Pub Date : 2024-02-28DOI: 10.1016/j.egyai.2024.100354
Kar Shen Tan , Chee Kiang Lam , Wee Choon Tan , Heap Sheng Ooi , Zi Hao Lim
The purpose of this study is to investigate the approaches applied to analyze solid oxide fuel cell (SOFC) microstructural properties. Both manual and automated image processing approaches applied on SOFC microstructural images which are obtained from several types of tomography such as dual-beam focused ion beam with scanning electron microscopy (FIB-SEM), Electron Backscatter Diffraction (EBSD) and others are discussed. In fact, to achieve a realistic and accurate SOFC microstructural properties, such as average diameter, volume fraction, triple phase boundary (TPB), area interface density and tortuosity factor, the approaches of image processing and quantification are crucial for a reliable image generation for quantification purposes. The microstructural properties are optimized to improve SOFC electrode performance. Therefore, the image processing and quantification approaches are outlined and reviewed. Despite the automated image processing and quantification algorithms significantly outperform manual image processing and quantification approaches in terms of computing speed when evaluating and measuring microstructural properties, the efficiency and productivity are still extremely taken into concern. As a result, image processing and quantification approaches are concluded and presented respectively in this paper.
{"title":"A review of image processing and quantification analysis for solid oxide fuel cell","authors":"Kar Shen Tan , Chee Kiang Lam , Wee Choon Tan , Heap Sheng Ooi , Zi Hao Lim","doi":"10.1016/j.egyai.2024.100354","DOIUrl":"https://doi.org/10.1016/j.egyai.2024.100354","url":null,"abstract":"<div><p>The purpose of this study is to investigate the approaches applied to analyze solid oxide fuel cell (SOFC) microstructural properties. Both manual and automated image processing approaches applied on SOFC microstructural images which are obtained from several types of tomography such as dual-beam focused ion beam with scanning electron microscopy (FIB-SEM), Electron Backscatter Diffraction (EBSD) and others are discussed. In fact, to achieve a realistic and accurate SOFC microstructural properties, such as average diameter, volume fraction, triple phase boundary (TPB), area interface density and tortuosity factor, the approaches of image processing and quantification are crucial for a reliable image generation for quantification purposes. The microstructural properties are optimized to improve SOFC electrode performance. Therefore, the image processing and quantification approaches are outlined and reviewed. Despite the automated image processing and quantification algorithms significantly outperform manual image processing and quantification approaches in terms of computing speed when evaluating and measuring microstructural properties, the efficiency and productivity are still extremely taken into concern. As a result, image processing and quantification approaches are concluded and presented respectively in this paper.</p></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S266654682400020X/pdfft?md5=f30e1e987a2fe67d45ec45e82cf2aea4&pid=1-s2.0-S266654682400020X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140041509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-27DOI: 10.1016/j.egyai.2024.100352
Haijun Ruan , Niall Kirkaldy , Gregory J. Offer , Billy Wu
Lithium-ion batteries with composite anodes of graphite and silicon are increasingly being used. However, their degradation pathways are complicated due to the blended nature of the electrodes, with graphite and silicon degrading at different rates. Here, we develop a deep learning health diagnostic framework to rapidly quantify and separate the different degradation rates of graphite and silicon in composite anodes using partial charging data. The convolutional neural network (CNN), trained with synthetic data, uses experimental partial charging data to diagnose electrode-level health of tested batteries, with errors of less than 3.1% (corresponding to the loss of active material reaching ∼75%). Sensitivity analysis of the capacity-voltage curve under different degradation modes is performed to provide a physically informed voltage window for diagnostics with partial charging data. By using the gradient-weighted class activation mapping approach, we provide explainable insights into how these CNNs work; highlighting regions of the voltage-curve to which they are most sensitive. Robustness is validated by introducing noise to the data, with no significant negative impact on the diagnostic accuracy for noise levels below 10 mV, thus highlighting the potential for deep learning approaches in the diagnostics of lithium-ion battery performance under real-world conditions. The framework presented here can be generalised to other cell formats and chemistries, providing robust and explainable battery diagnostics for both conventional single material electrodes, but also the more challenging composite electrodes.
