电价是影响所有市场参与者决策的关键因素。准确的电价预测非常重要,并且由于各种因素,电价高度挥发性,电价也非常具有挑战性。本文提出了一项综合的长期经常性卷积网络(ILRCN)模型,以预测考虑到市场价格的大多数贡献属性的电力价格。所提出的ILRCN模型将卷积神经网络和长短期记忆(LSTM)算法的功能与所提出的新颖的条件纠错项相结合。组合的ILRCN模型可以识别输入数据内的线性和非线性行为。我们使用鄂尔顿批发市场价格数据以及负载型材,温度和其他因素来说明所提出的模型。使用平均绝对误差和准确性等性能/评估度量来验证所提出的ILRCN电价预测模型的性能。案例研究表明,与支持向量机(SVM)模型,完全连接的神经网络模型,LSTM模型和LRCN模型,所提出的ILRCN模型在电价预测中是准确和有效的电力价格预测。
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With the evolution of power systems as it is becoming more intelligent and interactive system while increasing in flexibility with a larger penetration of renewable energy sources, demand prediction on a short-term resolution will inevitably become more and more crucial in designing and managing the future grid, especially when it comes to an individual household level. Projecting the demand for electricity for a single energy user, as opposed to the aggregated power consumption of residential load on a wide scale, is difficult because of a considerable number of volatile and uncertain factors. This paper proposes a customized GRU (Gated Recurrent Unit) and Long Short-Term Memory (LSTM) architecture to address this challenging problem. LSTM and GRU are comparatively newer and among the most well-adopted deep learning approaches. The electricity consumption datasets were obtained from individual household smart meters. The comparison shows that the LSTM model performs better for home-level forecasting than alternative prediction techniques-GRU in this case. To compare the NN-based models with contrast to the conventional statistical technique-based model, ARIMA based model was also developed and benchmarked with LSTM and GRU model outcomes in this study to show the performance of the proposed model on the collected time series data.
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电池储能系统(BES)可以有效地减轻可变生成的不确定性。降解是不可预防的,难以建模,并且可以预测诸如最受欢迎的锂离子电池(LIB)等电池。在本文中,我们提出了一种数据驱动的方法,以预测给定的预定电池操作专业文件的蝙蝠降解。特别是,提出了基于神经网络的电池降解(NNBD)模型,以用主要电池降解因子的输入来量化电池降解。当将拟议的NNBD模型限制为微电网日期调度(MDS)时,我们可以建立基于电池降解的MDS(BDMDS)模型,该模型可以考虑在拟议的基于循环的电池用途(CBUP)(CBUP)(CBUP)(CBUP)的情况下准确地考虑等效的电池降解成本NNBD模型的方法。由于所提出的NNBD模型是高度非线性的,因此BDMD很难解决。为了解决这个问题,本文提出了一个神经网络和优化解耦启发式(NNODH)算法,以有效解决此神经网络嵌入式优化问题。仿真结果表明,所提出的NNODH算法能够以最低的总成本(包括正常运行成本和电池降解成本)遵守最佳解决方案。
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A well-performing prediction model is vital for a recommendation system suggesting actions for energy-efficient consumer behavior. However, reliable and accurate predictions depend on informative features and a suitable model design to perform well and robustly across different households and appliances. Moreover, customers' unjustifiably high expectations of accurate predictions may discourage them from using the system in the long term. In this paper, we design a three-step forecasting framework to assess predictability, engineering features, and deep learning architectures to forecast 24 hourly load values. First, our predictability analysis provides a tool for expectation management to cushion customers' anticipations. Second, we design several new weather-, time- and appliance-related parameters for the modeling procedure and test their contribution to the model's prediction performance. Third, we examine six deep learning techniques and compare them to tree- and support vector regression benchmarks. We develop a robust and accurate model for the appliance-level load prediction based on four datasets from four different regions (US, UK, Austria, and Canada) with an equal set of appliances. The empirical results show that cyclical encoding of time features and weather indicators alongside a long-short term memory (LSTM) model offer the optimal performance.
