Energy Reports最新文献

{"title":"Study on coordinated control strategy for auxiliary power units in range-extended electric vehicles","authors":"Ye Yang ,&nbsp;Haoqing Sun ,&nbsp;Shan Jiang ,&nbsp;Jingyi Tian ,&nbsp;Qiang Ai","doi":"10.1016/j.egyr.2024.12.068","DOIUrl":"10.1016/j.egyr.2024.12.068","url":null,"abstract":"<div><div>This paper presents a novel fuzzy adaptive PI coordination control strategy for the Auxiliary Power Unit (APU) in range-extended electric vehicles (EREVs) to enhance dynamic and steady-state performance during power tracking. An improved genetic algorithm is utilized to optimize the PI controller's parameters, focusing on faster convergence. A fuzzy reasoning algorithm is then employed to dynamically adjust the APU's control parameters, achieving adaptive control during power tracking. Experimental verification is conducted through bench tests, where dynamic response characteristics and power change rates of the APU are evaluated. Comparative simulations with traditional control methods demonstrate that the proposed strategy significantly improves the APU's dynamic response, offering better real-time adaptability and steady-state accuracy, effectively addressing challenges posed by power variations.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"13 ","pages":"Pages 865-874"},"PeriodicalIF":4.7,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143154206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimising flywheel energy storage systems for enhanced windage loss reduction and heat transfer: A computational fluid dynamics and ANOVA-based approach","authors":"Mahmoud Eltaweel ,&nbsp;Noha A. Mostafa ,&nbsp;Christos Kalyvas ,&nbsp;Yong Chen ,&nbsp;Mohammad Reza Herfatmanesh","doi":"10.1016/j.egyr.2024.12.048","DOIUrl":"10.1016/j.egyr.2024.12.048","url":null,"abstract":"<div><div>Concerns about global warming and the need to reduce carbon emissions have prompted the creation of novel energy recovery systems. Continuous braking results in significant energy loss during urban driving. Flywheel energy storage systems (FESS) can recover and store vehicle kinetic energy during deceleration. In this work, Computational Fluid Dynamics (CFD) simulations have been carried out using the Analysis of Variance (ANOVA) technique to determine the effects of design parameters on flywheel windage losses and heat transfer characteristics. The influence of five parameters was studied: flywheel operating speed, radial airgap size, axial airgap size, rotor surface roughness and housing surface roughness. Two models were developed to assess the significance and effects of the studied parameters on windage losses and Nusselt number to determine the most optimal conditions. The significance and dependency of these parameters are investigated using the ANOVA technique. The ANOVA interaction analysis showed that all the studied parameters interact significantly. The results indicate that optimising the radial and axial airgap sizes led to a significant 19 % reduction in windage losses, while increasing the radial airgap significantly enhanced the Nusselt number by 33 %, thereby improving convective heat transfer. The study also found that increasing rotor and housing surface roughness improved heat dissipation, as observed by up to a 2.7 % increase in the Nusselt number. It was concluded that optimal configurations of radial radius ratio and axial radius ratio, in combination with targeted surface roughness, can lower rotor surface temperatures, reducing energy loss from frictional heating and enhancing the system’s energy efficiency. The findings of this study can be used to develop guidelines for the design optimisation of FESS.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"13 ","pages":"Pages 834-855"},"PeriodicalIF":4.7,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143154205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing residential energy and water solutions in Riyadh, Saudi Arabia: A cogeneration system with dynamic load management for optimized electricity and water production","authors":"Fahad Awjah Almehmadi ,&nbsp;Abdullah Najib ,&nbsp;Hany Al-Ansary","doi":"10.1016/j.egyr.2024.12.032","DOIUrl":"10.1016/j.egyr.2024.12.032","url":null,"abstract":"<div><div>This paper introduces a novel cogeneration power plant system coupled with Direct Contact Membrane Distillation (DCMD), specifically designed to meet the energy and water needs of residential complexes that include 18 apartments. Integrating solar collectors, a turbine, a condenser, a mixing chamber, a process heater, and a control valve, the proposed system optimizes energy distribution and load management. It is designed to utilize