这项研究提供了一个新颖的框架，以根据开源数据估算全球城市的公共交通巴士的经济，环境和社会价值。电动巴士是替代柴油巴士以获得环境和社会利益的引人注目的候选人。但是，评估总线电气化价值的最先进模型的适用性受到限制，因为它们需要可能难以购买的总线运营数据的细粒和定制数据。我们的估值工具使用通用过境饲料规范，这是全球运输机构使用的标准数据格式，为制定优先级排序策略提供了高级指导，以使总线机队电气化。我们开发了物理知识的机器学习模型，以评估每种运输途径的能耗，碳排放，健康影响以及总拥有成本。我们通过对大波士顿和米兰大都会地区的公交线路进行案例研究来证明我们的工具的可扩展性。

本文介绍了一种机器学习方法，可以在宏观水平下模拟电动车辆的电力消耗，即在不存在速度轮廓，同时保持微观级别精度。对于这项工作，我们利用了基于代理的代理的运输工具来模拟了在各种场景变化的大芝加哥地区发生的模型旅行，以及基于物理的建模和仿真工具，以提供高保真能量消耗值。产生的结果构成了车辆路径能量结果的非常大的数据集，其捕获车辆和路由设置的可变性，并且掩盖了车速动力学的高保真时间序列。我们表明，尽管掩盖了影响能量消耗的所有内部动态，但是可以以深入的学习方法准确地学习聚合级能量消耗值。当有大规模数据可用，并且仔细量身定制的功能工程，精心设计的模型可以克服和检索潜在信息。该模型已部署并集成在Polaris运输系统仿真工具中，以支持各个充电决策的实时行为运输模型，以及电动车辆的重新排出。

在带有电动车队的乘车系统中，充电是一个复杂的决策过程。大多数电动汽车（EV）出租车服务要求驾驶员做出利己主义决定，从而导致分散的临时充电策略。车辆之间通常缺乏或不共享移动性系统的当前状态，因此无法做出最佳的决定。大多数现有方法都不将时间，位置和持续时间结合到全面的控制算法中，也不适合实时操作。因此，我们提出了一种实时预测性充电方法，用于使用一个名为“闲置时间开发（ITX）”的单个操作员进行乘车服务，该方法预测了车辆闲置并利用这些时期来收获能量的时期。它依靠图形卷积网络和线性分配算法来设计最佳的车辆和充电站配对，以最大程度地提高利用的空闲时间。我们通过对纽约市现实世界数据集的广泛模拟研究评估了我们的方法。结果表明，就货币奖励功能而言，ITX的表现优于所有基线方法至少提高5％（相当于6,000个车辆操作的$ 70,000），该奖励奖励功能的建模旨在复制现实世界中乘车系统的盈利能力。此外，与基线方法相比，ITX可以将延迟至少减少4.68％，并且通常通过促进顾客在整个车队中更好地传播乘客的舒适度。我们的结果还表明，ITX使车辆能够在白天收获能量，稳定电池水平，并增加需求意外激增的弹性。最后，与表现最佳的基线策略相比，峰值负载减少了17.39％，这使网格操作员受益，并为更可持续的电网使用铺平了道路。

作为行业4.0时代的一项新兴技术，数字双胞胎因其承诺进一步优化流程设计，质量控制，健康监测，决策和政策制定等，通过全面对物理世界进行建模，以进一步优化流程设计，质量控制，健康监测，决策和政策，因此获得了前所未有的关注。互连的数字模型。在一系列两部分的论文中，我们研究了不同建模技术，孪生启用技术以及数字双胞胎常用的不确定性量化和优化方法的基本作用。第二篇论文介绍了数字双胞胎的关键启示技术的文献综述，重点是不确定性量化，优化方法，开源数据集和工具，主要发现，挑战和未来方向。讨论的重点是当前的不确定性量化和优化方法，以及如何在数字双胞胎的不同维度中应用它们。此外，本文介绍了一个案例研究，其中构建和测试了电池数字双胞胎，以说明在这两部分评论中回顾的一些建模和孪生方法。 GITHUB上可以找到用于生成案例研究中所有结果和数字的代码和预处理数据。

Ongoing risks from climate change have impacted the livelihood of global nomadic communities, and are likely to lead to increased migratory movements in coming years. As a result, mobility considerations are becoming increasingly important in energy systems planning, particularly to achieve energy access in developing countries. Advanced Plug and Play control strategies have been recently developed with such a decentralized framework in mind, more easily allowing for the interconnection of nomadic communities, both to each other and to the main grid. In light of the above, the design and planning strategy of a mobile multi-energy supply system for a nomadic community is investigated in this work. Motivated by the scale and dimensionality of the associated uncertainties, impacting all major design and decision variables over the 30-year planning horizon, Deep Reinforcement Learning (DRL) is implemented for the design and planning problem tackled. DRL based solutions are benchmarked against several rigid baseline design options to compare expected performance under uncertainty. The results on a case study for ger communities in Mongolia suggest that mobile nomadic energy systems can be both technically and economically feasible, particularly when considering flexibility, although the degree of spatial dispersion among households is an important limiting factor. Key economic, sustainability and resilience indicators such as Cost, Equivalent Emissions and Total Unmet Load are measured, suggesting potential improvements compared to available baselines of up to 25%, 67% and 76%, respectively. Finally, the decomposition of values of flexibility and plug and play operation is presented using a variation of real options theory, with important implications for both nomadic communities and policymakers focused on enabling their energy access.