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负载预测在电力系统的分析和网格计划中至关重要。因此,我们首先提出一种基于联邦深度学习和非侵入性负载监测(NILM)的家庭负载预测方法。就我们所知,这是基于尼尔姆的家庭负载预测中有关联合学习(FL)的首次研究。在这种方法中,通过非侵入性负载监控将集成功率分解为单个设备功率,并且使用联合深度学习模型分别预测单个设备的功率。最后,将单个设备的预测功率值聚合以形成总功率预测。具体而言,通过单独预测电气设备以获得预测的功率,它可以避免由于单个设备的功率信号的强烈依赖性而造成的误差。在联邦深度学习预测模型中,具有权力数据的家主共享本地模型的参数,而不是本地电源数据,从而保证了家庭用户数据的隐私。案例结果表明,所提出的方法比直接预测整个汇总信号的传统方法提供了更好的预测效果。此外,设计和实施了各种联合学习环境中的实验,以验证该方法的有效性。
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在具有可再生生成的大量份额的网格中,由于负载和发电的波动性增加,运营商将需要其他工具来评估运营风险。正向不确定性传播问题的计算要求必须解决众多安全受限的经济调度(SCED)优化,是这种实时风险评估的主要障碍。本文提出了一个即时风险评估学习框架(Jitralf)作为替代方案。 Jitralf训练风险代理,每天每小时一个,使用机器学习(ML)来预测估计风险所需的数量,而无需明确解决SCED问题。这大大减轻了正向不确定性传播的计算负担,并允许快速,实时的风险估计。本文还提出了一种新颖的,不对称的损失函数,并表明使用不对称损失训练的模型的性能优于使用对称损耗函数的模型。在法国传输系统上评估了Jitralf,以评估运营储量不足的风险,减轻负载的风险和预期的运营成本。
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准确的负载预测对于电力系统的电力市场运营以及电力系统中的其他实时决策任务至关重要。本文认为社区内的住宅客户的短期负荷预测(STLF)问题。现有的STLF工作主要侧重于预测馈线系统或单一客户的汇总负荷,但是在预测单个设备水平的负荷上,已经努力。在这项工作中,我们介绍了一种用于有效预测各个电器的功耗的STLF算法。所提出的方法在深度学习中强大的经常性神经网络(RNN)架构,称为长短短期记忆(LSTM)。当每个设备具有唯一重复的消耗模式时,将跟踪预测误差的模式,使得过去的预测误差可用于提高最终预测性能。实际负载数据集的数值测试证明了在现有的基于LSTM的方法和其他基准方法上提高了所提出的方法。
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In this paper, we propose a new short-term load forecasting (STLF) model based on contextually enhanced hybrid and hierarchical architecture combining exponential smoothing (ES) and a recurrent neural network (RNN). The model is composed of two simultaneously trained tracks: the context track and the main track. The context track introduces additional information to the main track. It is extracted from representative series and dynamically modulated to adjust to the individual series forecasted by the main track. The RNN architecture consists of multiple recurrent layers stacked with hierarchical dilations and equipped with recently proposed attentive dilated recurrent cells. These cells enable the model to capture short-term, long-term and seasonal dependencies across time series as well as to weight dynamically the input information. The model produces both point forecasts and predictive intervals. The experimental part of the work performed on 35 forecasting problems shows that the proposed model outperforms in terms of accuracy its predecessor as well as standard statistical models and state-of-the-art machine learning models.
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评估能源转型和能源市场自由化对资源充足性的影响是一种越来越重要和苛刻的任务。能量系统的上升复杂性需要足够的能量系统建模方法,从而提高计算要求。此外,随着复杂性,同样调用概率评估和场景分析同样增加不确定性。为了充分和高效地解决这些各种要求,需要来自数据科学领域的新方法来加速当前方法。通过我们的系统文献综述,我们希望缩小三个学科之间的差距(1)电力供应安全性评估,(2)人工智能和(3)实验设计。为此,我们对所选应用领域进行大规模的定量审查,并制作彼此不同学科的合成。在其他发现之外,我们使用基于AI的方法和应用程序的AI方法和应用来确定电力供应模型的复杂安全性的元素,并作为未充分涵盖的应用领域的储存调度和(非)可用性。我们结束了推出了一种新的方法管道,以便在评估电力供应安全评估时充分有效地解决当前和即将到来的挑战。
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短期负荷预测(STLF)由于复杂的时间序列(TS)是一种表达三个季节性模式和非线性趋势的挑战。本文提出了一种新的混合分层深度学习模型,涉及多个季节性,并产生两点预测和预测间隔(PIS)。它结合了指数平滑(ES)和经常性神经网络(RNN)。 ES动态提取每个单独的TS的主要组件,并启用在飞行的临时化,这在相对较小的数据集上操作时特别有用。多层RNN配备了一种新型扩张的经常性电池,旨在有效地模拟TS中的短期和长期依赖性。为了改善内部TS表示,因此模型的性能,RNN同时学习ES参数和主要映射函数将输入转换为预测。我们比较我们对几种基线方法的方法,包括古典统计方法和机器学习(ML)方法,在35个欧洲国家的STLF问题。实证研究清楚地表明,该模型具有高表现力,以解决非线性随机预测问题,包括多个季节性和显着的随机波动。实际上,它在准确性方面优于统计和最先进的ML模型。
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电力是一种波动的电源,需要短期和长期的精力计划和资源管理。更具体地说,在短期,准确的即时能源消耗中,预测极大地提高了建筑物的效率,为采用可再生能源提供了新的途径。在这方面,数据驱动的方法,即基于机器学习的方法,开始优先于更传统的方法,因为它们不仅提供了更简化的部署方式,而且还提供了最新的结果。从这个意义上讲,这项工作应用和比较了几种深度学习算法,LSTM,CNN,CNN-LSTM和TCN的性能,在制造业内的一个真实测试中。实验结果表明,TCN是预测短期即时能源消耗的最可靠方法。
