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Component modeling and updating method of integrated energy systems based on knowledge distillation 基于知识提炼的综合能源系统组件建模与更新方法
Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-02-10 DOI: 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.

在实现碳中和的背景下,传统能源生产正在向综合能源系统(IES)过渡,在供需双方都存在多种不确定性的情况下,基于模型的调度是关键所在。人工智能算法的发展解决了与模型精度相关的问题。然而,在高比例可再生能源集成的条件下,组件负荷调整需要更高的灵活性,因此组件的数学模型必须适应不断变化的运行条件。因此,运行条件变化的识别和模型的快速更新是亟待解决的问题。本研究提出了一种基于知识提炼的 IES 组件建模和更新方法。该建模方法的核心是模型的轻量化,通过知识蒸馏法实现,采用师生模式压缩复杂的神经网络模型。模型更新的触发是通过主成分分析实现的。研究还分析了模型更新延迟导致的模型误差对 IES 整体调度的影响。案例研究针对 IES 的关键部件,包括燃煤锅炉和涡轮机。结果表明,使用所提出的方法,模型更新的时间消耗减少了 76.67%。在变化条件下,与两种传统模型相比,该方法的平均偏差分别减少了 12.61 % 和 3.49 %,从而提高了模型的适应性。进一步分析了更新组件模型的必要性,因为组件模型中 1.00 % 的均方误差可能导致 0.075 MW 的功率偏差。该方法为 IES 数据建模和更新提供了实时、适应性强的支持。
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引用次数: 0
Predicting missing Energy Performance Certificates: Spatial interpolation of mixture distributions 预测丢失的能源性能证书:混合分布的空间插值
Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-02-09 DOI: 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.

在全国范围内实现大规模节能改造目标需要大量的公共财政投入。为了确定目标建筑,决策者需要全面了解能源性能。能源性能证书(EPC)提供的是空间区域的信息,如地块或建筑物的占地面积,而没有具体说明确切的位置。它们只覆盖了一小部分住宅。本文证明,从观测到的 EPCs 中学习,以预测建筑物层面上缺失的 EPCs,可视为一个空间插值问题,输入和输出变量都存在不确定性。克里金方法适用于在随机位置观测到的随机场,以确定最佳线性无偏预测器 (BLUP)。虽然失去了高斯设置,但条件矩仍然可以导出。即使观测数据缺失,也可以使用协变量。我们以法国某城市的具体案例研究为例,介绍了模拟数据和真实数据的应用。
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引用次数: 0
A new dust detection method for photovoltaic panel surface based on Pytorch and its economic benefit analysis 基于 Pytorch 的光伏面板表面灰尘检测新方法及其经济效益分析
Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-02-04 DOI: 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。
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引用次数: 0
Optimization of a photovoltaic-battery system using deep reinforcement learning and load forecasting 利用深度强化学习和负荷预测优化光伏电池系统
Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-02-02 DOI: 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.

家庭能源管理系统(HEMS)通过收集数据(能源、天气、电价)和控制家用电器或储能系统,在住宅层面的需求侧管理中发挥着越来越重要的作用。这种控制可以采用传统模型,找到最优解,但实时计算成本较高;也可以采用数据驱动方法,如强化学习,找到良好而灵活的解决方案,但这取决于负载和发电数据的可用性,并且需要大量计算资源进行训练。在这项工作中,我们提出了一种新型 HEMS,用于在真实、在线和数据驱动的环境中优化蓄电池的运行,该系统集成了最先进的负荷预测技术,并结合了 CNN 和 LSTM 神经网络,以提高决策的鲁棒性。使用不同的算法(双 DQN、决斗 DQN、彩虹和近端策略优化)对多个强化学习代理进行了训练,以最小化购电成本,最大化光伏电池住宅系统的光伏自消耗。结果表明,与基于优化的代理相比,最佳强化学习代理的总成本降低了 35%。
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引用次数: 0
Physics-constrained graph modeling for building thermal dynamics 建筑热动力学的物理约束图建模
Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-02-01 DOI: 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.

在本文中,我们提出了一种内嵌紧凑物理方程的图形模型,用于模拟建筑物的热动态。建筑物内各部件(如墙壁和门)之间的热流原理符合图神经网络(GNN)所使用的信息传递策略。所提出的方法是将多分区建筑物表示为一个图,其中只将分区视为节点,分区之间的任何热流都将根据建筑物结构的先验知识建模为一条边。此外,这些组件的热动态由图中的紧凑模型来描述。我们进一步采用 GNN 从收集到的数据中训练模型参数。在模型训练过程中,我们提出的方法会对模型参数施加物理约束(如区域大小和连接),并在 GNN 的损失函数中传播惩罚。这些约束对于确保模型的稳健性和可解释性至关重要。我们在一个具有多个区域的现实数据集上评估了所提出的建模方法的有效性。结果表明,多区域温度预测的准确性令人满意。此外,我们还证明了新模型可以在数据不完整的情况下可靠地学习隐藏的物理参数。
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引用次数: 0
Utilizing neural networks to supplant chemical kinetics tabulation through mass conservation and weighting of species depletion 通过质量守恒和物种损耗加权,利用神经网络取代化学动力学制表法
Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-01-30 DOI: 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 (H2, C7H16, C12H26, OME34) 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。
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引用次数: 0
Thermal stability enhancement and prediction by ANN model 热稳定性增强和 ANN 模型预测
Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-01-28 DOI: 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.