excess steam for DCMD processes when electrical production exceeds residential demand and harnesses waste heat from the process heater to enhance brackish water distillation efficiency. This innovative approach results in the production of 25,140 m³ /year from DCMD, demonstrating a highly efficient method of converting waste heat into a valuable resource for desalination. Capable of supplying 409 MW annually, this system covers 36 % of the total energy demand for residential complexes. Thus, it addresses a significant share of energy and water needs for 18 apartments and offers a scalable, efficient approach to meld sustainable energy production with residential living requirements, marking a significant stride in sustainable water and energy production for residential buildings.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"13 ","pages":"Pages 824-833"},"PeriodicalIF":4.7,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143153619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust resampling and stacked learning models for electricity theft detection in smart grid","authors":"Ashraf Ullah ,&nbsp;Inam Ullah Khan ,&nbsp;Muhammad Zeeshan Younas ,&nbsp;Maqbool Ahmad ,&nbsp;Natalia Kryvinska","doi":"10.1016/j.egyr.2024.12.041","DOIUrl":"10.1016/j.egyr.2024.12.041","url":null,"abstract":"<div><div>Electricity theft (ET) is a critical contributor to non-technical losses (NTLs) that significantly threaten the efficiency and reliability of power grids, leading to increased power wastage and financial losses. Despite the development of various artificial intelligence (AI)-based machine learning (ML) and deep learning (DL) approaches for electricity theft detection (ETD), existing methods often exhibit limitations in memorization and generalization, mainly when applied to large-scale electricity consumption datasets characterized by high variance, missing values, and complex nonlinear relationships. These challenges can result in models needing high variance and bias, reducing their effectiveness in accurately predicting electricity theft cases. To address these limitations, we propose a three-layer framework that employs a stacking ensemble model to combine the benefits of both ML and DL algorithms. During the first stage of data preprocessing, missing data is imputed through data interpolation, while the normalization is done through min–max scaling. To solve the high-class imbalance problem prevalent in most real-world datasets, we combine borderline synthetic minority oversampling techniques and near-miss undersampling strategies. In the final layer of our proposed ETD framework, we employ four ML base and five meta-classifiers. The outputs of base classifiers are aggregated and passed to a meta-classifier, where we evaluate recurrent neural networks (RNN) and convolutional neural network (CNN) as potential meta-classifiers. The RNN are long short-term memory (LSTM), gated recurrent unit (GRU), Bi-directional LSTM (Bi-LSTM) and Bi-directional GRU (Bi-GRU), respectively. Experimental outcomes show that the proposed Bi-GRU better achieves accuracy enhancement of detection in general than meta-classifiers and other state-of-the-art models used for ETD.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"13 ","pages":"Pages 770-779"},"PeriodicalIF":4.7,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143154204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Seasonal and intraday effects on spectral mismatch corrections for photovoltaic performance modelling in the United Kingdom","authors":"Rajiv Daxini,&nbsp;Robin Wilson,&nbsp;Yupeng Wu","doi":"10.1016/j.egyr.2024.11.086","DOIUrl":"10.1016/j.egyr.2024.11.086","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Modelling photovoltaic (PV) performance is essential for improving system design and operation. Current models to account for the spectral influence on PV performance (spectral correction functions, SCFs) are typically developed and validated on annual or multi-year timescales. Through an empirical analysis of short-term (monthly and intraday) meteorological and PV performance data, this work shows that there is significant variation in the accuracy of different methods to characterise the prevailing spectral irradiance conditions that are adopted by published SCFs. Compared with the use of weather– and system-specific models, a one-size-fits-all approach to model selection may result in an order of magnitude increase in the model residual sum of squares (RSS). One of the reasons for these inaccuracies includes the fact that model performance depends on the prevailing weather conditions. A model that performs well under clearsky conditions can suffer