respectively, and also shows better performance in terms of operational performance and satisfaction. In the future, we will further improve the scalability and apply the method to more complex FCU systems in variable environments. Full article
(This article belongs to the Section Building Energy, Physics, Environment, and Systems)
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39 pages, 7824 KiB  
Article
The Building Energy Performance Gap in Multifamily Buildings: A Detailed Case Study Analysis of the Energy Demand and Collective Heating System
by Stijn Van de Putte, Marijke Steeman and Arnold Janssens
Sustainability 2025, 17(1), 252; https://doi.org/10.3390/su17010252 - 1 Jan 2025
Viewed by 1250
Abstract
The building energy performance gap, resulting from a discrepancy between the actual energy use and theoretical calculations, remains a persistent issue in building design. This study examines the energy performance of three multifamily buildings with a collective heating system powered by gas boilers [...] Read more.
The building energy performance gap, resulting from a discrepancy between the actual energy use and theoretical calculations, remains a persistent issue in building design. This study examines the energy performance of three multifamily buildings with a collective heating system powered by gas boilers and solar collectors: two that underwent deep renovation and one newly built. An extensive on-site monitoring system provides detailed data on both the heating demand and the final energy use. To ensure comparability, the total energy use of each unit is normalised using the energy signature method. The findings show the large spread of actual energy demands due to a wide variation in user profiles. The majority of dwellings have an actual energy use that is significantly higher than calculated, which is largely attributable to space heating. The gap is further exacerbated by substantial heat losses within the building’s heating system and by limited gains from the solar collectors, indicating discrepancies between design models and operational realities. To bridge this gap, there is a need for rigorous commissioning processes, at least during the initial operation phase start-up and ideally continuously. This can ensure more effective utilisation of renewable energy sources and reduce energy inefficiencies. Full article
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22 pages, 4723 KiB  
Article
Parameter Correction on Waste Heat Recovery System of a Gas Turbine Using Supercritical CO2 Based on Data Reconciliation
by Liang Xu, Jiarui You, Jiahua Wu, Yikang Liu and Di Zhang
Appl. Sci. 2025, 15(1), 248; https://doi.org/10.3390/app15010248 - 30 Dec 2024
Viewed by 658
Abstract
At present, the research significance of a waste heat recovery system of a gas turbine using CO2 is gradually becoming prominent, and accurate parameter measurement and performance monitoring are very necessary in the process of establishing coupled circulation system and actual operation. [...] Read more.
At present, the research significance of a waste heat recovery system of a gas turbine using CO2 is gradually becoming prominent, and accurate parameter measurement and performance monitoring are very necessary in the process of establishing coupled circulation system and actual operation. In this paper, a data reconciliation model is established based on the waste heat recovery system of a gas turbine using supercritical CO2 (S-CO2) cycle with two S-CO2 turbines, and several random errors and gross errors are added to verify the data reconciliation ability of the model. The calculation shows that the data reconciliation model established in this paper can obviously reduce the overall deviation level of each parameter in the thermal system, and can also identify and eliminate gross errors in the system. For some of the key parameters such as the total mass flow of CO2, data deviation is reduced to less than 1%, and high-precision power values of the equipment are calculated. This means that the measurement accuracy is effectively improved. In general, this paper makes a new attempt to use data reconciliation in parameter measurement and correction of a simple coupled cyclic system, and provides a certain reference for the subsequent application. Full article
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