超音速飞机在冷却发动机和飞机的过程中需要航空燃料的热稳定性。由于石油基喷气燃料(RP-3)的成分受原油和提炼工艺的限制,因此,可持续的、减少温室气体排放的替代喷气燃料就成为提高热稳定性的成分优化对象。为了设计热稳定性强的航空燃料,并详细了解热稳定性机理,研究人员在不同条件下,通过改变不同的混合比例,研究了 RP-3、费托燃料和具有吸收自由基的环状结构添加剂的热稳定性。通过色度和沉积趋势评估热稳定性。FT 与环状碳氢化合物的混合可提高热稳定性。根据各自优化的混合比例,其贡献依次为甲基环戊烷、癸醛、甲基环己烷、四萘、正丙基苯和 1,2,4-三甲基苯。适当的混合比例可以为终止携氧自由基的传播提供氢供体,但具有环状结构的碳氢化合物会增强沉积趋势。甲基环戊烷及其氧化衍生物在热稳定性过程中既是抗聚合的溶剂,又是开启环状结构的供氢体,因此可在较宽的混合比范围内显著提高热稳定性。在早期分解阶段,C-C 键裂解导致的 β 裂解是主要反应,从而产生了最丰富的衍生物加 C2。添加剂对热稳定性的影响是复杂的,与热沉积趋势和热耐受程度呈非线性关系,因此根据实验数据训练了合适的 ANN 热稳定性模型,相关系数可达 0.995 以上。ANN 热稳定性模型不仅能预测酯、烯烃、醇、酮、环氧化物、芳烃等衍生物的含量,还能预测热稳定性的程度。
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引用次数: 0
Comparative study of univariate and multivariate strategy for short-term forecasting of heat demand density: Exploring single and hybrid deep learning models 热需求密度短期预测的单变量和多变量策略比较研究:探索单一和混合深度学习模型
Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-01-24 DOI: 10.1016/j.egyai.2024.100343
Sajad Salehi , Miroslava Kavgic , Hossein Bonakdari , Luc Begnoche

Accurate short-term forecasting of heating energy demand is needed for achieving optimal building energy management, cost savings, environmental sustainability, and responsible energy consumption. Furthermore, short-term heating energy prediction contributes to zero-energy building performance in cold climates. Given the critical importance of short-term forecasting in heating energy management, this study evaluated six prevalent deep-learning algorithms to predict energy load, including single and hybrid models. The overall best-performing predictors were hybrid models using Convolutional Neural Networks, regardless of whether they were multivariate or univariate. Nevertheless, while the multivariate models performed better in the first hour, the univariate models often were more accurate in the final 24 h. Thus, the best-performing predictor of the first timestep was a multivariate hybrid Convolutional Neural Network–Recurrent Neural Network model with a coefficient of determination (R²) of 0.98 and the lowest mean absolute error. Yet, the best-performing predictor of the final timestep was the univariate hybrid model Convolutional Neural Network–Long Short-Term Memory with an R² of 0.80. Also, the prediction accuracy of the best-performing multivariate hybrid models reduced faster per hour compared to the univariate models. These findings suggest that multivariate models may be better suited for early timestep predictions, while univariate models may be better suited for later time steps. Hence, combining the models can enhance accuracy at various timesteps for achieving high fidelity in forecasting and offering a comprehensive tool for energy management.

要实现最佳的建筑能源管理、节约成本、环境可持续性和负责任的能源消耗,就需要对供暖能源需求进行准确的短期预测。此外,短期供暖能源预测还有助于在寒冷气候条件下实现零能耗建筑性能。鉴于短期预测在供热能源管理中的极端重要性,本研究评估了六种常用的深度学习算法来预测能源负荷,包括单一模型和混合模型。总体而言,使用卷积神经网络的混合模型是表现最好的预测模型,无论它们是多元模型还是单变量模型。然而,虽然多元模型在第一个小时内表现较好,但在最后 24 小时内,单变量模型往往更为准确。因此,第一个时间步表现最好的预测模型是多元混合卷积神经网络-循环神经网络模型,其决定系数(R²)为 0.98,平均绝对误差最小。然而,对最终时间步预测效果最好的是单变量混合模型卷积神经网络-长短期记忆,其 R² 为 0.80。此外,与单变量模型相比,表现最佳的多元混合模型的预测准确率每小时下降得更快。这些发现表明,多元模型可能更适合早期时间步预测,而单变量模型可能更适合后期时间步预测。因此,组合模型可以提高不同时间步的准确性,从而实现高保真预测,为能源管理提供全面的工具。
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引用次数: 0
Data-driven modeling and fault diagnosis for fuel cell vehicles using deep learning 利用深度学习对燃料电池汽车进行数据驱动建模和故障诊断
Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-01-24 DOI: 10.1016/j.egyai.2024.100345
Yangeng Chen , Jingjing Zhang , Shuang Zhai , Zhe Hu