{"title":"Diagnosing health in composite battery electrodes with explainable deep learning and partial charging data","authors":"Haijun Ruan , Niall Kirkaldy , Gregory J. Offer , Billy Wu","doi":"10.1016/j.egyai.2024.100352","DOIUrl":"https://doi.org/10.1016/j.egyai.2024.100352","url":null,"abstract":"<div><p>Lithium-ion batteries with composite anodes of graphite and silicon are increasingly being used. However, their degradation pathways are complicated due to the blended nature of the electrodes, with graphite and silicon degrading at different rates. Here, we develop a deep learning health diagnostic framework to rapidly quantify and separate the different degradation rates of graphite and silicon in composite anodes using partial charging data. The convolutional neural network (CNN), trained with synthetic data, uses experimental partial charging data to diagnose electrode-level health of tested batteries, with errors of less than 3.1% (corresponding to the loss of active material reaching ∼75%). Sensitivity analysis of the capacity-voltage curve under different degradation modes is performed to provide a physically informed voltage window for diagnostics with partial charging data. By using the gradient-weighted class activation mapping approach, we provide explainable insights into how these CNNs work; highlighting regions of the voltage-curve to which they are most sensitive. Robustness is validated by introducing noise to the data, with no significant negative impact on the diagnostic accuracy for noise levels below 10 mV, thus highlighting the potential for deep learning approaches in the diagnostics of lithium-ion battery performance under real-world conditions. The framework presented here can be generalised to other cell formats and chemistries, providing robust and explainable battery diagnostics for both conventional single material electrodes, but also the more challenging composite electrodes.</p></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666546824000181/pdfft?md5=af26ff78ede8c951359b33ae603e5c39&pid=1-s2.0-S2666546824000181-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140063159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-18DOI: 10.1016/j.egyai.2024.100351
Bo Li, Ali Mostafavi
Power system is vital to modern societies, while it is susceptible to hazard events. Thus, analyzing resilience characteristics of power system is important. The standard model of infrastructure resilience, the resilience triangle, has been the primary way of characterizing and quantifying resilience in infrastructure systems for more than two decades. However, the theoretical model provides a one-size-fits-all framework for all infrastructure systems and specifies general characteristics of resilience curves (e.g., residual performance and duration of recovery). Little empirical work has been done to delineate infrastructure resilience curve archetypes and their fundamental properties based on observational data. Most of the existing studies examine the characteristics of infrastructure resilience curves based on analytical models constructed upon simulated system performance. There is a dire dearth of empirical studies in the field, which hindered our ability to fully understand and predict resilience characteristics in infrastructure systems. To address this gap, this study examined more than two hundred power-grid resilience curves related to power outages in three major extreme weather events in the United States. Through the use of unsupervised machine learning, we examined different curve archetypes, as well as the fundamental properties of each resilience curve archetype. The results show two primary archetypes for power grid resilience curves, triangular curves, and trapezoidal curves. Triangular curves characterize resilience behavior based on three fundamental properties: 1. critical functionality threshold, 2. critical functionality recovery rate, and 3. recovery pivot point. Trapezoidal archetypes explain resilience curves based on 1. duration of sustained function loss and 2. constant recovery rate. The longer the duration of sustained function loss, the slower the constant rate of recovery. The findings of this study provide novel perspectives enabling better understanding and prediction of resilience performance of power system infrastructure in extreme weather events.