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在本文中,我们提出了一种基于短期内存网络的长期方法,以根据过去的测量值预测公共建筑物的能源消耗。我们的方法包括三个主要步骤:数据处理步骤,培训和验证步骤,最后是预测步骤。我们在一个数据集上测试了我们的方法,该数据集由英国国家档案馆的主要建筑物的主要建筑物,在KEW中,作为评估指标,我们使用了平均绝对错误(MAE)和平均绝对百分比错误(Mape)。
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变压器已成为自然语言处理(NLP)字段中的De-Facto标准。他们也在计算机视觉和其他域中获得了势头。变形金刚可以使人工智能(AI)模型能够动态地关注其输入的某些部分,因此更有效地关注某些部分。灵感来自变形金刚的成功,我们采用了这种技术来预测在多个视野中的战略飞行偏离需求。这项工作是为了支持斜切式的移动应用程序,PAIR,将预测的偏离需求显示为通用航空(GA)飞行运营商,因此他们可以更好地了解繁忙时期离开延误潜力的意识。涉及Pacer以前设计的基于规则的预测方法的现场示范表明,离职需求的预测准确性仍然具有改进的空间。本研究致力于提高来自两个关键方面的预测精度:更好的数据源和鲁棒预测算法。我们利用了两个数据来源,航空系统性能指标(ASPM)和系统广播信息管理(游泳)作为我们的输入。然后,我们用时间融合变压器(TFT)接受了预测的预测模型,用于五个不同的机场。案例研究表明,TFT通过大幅度的传统预测方法可以更好地表现优于传统的预测方法,它们可以在各种机场和更好的解释性方面导致更好的预测。
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就微电网的运行而言,最佳调度是必须考虑的至关重要问题。在这方面,本文提出了一个有效的框架,用于考虑储能设备,风力涡轮机,微型涡轮机的最佳计划可再生微电网。由于微电网操作问题的非线性和复杂性,使用准确且可靠的优化技术有效解决此问题至关重要。为此,在拟议的框架中,基于教师学习的优化可有效地解决系统中的调度问题。此外,提出了基于双向长期短期记忆的深度学习模型,以解决短期风能预测问题。使用IEEE 33-BUS测试系统检查了建议的框架的可行性和性能以及风力预测对操作效率的影响。此外,澳大利亚羊毛北风现场数据被用作现实世界数据集,以评估预测模型的性能。结果表明,在微电网的最佳计划中,提出的框架的有效性能有效。
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随着高级数字技术的蓬勃发展,用户以及能源分销商有可能获得有关家庭用电的详细信息。这些技术也可以用来预测家庭用电量(又称负载)。在本文中,我们研究了变分模式分解和深度学习技术的使用,以提高负载预测问题的准确性。尽管在文献中已经研究了这个问题,但选择适当的分解水平和提供更好预测性能的深度学习技术的关注较少。这项研究通过研究六个分解水平和五个不同的深度学习网络的影响来弥合这一差距。首先,使用变分模式分解将原始负载轮廓分解为固有模式函数,以减轻其非平稳方面。然后,白天,小时和过去的电力消耗数据作为三维输入序列馈送到四级小波分解网络模型。最后,将与不同固有模式函数相关的预测序列组合在一起以形成聚合预测序列。使用摩洛哥建筑物的电力消耗数据集(MORED)的五个摩洛哥家庭的负载曲线评估了该方法,并根据最新的时间序列模型和基线持久性模型进行了基准测试。
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Wind power forecasting helps with the planning for the power systems by contributing to having a higher level of certainty in decision-making. Due to the randomness inherent to meteorological events (e.g., wind speeds), making highly accurate long-term predictions for wind power can be extremely difficult. One approach to remedy this challenge is to utilize weather information from multiple points across a geographical grid to obtain a holistic view of the wind patterns, along with temporal information from the previous power outputs of the wind farms. Our proposed CNN-RNN architecture combines convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to extract spatial and temporal information from multi-dimensional input data to make day-ahead predictions. In this regard, our method incorporates an ultra-wide learning view, combining data from multiple numerical weather prediction models, wind farms, and geographical locations. Additionally, we experiment with global forecasting approaches to understand the impact of training the same model over the datasets obtained from multiple different wind farms, and we employ a method where spatial information extracted from convolutional layers is passed to a tree ensemble (e.g., Light Gradient Boosting Machine (LGBM)) instead of fully connected layers. The results show that our proposed CNN-RNN architecture outperforms other models such as LGBM, Extra Tree regressor and linear regression when trained globally, but fails to replicate such performance when trained individually on each farm. We also observe that passing the spatial information from CNN to LGBM improves its performance, providing further evidence of CNN's spatial feature extraction capabilities.