from reduced accuracy in “dynamic sky” conditions, as characterised by fast-changing partial cloud cover. Four SCFs are studied in this paper, namely an air mass model, &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;f&lt;/mi&gt;&lt;mrow&gt;&lt;mo&gt;(&lt;/mo&gt;&lt;mi&gt;A&lt;/mi&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;M&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;a&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;mo&gt;)&lt;/mo&gt;&lt;/mrow&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;, average photon energy model, &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;f&lt;/mi&gt;&lt;mrow&gt;&lt;mo&gt;(&lt;/mo&gt;&lt;mi&gt;φ&lt;/mi&gt;&lt;mo&gt;)&lt;/mo&gt;&lt;/mrow&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;, air mass and clearsky index model, &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;f&lt;/mi&gt;&lt;mrow&gt;&lt;mo&gt;(&lt;/mo&gt;&lt;mi&gt;A&lt;/mi&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;M&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;a&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;mo&gt;,&lt;/mo&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;k&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;c&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;mo&gt;)&lt;/mo&gt;&lt;/mrow&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;, and an average photon energy and spectral band depth model, &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;f&lt;/mi&gt;&lt;mrow&gt;&lt;mo&gt;(&lt;/mo&gt;&lt;mi&gt;φ&lt;/mi&gt;&lt;mo&gt;,&lt;/mo&gt;&lt;mi&gt;ɛ&lt;/mi&gt;&lt;mo&gt;)&lt;/mo&gt;&lt;/mrow&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;. The two single-variable models (air mass spectral correction, &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;f&lt;/mi&gt;&lt;mrow&gt;&lt;mo&gt;(&lt;/mo&gt;&lt;mi&gt;A&lt;/mi&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;M&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;a&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;mo&gt;)&lt;/mo&gt;&lt;/mrow&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;, and the average photon energy spectral correction, &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;f&lt;/mi&gt;&lt;mrow&gt;&lt;mo&gt;(&lt;/mo&gt;&lt;mi&gt;φ&lt;/mi&gt;&lt;mo&gt;)&lt;/mo&gt;&lt;/mrow&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;) are shown to be unreliable across the seasons, with reduced performance in summer and under dynamic sky conditions. Furthermore, they exhibit systematic time-of-day errors, even under clear skies, resulting in part from the non-bijective relationships between the spectrum and the independent variables (&lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;A&lt;/mi&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;M&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;a&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; and &lt;span&gt;&lt;math&gt;&lt;mi&gt;φ&lt;/mi&gt;&lt;/math&gt;&lt;/span&gt;). On the other hand, the multivariable approaches (air mass and clearsky index, &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;f&lt;/mi&gt;&lt;mrow&gt;&lt;mo&gt;(&lt;/mo&gt;&lt;mi&gt;A&lt;/mi&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;M&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;a&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;mo&gt;,&lt;/mo&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;k&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;c&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;mo&gt;)&lt;/mo&gt;&lt;/mrow&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;, and average photon energy and the","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"13 ","pages":"Pages 759-769"},"PeriodicalIF":4.7,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143154261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Kolmogorov–Arnold recurrent network for short term load forecasting across diverse consumers","authors":"Muhammad Umair Danish,&nbsp;Katarina Grolinger","doi":"10.1016/j.egyr.2024.12.038","DOIUrl":"10.1016/j.egyr.2024.12.038","url":null,"abstract":"<div><div>Load forecasting plays a crucial role in energy management, directly impacting grid stability, operational efficiency, cost reduction, and environmental sustainability. Traditional Vanilla Recurrent Neural Networks (RNNs) face issues such as vanishing and exploding gradients, whereas sophisticated RNNs such as Long Short-Term Memory Networks (LSTMs) have shown considerable success in this domain. However, these models often struggle to accurately capture complex and sudden variations in energy consumption, and their applicability is typically limited to specific consumer