{"title":"Unraveling fundamental properties of power system resilience curves using unsupervised machine learning","authors":"Bo Li, Ali Mostafavi","doi":"10.1016/j.egyai.2024.100351","DOIUrl":"https://doi.org/10.1016/j.egyai.2024.100351","url":null,"abstract":"<div><p>Power system is vital to modern societies, while it is susceptible to hazard events. Thus, analyzing resilience characteristics of power system is important. The standard model of infrastructure resilience, the resilience triangle, has been the primary way of characterizing and quantifying resilience in infrastructure systems for more than two decades. However, the theoretical model provides a one-size-fits-all framework for all infrastructure systems and specifies general characteristics of resilience curves (e.g., residual performance and duration of recovery). Little empirical work has been done to delineate infrastructure resilience curve archetypes and their fundamental properties based on observational data. Most of the existing studies examine the characteristics of infrastructure resilience curves based on analytical models constructed upon simulated system performance. There is a dire dearth of empirical studies in the field, which hindered our ability to fully understand and predict resilience characteristics in infrastructure systems. To address this gap, this study examined more than two hundred power-grid resilience curves related to power outages in three major extreme weather events in the United States. Through the use of unsupervised machine learning, we examined different curve archetypes, as well as the fundamental properties of each resilience curve archetype. The results show two primary archetypes for power grid resilience curves, triangular curves, and trapezoidal curves. Triangular curves characterize resilience behavior based on three fundamental properties: 1. critical functionality threshold, 2. critical functionality recovery rate, and 3. recovery pivot point. Trapezoidal archetypes explain resilience curves based on 1. duration of sustained function loss and 2. constant recovery rate. The longer the duration of sustained function loss, the slower the constant rate of recovery. The findings of this study provide novel perspectives enabling better understanding and prediction of resilience performance of power system infrastructure in extreme weather events.</p></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S266654682400017X/pdfft?md5=cb4fd6b7ff61627f9c6f3afca7bd834b&pid=1-s2.0-S266654682400017X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139941919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-10DOI: 10.1016/j.egyai.2024.100350
Xueru Lin , Wei Zhong , Xiaojie Lin , Yi Zhou , Long Jiang , Liuliu Du-Ikonen , Long Huang
Amid the backdrop of carbon neutrality, traditional energy production is transitioning towards integrated energy systems (IES), where model-based scheduling is key in scenarios with multiple uncertainties on both supply and demand sides. The development of artificial intelligence algorithms, has resolved issues related to model accuracy. However, under conditions of high proportion renewable energy integration, component load adjustments require increased flexibility, so the mathematical model of the component must adapt to constantly changing operating conditions. Therefore, the identification of operating condition changes and rapid model updating are pressing issues. This study proposes a modeling and updating method for IES components based on knowledge distillation. The core of this modeling method is the light weighting of the model, which is achieved through a knowledge distillation method, using a teacher-student mode to compress complex neural network models. The triggering of model updates is achieved through principal component analysis. The study also analyzes the impact of model errors caused by delayed model updates on the overall scheduling of IES. Case studies are conducted on critical components in IES, including coal-fired boilers and turbines. The results show that the time consumption for model updating is reduced by 76.67 % using the proposed method. Under changing conditions, compared with two traditional models, the average deviation of this method is reduced by 12.61 % and 3.49 %, respectively, thereby improving the model's adaptability. The necessity of updating the component model is further analyzed, as a 1.00 % mean squared error in the component model may lead to a power deviation of 0.075 MW. This method provides real-time, adaptable support for IES data modeling and updates.
{"title":"Component modeling and updating method of integrated energy systems based on knowledge distillation","authors":"Xueru Lin , Wei Zhong , Xiaojie Lin , Yi Zhou , Long Jiang , Liuliu Du-Ikonen , Long Huang","doi":"10.1016/j.egyai.2024.100350","DOIUrl":"https://doi.org/10.1016/j.egyai.2024.100350","url":null,"abstract":"<div><p>Amid the backdrop of carbon neutrality, traditional energy production is transitioning towards integrated energy systems (IES), where model-based scheduling is key in scenarios with multiple uncertainties on both supply and demand sides. The development of artificial intelligence algorithms, has resolved issues related to model accuracy. However, under conditions of high proportion renewable energy integration, component load adjustments require increased flexibility, so the mathematical model of the component must adapt to constantly changing operating conditions. Therefore, the identification of operating condition changes and rapid model updating are pressing issues. This study proposes a modeling and updating method for IES components based on knowledge distillation. The core of this modeling method is the light weighting of the model, which is achieved through a knowledge distillation method, using a teacher-student mode to compress complex neural network models. The triggering of model updates is achieved through principal component analysis. The study also analyzes the impact of model errors caused by delayed model updates on the overall scheduling of IES. Case studies are conducted on critical components in IES, including coal-fired boilers and turbines. The results show that the time consumption for model updating is reduced by 76.67 % using the proposed method. Under changing conditions, compared with two traditional models, the average deviation of this method is reduced by 12.61 % and 3.49 %, respectively, thereby improving the model's adaptability. The necessity of updating the component model is further analyzed, as a 1.00 % mean squared error in the component model may lead to a power deviation of 0.075 MW. This method provides real-time, adaptable support for IES data modeling and updates.</p></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666546824000168/pdfft?md5=755ffadb08a047467ff057551e9c2d5e&pid=1-s2.0-S2666546824000168-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139737708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-09DOI: 10.1016/j.egyai.2024.100339
Marc Grossouvre , Didier Rullière , Jonathan Villot
Mass renovation goals aimed at energy savings on a national scale require a significant level of public financial commitment. To identify target buildings, decision-makers need a thorough understanding of energy performance. Energy Performance Certificates (EPC) provide information about areas of space, such as land plots or a building’s footprint, without specifying exact locations. They cover only a fraction of dwellings. This paper demonstrates that learning from observed EPCs to predict missing ones at the building level can be viewed as a spatial interpolation problem with uncertainty both on input and output variables. The Kriging methodology is applied to random fields observed at random locations to determine the Best Linear Unbiased Predictor (BLUP). Although the Gaussian setting is lost, conditional moments can still be derived. Covariates are admissible, even with missing observations. We present applications using both simulated and real data, with a specific case study of a city in France serving as an example.