The high emission and low energy efficiency caused by internal combustion engines (ICE) have become unacceptable under environmental regulations and the energy crisis. As a promising alternative solution, multi-power source electric vehicles (MPS-EVs) introduce different clean energy systems to improve powertrain efficiency. The energy management strategy (EMS) is a critical technology for MPS-EVs to maximize efficiency, fuel economy, and range. Reinforcement learning (RL) has become an effective methodology for the development of EMS. RL has received continuous attention and research, but there is still a lack of systematic analysis of the design elements of RL-based EMS. To this end, this paper presents an in-depth analysis of the current research on RL-based EMS (RL-EMS) and summarizes the design elements of RL-based EMS. This paper first summarizes the previous applications of RL in EMS from five aspects: algorithm, perception scheme, decision scheme, reward function, and innovative training method. The contribution of advanced algorithms to the training effect is shown, the perception and control schemes in the literature are analyzed in detail, different reward function settings are classified, and innovative training methods with their roles are elaborated. Finally, by comparing the development routes of RL and RL-EMS, this paper identifies the gap between advanced RL solutions and existing RL-EMS. Finally, this paper suggests potential development directions for implementing advanced artificial intelligence (AI) solutions in EMS.

如今，世界各地的城市推出了电动公共汽车以优化城市交通，减少当地碳排放量。为了减少碳排放并最大化电动公共汽车的效用，重要的是为它们选择合适的路线很重要。传统上，路线选择是在专用调查的基础上，这在时间和劳动力成本高昂。在本文中，我们主要关注智能规划电动公交线路，具体取决于整个城市各地区的独特需求。我们提出了一种铺张山庄，一个路线规划系统，利用深度神经网络和多层的感知者，以预测未来人民的旅行和整个城市的未来运输碳排放。鉴于人们旅行和运输碳排放的未来信息，我们利用了一种贪婪的机制来推荐将以理想状态离开的电动公交车的公交线路。此外，从异构城市数据集中提取两个神经网络的代表特征。我们通过对珠海省珠海真实世界资源的大量实验来评估我们的方法。结果表明，我们设计的基于神经网络的算法始终如一地优于典型的基线。此外，电动公交车的建议路线有助于降低碳排放的峰值，并充分利用城市的电动公共汽车。

多源机电耦合使燃料电池电动汽车（FCEV）的能源管理相对非线性和复杂，尤其是在4轮驱动（4WD）FCEV的类型中。复杂的非线性系统的准确观察状态是FCEV中出色的能源管理的基础。为了释放FCEV的节能潜力，为4WD FCEV提出了一种基于学习的新型鲁棒模型预测控制（LRMPC）策略，从而有助于多个能源之间的合适功率分布。基于机器学习（ML）的精心设计的策略将非线性系统的知识转化为具有出色稳健性能的显式控制方案。首先，具有高回归准确性和出色概括能力的ML方法是离线训练的，以建立SOC的精确状态观察者。然后，使用国家观察者生成的SOC的显式数据表用于抓住准确的状态更改，其输入功能包括车辆状态和车辆组件状态。具体来说，提供未来速度参考的车辆速度估计是由深森林构建的。接下来，将包括显式数据表和车辆速度估计的组件与模型预测控制（MPC）结合使用，以释放FCEV中多释放系统的最新能源节能能力，其名称是LRMPC。最后，在模拟测试中进行详细评估以验证LRMPC的进步性能。相应的结果突出了LRMPC的最佳控制效应和强大的实时应用能力。

Reinforcement learning-based (RL-based) energy management strategy (EMS) is considered a promising solution for the energy management of electric vehicles with multiple power sources. It has been shown to outperform conventional methods in energy management problems regarding energy-saving and real-time performance. However, previous studies have not systematically examined the essential elements of RL-based EMS. This paper presents an empirical analysis of RL-based EMS in a Plug-in Hybrid Electric Vehicle (PHEV) and Fuel Cell Electric Vehicle (FCEV). The empirical analysis is developed in four aspects: algorithm, perception and decision granularity, hyperparameters, and reward function. The results show that the Off-policy algorithm effectively develops a more fuel-efficient solution within the complete driving cycle compared with other algorithms. Improving the perception and decision granularity does not produce a more desirable energy-saving solution but better balances battery power and fuel consumption. The equivalent energy optimization objective based on the instantaneous state of charge (SOC) variation is parameter sensitive and can help RL-EMSs to achieve more efficient energy-cost strategies.