types, such as offices or schools. To address these challenges, this paper proposes the Kolmogorov–Arnold Recurrent Network (KARN), a novel load forecasting approach that combines the flexibility of Kolmogorov–Arnold Networks with RNN’s temporal modeling capabilities. KARN utilizes learnable temporal spline functions and edge-based activations to better model non-linear relationships in load data, making it adaptable across a diverse range of consumer types. The proposed KARN model was rigorously evaluated on a variety of real-world datasets, including student residences, detached homes, a home with electric vehicle charging, a townhouse, and industrial buildings. Across all these consumer categories, KARN consistently outperformed traditional Vanilla RNNs, while it surpassed LSTM and Gated Recurrent Units (GRUs) in six buildings. The results demonstrate KARN’s superior accuracy and applicability, making it a promising tool for enhancing load forecasting in diverse energy management scenarios.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"13 ","pages":"Pages 713-727"},"PeriodicalIF":4.7,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143154260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel adaptive controller for one-stage PV power plants considering PLL dynamic performance","authors":"R. Henríquez ,&nbsp;C. Rahmann ,&nbsp;J. Vega-Herrera ,&nbsp;V. Vittal ,&nbsp;B. Vega","doi":"10.1016/j.egyr.2024.12.017","DOIUrl":"10.1016/j.egyr.2024.12.017","url":null,"abstract":"<div><div>The ongoing transition from power systems dominated by synchronous machines to systems based on renewable energy sources (RES) is pushing a change from conventional robust power systems towards weak low-inertia systems. Stability problems can manifest in several ways in these systems. Problems such as small-signal, voltage, and converter-driven instability are more likely to arise in weak networks. Recent experience has shown that the phase-lock-loop (PLL) control loop is among the key instability drivers in RES power plants. Moreover, the dynamic performance of RES and their interaction with the grid during faults strongly depend on system strength at the connection point and the control strategy implemented. In this context, this paper contributes by proposing a novel adaptive controller that considers the dynamic performance of the PLL and the level of network strength, along with a methodology for systematically tuning its control parameters. The controller is specially designed for photovoltaic (PV) power plants to ensure that these units can successfully ride through grid faults even when connected to weak networks. The stability of the system under all operating conditions is achieved by considering the level of network strength endogenously in the tuning process. The proposed Control and methodology are validated through transient simulations using the 9-bus IEEE test system.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"13 ","pages":"Pages 670-679"},"PeriodicalIF":4.7,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143154259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantification of the impact of irradiance, heat, humidity, and cyclic temperature on the aging of photovoltaic panels: a case study in Algeria","authors":"Meriem Farou ,&nbsp;Abdelhak Djellad ,&nbsp;Sofiane Chiheb ,&nbsp;Hala Lalaymia ,&nbsp;Badri Rekik ,&nbsp;Pierre-Olivier Logerais","doi":"10.1016/j.egyr.2024.12.025","DOIUrl":"10.1016/j.egyr.2024.12.025","url":null,"abstract":"<div><div>Photovoltaic (PV) aging refers to the inevitable decline in the efficiency of solar modules over time due to various environmental factors. The main elements contributing to this degradation include irradiance, heat, humidity, and cyclic temperature. This paper details the accelerated factors (AFs) calculated from a series of developed equations established to quantify the deterioration mechanisms affecting PV panels. These equations are derived from several models: the Arrhenius model for temperature and irradiance, the Eyring and Peck models for humidity, and the Coffin-Manson model for cyclic temperature. After formulating equations that measure the combined effects of temperature, irradiance, humidity, and cyclic temperature, these equations were employed to analyze the deterioration of the PV panels installed at the Oued El Keberit solar plant in Souk Ahras, Algeria. The investigation revealed that humidity significantly affected the panels during the winter season. In spring, both humidity and irradiance become important factors. During the summer, temperature greatly influences degradation, while lower humidity levels do not significantly affect the panels. In autumn, humidity continues to be a critical factor. According to the obtained results, the highest AF values occur during the summer months, while the lowest AF values are observed in winter. As a result, the PV panels would deteriorate more noticeably during the winter season than in the summer time.