{"title":"Predicting missing Energy Performance Certificates: Spatial interpolation of mixture distributions","authors":"Marc Grossouvre , Didier Rullière , Jonathan Villot","doi":"10.1016/j.egyai.2024.100339","DOIUrl":"https://doi.org/10.1016/j.egyai.2024.100339","url":null,"abstract":"<div><p>Mass renovation goals aimed at energy savings on a national scale require a significant level of public financial commitment. To identify target buildings, decision-makers need a thorough understanding of energy performance. Energy Performance Certificates (EPC) provide information about areas of space, such as land plots or a building’s footprint, without specifying exact locations. They cover only a fraction of dwellings. This paper demonstrates that learning from observed EPCs to predict missing ones at the building level can be viewed as a spatial interpolation problem with uncertainty both on input and output variables. The Kriging methodology is applied to random fields observed at random locations to determine the Best Linear Unbiased Predictor (BLUP). Although the Gaussian setting is lost, conditional moments can still be derived. Covariates are admissible, even with missing observations. We present applications using both simulated and real data, with a specific case study of a city in France serving as an example.</p></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666546824000053/pdfft?md5=933001c42f57cb4042aeb839ad99116b&pid=1-s2.0-S2666546824000053-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139743439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-04DOI: 10.1016/j.egyai.2024.100349
Yichuan Shao , Can Zhang , Lei Xing , Haijing Sun , Qian Zhao , Le Zhang
Dust accumulation on the surface of solar photovoltaic panels diminishes their power generation efficiency, leading to reduced energy generation. Regular monitoring and cleaning of solar photovoltaic panels is essential. Thus, developing optimal procedures for their upkeep is crucial for improving component efficiency, reducing maintenance costs, and conserving resources. This study introduces an improved Adam optimization algorithm designed specifically for detecting dust on the surface of solar photovoltaic panels. Although the traditional Adam algorithm is the preferred choice for optimizing neural network models, it occasionally encounters problems such as local optima, overfitting, and not convergence due to inconsistent learning rates during the optimization process. To mitigate these issues, the improved algorithm incorporates Warmup technology and cosine annealing strategies with traditional Adam algorithm, that allows for a gradual increase in the learning rate, ensuring stability in the preliminary phases of training. Concurrently, the improved algorithm employs a cosine annealing strategy to dynamically tweak the learning rate. This not only counters the local optimization issues to some degree but also bolsters the generalization ability of the model. When applied on the dust detection on the surface of solar photovoltaic panels, this improved algorithm exhibited superior convergence and training accuracy on the surface dust detection dataset of solar photovoltaic panels in comparison to the standard Adam method. Remarkably, it displayed noteworthy improvements within three distinct neural network frameworks: ResNet-18, VGG-16, and MobileNetV2, thereby attesting to the effectiveness of the novel algorithm. These findings hold significant promise and potential applications in the field of surface dust detection of solar photovoltaic panels. These research results will create economic benefits for enterprises and individuals, and are an important strategic development direction for the country.