The reliability and safety of fuel cell vehicle are crucial for the daily operation. Insulation resistance serves as a crucial index of vehicle reliability, especially when fuel cells operate at high voltages. Low insulation resistance can lead to vehicle malfunctions, exposing the operator to the risk of electric shock. In this study, long-term insulation resistance data from thirteen vehicles equipped with three different types of fuel cell systems are analyzed to diagnose possible low insulation resistance issues. For this purpose, a robust locally weighted scatterplot smoothing method is utilized to filter the original data. In this research, an insulation variation model is developed using a data-driven long short-term memory neural network to identify insulation resistance value anomalies caused by deionizer failure. The results indicate that the coefficient of determination of the failure model is 99.78 %. Moreover, current model efficiently identifies insulation faults resulting from reliability issues, such as conductivity issues of cooling pipes and erosion of vehicle wiring harnesses.

燃料电池汽车的可靠性和安全性对日常运行至关重要。绝缘电阻是衡量车辆可靠性的重要指标,尤其是当燃料电池在高电压下工作时。绝缘电阻过低会导致车辆故障,使操作人员面临触电风险。本研究分析了 13 辆配备三种不同类型燃料电池系统的车辆的长期绝缘电阻数据,以诊断可能存在的低绝缘电阻问题。为此,采用了一种稳健的局部加权散点图平滑方法来过滤原始数据。在这项研究中,利用数据驱动的长短期记忆神经网络开发了一个绝缘变化模型,以识别由去离子器故障引起的绝缘电阻值异常。结果表明,故障模型的判定系数为 99.78 %。此外,当前模型还能有效识别可靠性问题导致的绝缘故障,如冷却管的导电性问题和汽车线束的侵蚀问题。
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引用次数: 0
Cross-level steam load smoothing and optimization in industrial parks using data-driven approaches 利用数据驱动方法平滑和优化工业园区的跨级蒸汽负荷
Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-01-24 DOI: 10.1016/j.egyai.2024.100344
Xiaojie Lin , Xueru Lin , Wei Zhong , Feiyun Cong , Yi Zhou

This study focuses on the integrated energy production system in industrial parks, addressing the problem of stable load dispatch of equipment under demand fluctuations. A cross-level method for steam load smoothing and optimization is proposed, aiming to achieve stable production and optimal economic performance through three levels of integration: load forecasting, load dispatch, and load regulation. Unlike traditional methods that directly use load forecasting values, heat network elasticity is presented as a buffer between demand and supply. Constraints for minimal changes in equipment load and operational parameters are established for smooth regulation. Industrial cases demonstrate that the load forecasting model has mean absolute percentage errors of 2.44% and 1.68% for medium-pressure and low-pressure steam, respectively, meeting accuracy requirements. The modified supply-side load smoothness is effectively improved by considering heat network elasticity. The method increases boiler efficiency by 1.92%, reducing average coal consumption by 0.92 t/h. Compared to manual operation, the proposed model leads to an average increase of 5.69 MW in power generation and an average reduction of 10.81% in coal-to-electricity ratio. This study verifies the importance of smooth integration across different levels and analyzes the effective response of the proposed method to the uncertainty in load forecasting. The method demonstrates the enormous potential of data-driven methods in achieving safe, economical, and sustainable production in industrial parks.

本研究以工业园区综合能源生产系统为重点,解决需求波动下的设备稳定负荷调度问题。提出了一种跨层次的蒸汽负荷平滑和优化方法,旨在通过负荷预测、负荷调度和负荷调节三个层次的整合,实现稳定的生产和最优的经济效益。与直接使用负荷预测值的传统方法不同,该方法将热网弹性作为供需之间的缓冲。为实现平稳调节,对设备负荷和运行参数的最小变化进行了限制。工业案例表明,负荷预测模型对中压蒸汽和低压蒸汽的平均绝对百分比误差分别为 2.44% 和 1.68%,符合精度要求。通过考虑热网弹性,修正后的供方负荷平稳性得到了有效改善。该方法使锅炉效率提高了 1.92%,平均煤耗降低了 0.92 吨/小时。与手动操作相比,所提出的模型可使发电量平均增加 5.69 兆瓦,煤电比平均降低 10.81%。这项研究验证了不同层面平滑整合的重要性,并分析了所提方法对负荷预测不确定性的有效响应。该方法展示了数据驱动方法在实现工业园区安全、经济和可持续生产方面的巨大潜力。
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引用次数: 0
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