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"13 ","pages":"Pages 642-652"},"PeriodicalIF":4.7,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143154258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of indoor/outdoor environment and dynamic clothing insulation-based thermal comfort prediction model using artificial neural network","authors":"Chul Ho Kim ,&nbsp;Sang Hun Yeon ,&nbsp;Kwang Ho Lee","doi":"10.1016/j.egyr.2024.12.030","DOIUrl":"10.1016/j.egyr.2024.12.030","url":null,"abstract":"<div><div>This study presents the development of a predicted mean vote (PMV) prediction model based on an artificial neural network (ANN), utilizing dynamic clothing insulation calculated via a linear regression model and easily measurable indoor and outdoor environmental factors. Additionally, the cooling and heating performance of an air-cooled variable refrigerant flow (VRF) heat pump system was modeled under various load conditions. The validity of the building model for PMV prediction was established by comparing simulation outcomes with actual building power consumption. Four scenarios were designed by varying the combinations of input variables required for PMV prediction, and each scenario's performance was evaluated. Among these, Scenario 3, which only considered dynamic clothing volume alongside simple indoor and outdoor variables, demonstrated improved predictive accuracy compared to the more comprehensive Scenario 1. Furthermore, Scenario 4, which included CO₂ concentration as an additional variable, exhibited the best prediction performance. The model effectively achieved high thermal comfort prediction across different American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) climate zones, showing an average coefficient of variation of the root mean square error (CVRMSE) of 7.83 %, 5.16 %, and 6.78 %, and a standard deviation percentage error of 4.34 %, 4.94 %, and 3.16 %, respectively. The results indicate that the developed model not only aligns well with the predicted PMV distribution and mean values but also captures the variability observed in real-world measurements. This demonstrates the model’s capability to accurately forecast PMV using readily measurable environmental factors through ANN.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"13 ","pages":"Pages 622-641"},"PeriodicalIF":4.7,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143154257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Experimental investigations of the operating behavior of a low-flow drainback solar heating system","authors":"Kassa W. Liyew ,&nbsp;Yoann Louvet ,&nbsp;Nigus G. Habtu ,&nbsp;Ulrike Jordan","doi":"10.1016/j.egyr.2024.12.031","DOIUrl":"10.1016/j.egyr.2024.12.031","url":null,"abstract":"<div><div>Solar heating plants operated as drainback systems (DBS) have proven potential for cost reduction and protection against system overheating and freezing. By selecting a lower flow rate in the collector loop, investment and operation costs can be further reduced. However, the impact of low-flow operation on hydraulic and thermal performance and the associated aspects during planning needs better understanding. Therefore, the phenomena of water/air two-phase flow characteristics under different flow conditions, planning and installation issues, improvements for low-flow operation, and associated malfunctions of a DBS with heat pipe evacuated tube collector are investigated. The low-flow operation of DBS with a concentric manifold configuration creates unforeseen problems of flow break and reduced collector performance. The air that penetrates the system and accumulates in the collector manifold creates these problems. The study indicates that, after removing the air, the collector performance outweighs the reduction in performance induced by lowering the specific flow rate from 40.8 l/(h∙m²) to 11.5 l/(h∙m²) for reduced temperature differences above 0.04 K∙m²/W. Concerning the design of the collector manifold, an eccentric reducer with 0.2° inclination performs better than the concentric reducers do at low-flow operation. The air entering the collector loop is well pushed out of the manifold.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"13 ","pages":"Pages 594-608"},"PeriodicalIF":4.7,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143154203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}