太阳能光伏电池板表面的积尘会降低其发电效率,导致发电量减少。定期监测和清洁太阳能光伏电池板至关重要。因此,制定最佳的维护程序对于提高组件效率、降低维护成本和节约资源至关重要。本研究介绍了一种改进的 Adam 优化算法,专门用于检测太阳能光伏板表面的灰尘。虽然传统的 Adam 算法是优化神经网络模型的首选,但由于优化过程中学习率不一致,偶尔会遇到局部最优、过拟合和不收敛等问题。为了缓解这些问题,改进算法在传统亚当算法的基础上加入了热身技术和余弦退火策略,使学习率逐步提高,确保训练初期的稳定性。同时,改进算法采用余弦退火策略动态调整学习率。这不仅在一定程度上解决了局部优化问题,还增强了模型的泛化能力。在应用于太阳能光伏板表面灰尘检测时,与标准 Adam 方法相比,改进算法在太阳能光伏板表面灰尘检测数据集上表现出更高的收敛性和训练精度。值得注意的是,该算法在三种不同的神经网络框架中都有显著改进:ResNet-18、VGG-16 和 MobileNetV2,从而证明了新算法的有效性。这些发现为太阳能光伏板表面灰尘检测领域带来了重大希望和潜在应用。这些研究成果将为企业和个人创造经济效益,是国家重要的战略发展方向。
{"title":"A new dust detection method for photovoltaic panel surface based on Pytorch and its economic benefit analysis","authors":"Yichuan Shao , Can Zhang , Lei Xing , Haijing Sun , Qian Zhao , Le Zhang","doi":"10.1016/j.egyai.2024.100349","DOIUrl":"https://doi.org/10.1016/j.egyai.2024.100349","url":null,"abstract":"<div><p>Dust accumulation on the surface of solar photovoltaic panels diminishes their power generation efficiency, leading to reduced energy generation. Regular monitoring and cleaning of solar photovoltaic panels is essential. Thus, developing optimal procedures for their upkeep is crucial for improving component efficiency, reducing maintenance costs, and conserving resources. This study introduces an improved Adam optimization algorithm designed specifically for detecting dust on the surface of solar photovoltaic panels. Although the traditional Adam algorithm is the preferred choice for optimizing neural network models, it occasionally encounters problems such as local optima, overfitting, and not convergence due to inconsistent learning rates during the optimization process. To mitigate these issues, the improved algorithm incorporates Warmup technology and cosine annealing strategies with traditional Adam algorithm, that allows for a gradual increase in the learning rate, ensuring stability in the preliminary phases of training. Concurrently, the improved algorithm employs a cosine annealing strategy to dynamically tweak the learning rate. This not only counters the local optimization issues to some degree but also bolsters the generalization ability of the model. When applied on the dust detection on the surface of solar photovoltaic panels, this improved algorithm exhibited superior convergence and training accuracy on the surface dust detection dataset of solar photovoltaic panels in comparison to the standard Adam method. Remarkably, it displayed noteworthy improvements within three distinct neural network frameworks: ResNet-18, VGG-16, and MobileNetV2, thereby attesting to the effectiveness of the novel algorithm. These findings hold significant promise and potential applications in the field of surface dust detection of solar photovoltaic panels. These research results will create economic benefits for enterprises and individuals, and are an important strategic development direction for the country.</p></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666546824000156/pdfft?md5=c78266a2122e06eccd7d26db304d2f0b&pid=1-s2.0-S2666546824000156-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139714658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-02DOI: 10.1016/j.egyai.2024.100347
António Corte Real , G. Pontes Luz , J.M.C. Sousa , M.C. Brito , S.M. Vieira
Home Energy Management Systems (HEMS) are increasingly relevant for demand-side management at the residential level by collecting data (energy, weather, electricity prices) and controlling home appliances or storage systems. This control can be performed with classical models that find optimal solutions, with high real-time computational cost, or data-driven approaches, like Reinforcement Learning, that find good and flexible solutions, but depend on the availability of load and generation data and demand high computational resources for training. In this work, a novel HEMS is proposed for the optimization of an electric battery operation in a real, online and data-driven environment that integrates state-of-the-art load forecasting combining CNN and LSTM neural networks to increase the robustness of decisions. Several Reinforcement Learning agents are trained with different algorithms (Double DQN, Dueling DQN, Rainbow and Proximal Policy Optimization) in order to minimize the cost of electricity purchase and to maximize photovoltaic self-consumption for a PV-Battery residential system. Results show that the best Reinforcement Learning agent achieves a 35% reduction in total cost when compared with an optimization-based agent.