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Algorithms that involve both forecasting and optimization are at the core of solutions to many difficult real-world problems, such as in supply chains (inventory optimization), traffic, and in the transition towards carbon-free energy generation in battery/load/production scheduling in sustainable energy systems. Typically, in these scenarios we want to solve an optimization problem that depends on unknown future values, which therefore need to be forecast. As both forecasting and optimization are difficult problems in their own right, relatively few research has been done in this area. This paper presents the findings of the ``IEEE-CIS Technical Challenge on Predict+Optimize for Renewable Energy Scheduling," held in 2021. We present a comparison and evaluation of the seven highest-ranked solutions in the competition, to provide researchers with a benchmark problem and to establish the state of the art for this benchmark, with the aim to foster and facilitate research in this area. The competition used data from the Monash Microgrid, as well as weather data and energy market data. It then focused on two main challenges: forecasting renewable energy production and demand, and obtaining an optimal schedule for the activities (lectures) and on-site batteries that lead to the lowest cost of energy. The most accurate forecasts were obtained by gradient-boosted tree and random forest models, and optimization was mostly performed using mixed integer linear and quadratic programming. The winning method predicted different scenarios and optimized over all scenarios jointly using a sample average approximation method.
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As ride-hailing services become increasingly popular, being able to accurately predict demand for such services can help operators efficiently allocate drivers to customers, and reduce idle time, improve congestion, and enhance the passenger experience. This paper proposes UberNet, a deep learning Convolutional Neural Network for short-term prediction of demand for ride-hailing services. UberNet empploys a multivariate framework that utilises a number of temporal and spatial features that have been found in the literature to explain demand for ride-hailing services. The proposed model includes two sub-networks that aim to encode the source series of various features and decode the predicting series, respectively. To assess the performance and effectiveness of UberNet, we use 9 months of Uber pickup data in 2014 and 28 spatial and temporal features from New York City. By comparing the performance of UberNet with several other approaches, we show that the prediction quality of the model is highly competitive. Further, Ubernet's prediction performance is better when using economic, social and built environment features. This suggests that Ubernet is more naturally suited to including complex motivators in making real-time passenger demand predictions for ride-hailing services.
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电力公用事业公司依靠短期需求预测,以期待重大变化的预期调整生产和分配。该系统审查分析了2000年至2019年之间的学术期刊上发布的240份作品,专注于将人工智能(AI),统计和混合模型应用于短期负荷预测(STLF)。这项工作代表了迄今为止对该主题的最全面的审查。进行了对文献的完整分析,以确定最流行和最准确的技术以及现有的空隙。研究结果表明,尽管人工神经网络(ANN)继续成为最常用的独立技术,但研究人员已经超出了不同技术的混合组合,以利用各种方法的组合优势。审查表明,这些混合组合通常可以实现超过99%的预测精度。短期预测最成功的持续时间已被识别为每小时间隔的一天的预测。审查已确定访问培训模型所需的数据集的不足。在亚洲,欧洲,北美和澳大利亚以外的研究区域中已经确定了一个显着差距。
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公共收费站占用预测在开发智能充电策略方面发挥了重要意义,以减少电动车辆(EV)操作员和用户不便。然而,现有研究主要基于具有有限的准确度的传统经济学或时间序列方法。我们提出了一种新的混合长期内记忆神经网络,其包括历史充电状态序列和时间相关的特征,用于多步离散充电占用状态预测。与现有的LSTM网络不同,所提出的模型将不同类型的特征分开,并用混合神经网络架构处理它们。该模型与许多最先进的机器学习和深度学习方法进行了比较,基于从英国邓迪市的开放数据门户网站获得的EV充电数据。结果表明,该方法分别产生非常准确的预测(99.99%和81.87%,分别前进(10分钟)和6个步骤(1小时),优于基准接近的(+ 22.4%)前方预测和6步前方的预测和6.2%)。进行灵敏度分析,以评估模型参数对预测精度的影响。
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