{"title":"Optimization of a photovoltaic-battery system using deep reinforcement learning and load forecasting","authors":"António Corte Real , G. Pontes Luz , J.M.C. Sousa , M.C. Brito , S.M. Vieira","doi":"10.1016/j.egyai.2024.100347","DOIUrl":"10.1016/j.egyai.2024.100347","url":null,"abstract":"<div><p>Home Energy Management Systems (HEMS) are increasingly relevant for demand-side management at the residential level by collecting data (energy, weather, electricity prices) and controlling home appliances or storage systems. This control can be performed with classical models that find optimal solutions, with high real-time computational cost, or data-driven approaches, like Reinforcement Learning, that find good and flexible solutions, but depend on the availability of load and generation data and demand high computational resources for training. In this work, a novel HEMS is proposed for the optimization of an electric battery operation in a real, online and data-driven environment that integrates state-of-the-art load forecasting combining CNN and LSTM neural networks to increase the robustness of decisions. Several Reinforcement Learning agents are trained with different algorithms (Double DQN, Dueling DQN, Rainbow and Proximal Policy Optimization) in order to minimize the cost of electricity purchase and to maximize photovoltaic self-consumption for a PV-Battery residential system. Results show that the best Reinforcement Learning agent achieves a 35% reduction in total cost when compared with an optimization-based agent.</p></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666546824000132/pdfft?md5=801e90a3cad6681c711e85effe347670&pid=1-s2.0-S2666546824000132-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139686159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-01DOI: 10.1016/j.egyai.2024.100346
Ziyao Yang , Amol D. Gaidhane , Ján Drgoňa , Vikas Chandan , Mahantesh M. Halappanavar , Frank Liu , Yu Cao
In this paper, we propose a graph model embedded with compact physical equations for modeling the thermal dynamics of buildings. The principles of heat flow across various components in the building, such as walls and doors, fit the message-passing strategy used by Graph Neural networks (GNNs). The proposed method is to represent the multi-zone building as a graph, in which only zones are considered as nodes, and any heat flow between zones is modeled as an edge based on prior knowledge of the building structure. Furthermore, the thermal dynamics of these components are described by compact models in the graph. GNNs are further employed to train model parameters from collected data. During model training, our proposed method enforces physical constraints (e.g., zone sizes and connections) on model parameters and propagates the penalty in the loss function of GNN. Such constraints are essential to ensure model robustness and interpretability. We evaluate the effectiveness of the proposed modeling approach on a realistic dataset with multiple zones. The results demonstrate a satisfactory accuracy in the prediction of multi-zone temperature. Moreover, we illustrate that the new model can reliably learn hidden physical parameters with incomplete data.
{"title":"Physics-constrained graph modeling for building thermal dynamics","authors":"Ziyao Yang , Amol D. Gaidhane , Ján Drgoňa , Vikas Chandan , Mahantesh M. Halappanavar , Frank Liu , Yu Cao","doi":"10.1016/j.egyai.2024.100346","DOIUrl":"10.1016/j.egyai.2024.100346","url":null,"abstract":"<div><p>In this paper, we propose a graph model embedded with compact physical equations for modeling the thermal dynamics of buildings. The principles of heat flow across various components in the building, such as walls and doors, fit the message-passing strategy used by Graph Neural networks (GNNs). The proposed method is to represent the multi-zone building as a graph, in which only zones are considered as nodes, and any heat flow between zones is modeled as an edge based on prior knowledge of the building structure. Furthermore, the thermal dynamics of these components are described by compact models in the graph. GNNs are further employed to train model parameters from collected data. During model training, our proposed method enforces physical constraints (e.g., zone sizes and connections) on model parameters and propagates the penalty in the loss function of GNN. Such constraints are essential to ensure model robustness and interpretability. We evaluate the effectiveness of the proposed modeling approach on a realistic dataset with multiple zones. The results demonstrate a satisfactory accuracy in the prediction of multi-zone temperature. Moreover, we illustrate that the new model can reliably learn hidden physical parameters with incomplete data.</p></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666546824000120/pdfft?md5=0e483d0c4e3f88c26fc90d4d25f20085&pid=1-s2.0-S2666546824000120-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139685552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-30DOI: 10.1016/j.egyai.2024.100341
Franz M. Rohrhofer , Stefan Posch , Clemens Gößnitzer , José M. García-Oliver , Bernhard C. Geiger
Artificial Neural Networks (ANNs) have emerged as a powerful tool in combustion simulations to replace memory-intensive tabulation of integrated chemical kinetics. Complex reaction mechanisms, however, present a challenge for standard ANN approaches as modeling multiple species typically suffers from inaccurate predictions on minor species. This paper presents a novel ANN approach which can be applied on complex reaction mechanisms in tabular data form, and only involves training a single ANN for a complete reaction mechanism. The approach incorporates a network architecture that automatically conserves mass and employs a particular loss weighting based on species depletion. Both modifications are used to improve the overall ANN performance and individual prediction accuracies, especially for minor species mass fractions. To validate its effectiveness, the approach is compared to standard ANNs in terms of performance and ANN complexity. Four distinct reaction mechanisms (H, CH, CH, OME) are used as a test cases, and results demonstrate that considerable improvements can be achieved by applying both modifications.
人工神经网络(ANN)已成为燃烧模拟中的一种强大工具,可取代需要大量记忆的综合化学动力学表格。然而,复杂的反应机制给标准的人工神经网络方法带来了挑战,因为多物种建模通常会导致对次要物种的预测不准确。本文介绍了一种新颖的方差网络方法,该方法可应用于表格数据形式的复杂反应机理,而且只需为完整的反应机理训练一个方差网络。该方法采用了自动保存质量的网络架构,并根据物种损耗采用了特定的损耗加权。这两项修改都用于提高 ANN 的整体性能和单个预测的准确性,尤其是对小物种质量分数的预测。为了验证该方法的有效性,我们将其与标准自动数值网络的性能和复杂性进行了比较。四个不同的反应机理(H2、C7H16、C12H26、OME34)被用作测试案例,结果表明,通过应用这两种修改,可以实现相当大的改进。
{"title":"Utilizing neural networks to supplant chemical kinetics tabulation through mass conservation and weighting of species depletion","authors":"Franz M. Rohrhofer , Stefan Posch , Clemens Gößnitzer , José M. García-Oliver , Bernhard C. Geiger","doi":"10.1016/j.egyai.2024.100341","DOIUrl":"https://doi.org/10.1016/j.egyai.2024.100341","url":null,"abstract":"<div><p>Artificial Neural Networks (ANNs) have emerged as a powerful tool in combustion simulations to replace memory-intensive tabulation of integrated chemical kinetics. Complex reaction mechanisms, however, present a challenge for standard ANN approaches as modeling multiple species typically suffers from inaccurate predictions on minor species. This paper presents a novel ANN approach which can be applied on complex reaction mechanisms in tabular data form, and only involves training a single ANN for a complete reaction mechanism. The approach incorporates a network architecture that automatically conserves mass and employs a particular loss weighting based on species depletion. Both modifications are used to improve the overall ANN performance and individual prediction accuracies, especially for minor species mass fractions. To validate its effectiveness, the approach is compared to standard ANNs in terms of performance and ANN complexity. Four distinct reaction mechanisms (H<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span>, C<span><math><msub><mrow></mrow><mrow><mn>7</mn></mrow></msub></math></span>H<span><math><msub><mrow></mrow><mrow><mn>16</mn></mrow></msub></math></span>, C<span><math><msub><mrow></mrow><mrow><mn>12</mn></mrow></msub></math></span>H<span><math><msub><mrow></mrow><mrow><mn>26</mn></mrow></msub></math></span>, OME<span><math><msub><mrow></mrow><mrow><mn>34</mn></mrow></msub></math></span>) are used as a test cases, and results demonstrate that considerable improvements can be achieved by applying both modifications.</p></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666546824000077/pdfft?md5=74cf715196106974d71e060a8c29f244&pid=1-s2.0-S2666546824000077-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139674993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-28DOI: 10.1016/j.egyai.2024.100348
Ziyu Liu , Xiaoyi Yang
Supersonic aircraft requires thermal endurance of aviation fuel in the process of cooling engine and aircraft. As the composition of petroleum-based jet fuel (RP-3) is confined by crude oil and refining process, sustainable alternative jet fuel with green house gas reduction become to undertake the composition optimization for improving thermal stability. For designing aviation fuel with robust thermal stability and the detail understanding of thermal stability mechanism, RP-3, Fischer–Tropsch fuel, and additives with cyclic structure for absorbing free radical, were investigated thermal stability by modifying different blend ratios under different conditions. Thermal endurance degree was assessed by chroma and deposition tendency. FT blend with cyclic hydrocarbon can improve thermal endurance degree. In compliance with individual optimized blend ratio, the contribution follows methyl cyclopentane > decalin > methyl cyclohexane > tetralin > n-propyl-benzene > 1,2,4 trimethyl-benzene. The appropriate blend ratio could undertake hydrogen donors for terminating the propagation of oxygen-carrying radical, but hydrocarbons with cyclic structure could enhance deposition tendency. Methyl cyclopentane and its oxidation derivatives take the roles of solvent by anti-polymerization and hydrogen donor by opening cyclic structure in the thermal endurance process, and thus lead to a wide range of blend ratio for improving significantly thermal stability. β-scission leading to C–C bond cleavage is the major reaction at the early decomposition stage, which resulted in most abundant derivatives plus C2. The effects of additives on thermal stability are complex and nonlinear on the tendency of thermal deposits and thermal endurance degree, and thus the appropriate ANN-thermal stability model has been trained based on the experiment data and can achieve above 0.995 correlation coefficient. ANN - thermal stability model can predict not only the content of derivatives including ester, olefin, alcohol, ketone, cyclic oxide, aromatics but also the degree of thermal endurance.
{"title":"Thermal stability enhancement and prediction by ANN model","authors":"Ziyu Liu , Xiaoyi Yang","doi":"10.1016/j.egyai.2024.100348","DOIUrl":"10.1016/j.egyai.2024.100348","url":null,"abstract":"<div><p>Supersonic aircraft requires thermal endurance of aviation fuel in the process of cooling engine and aircraft. As the composition of petroleum-based jet fuel (RP-3) is confined by crude oil and refining process, sustainable alternative jet fuel with green house gas reduction become to undertake the composition optimization for improving thermal stability. For designing aviation fuel with robust thermal stability and the detail understanding of thermal stability mechanism, RP-3, Fischer–Tropsch fuel, and additives with cyclic structure for absorbing free radical, were investigated thermal stability by modifying different blend ratios under different conditions. Thermal endurance degree was assessed by chroma and deposition tendency. FT blend with cyclic hydrocarbon can improve thermal endurance degree. In compliance with individual optimized blend ratio, the contribution follows methyl cyclopentane > decalin > methyl cyclohexane > tetralin > n-propyl-benzene > 1,2,4 trimethyl-benzene. The appropriate blend ratio could undertake hydrogen donors for terminating the propagation of oxygen-carrying radical, but hydrocarbons with cyclic structure could enhance deposition tendency. Methyl cyclopentane and its oxidation derivatives take the roles of solvent by anti-polymerization and hydrogen donor by opening cyclic structure in the thermal endurance process, and thus lead to a wide range of blend ratio for improving significantly thermal stability. <em>β</em>-scission leading to C–C bond cleavage is the major reaction at the early decomposition stage, which resulted in most abundant derivatives plus C2. The effects of additives on thermal stability are complex and nonlinear on the tendency of thermal deposits and thermal endurance degree, and thus the appropriate ANN-thermal stability model has been trained based on the experiment data and can achieve above 0.995 correlation coefficient. ANN - thermal stability model can predict not only the content of derivatives including ester, olefin, alcohol, ketone, cyclic oxide, aromatics but also the degree of thermal endurance.</p></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666546824000144/pdfft?md5=a90d0cecc6ebe83e3174b5bdd57fd399&pid=1-s2.0-S2666546824000144-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139634775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}