Efficient energy consumption is crucial for achieving sustainable energy goals in the era of climate change and grid modernization. Thus, it is vital to understand how energy is consumed at finer resolutions such as household in order to plan demand-response events or analyze the impacts of weather, electricity prices, electric vehicles, solar, and occupancy schedules on energy consumption. However, availability and access to detailed energy-use data, which would enable detailed studies, has been rare. In this paper, we release a unique, large-scale, synthetic, residential energy-use dataset for the residential sector across the contiguous United States covering millions of households. The data comprise of hourly energy use profiles for synthetic households, disaggregated into Thermostatically Controlled Loads (TCL) and appliance use. The underlying framework is constructed using a bottom-up approach. Diverse open-source surveys and first principles models are used for end-use modeling. Extensive validation of the synthetic dataset has been conducted through comparisons with reported energy-use data. We present a detailed, open, high-resolution, residential energy-use dataset for the United States.

As various city agencies and mobility operators navigate toward innovative mobility solutions, there is a need for strategic flexibility in well-timed investment decisions in the design and timing of mobility service regions, i.e. cast as "real options" (RO). This problem becomes increasingly challenging with multiple interacting RO in such investments. We propose a scalable machine learning based RO framework for multi-period sequential service region design & timing problem for mobility-on-demand services, framed as a Markov decision process with non-stationary stochastic variables. A value function approximation policy from literature uses multi-option least squares Monte Carlo simulation to get a policy value for a set of interdependent investment decisions as deferral options (CR policy). The goal is to determine the optimal selection and timing of a set of zones to include in a service region. However, prior work required explicit enumeration of all possible sequences of investments. To address the combinatorial complexity of such enumeration, we propose a new variant "deep" RO policy using an efficient recurrent neural network (RNN) based ML method (CR-RNN policy) to sample sequences to forego the need for enumeration, making network design & timing policy tractable for large scale implementation. Experiments on multiple service region scenarios in New York City (NYC) shows the proposed policy substantially reduces the overall computational cost (time reduction for RO evaluation of > 90% of total investment sequences is achieved), with zero to near-zero gap compared to the benchmark. A case study of sequential service region design for expansion of MoD services in Brooklyn, NYC show that using the CR-RNN policy to determine optimal RO investment strategy yields a similar performance (0.5% within CR policy value) with significantly reduced computation time (about 5.4 times faster).

Energy consumption in buildings, both residential and commercial, accounts for approximately 40% of all energy usage in the U.S., and similar numbers are being reported from countries around the world. This significant amount of energy is used to maintain a comfortable, secure, and productive environment for the occupants. So, it is crucial that the energy consumption in buildings must be optimized, all the while maintaining satisfactory levels of occupant comfort, health, and safety. Recently, Machine Learning has been proven to be an invaluable tool in deriving important insights from data and optimizing various systems. In this work, we review the ways in which machine learning has been leveraged to make buildings smart and energy-efficient. For the convenience of readers, we provide a brief introduction of several machine learning paradigms and the components and functioning of each smart building system we cover. Finally, we discuss challenges faced while implementing machine learning algorithms in smart buildings and provide future avenues for research at the intersection of smart buildings and machine learning.

使用机器学习技术校准低成本传感器是现在广泛使用的方法。虽然在部署低成本传感器的空气质量监测的低成本传感器中仍有许多挑战，但低成本传感器已被证明与高精度仪器相结合。因此，大多数研究专注于使用机器学习应用不同的校准技术。然而，这些模型的成功应用取决于传感器获得的数据的质量，并且已经从传感器采样和数据预处理到传感器本身的校准，从传感器采集过程中支付了很少的关注。在本文中，我们展示了主要的传感器采样参数，它们对基于机器学习的传感器校准的质量的相应影响及其对能源消耗的影响，因此显示了现有的权衡。最后，实验节点上的结果显示了数据采样策略在对流层臭氧，二氧化氮和一氧化氮低成本传感器的校准中的影响。具体地，我们展示了如何最小化感测子系统的占空比的采样策略可以降低功耗，同时保持数据质量。

Accurate reporting of energy and carbon usage is essential for understanding the potential climate impacts of machine learning research. We introduce a framework that makes this easier by providing a simple interface for tracking realtime energy consumption and carbon emissions, as well as generating standardized online appendices. Utilizing this framework, we create a leaderboard for energy efficient reinforcement learning algorithms to incentivize responsible research in this area as an example for other areas of machine learning. Finally, based on case studies using our framework, we propose strategies for mitigation of carbon emissions and reduction of energy consumption. By making accounting easier, we hope to further the sustainable development of machine learning experiments and spur more research into energy efficient algorithms.

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。

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.

通过提供前所未有的计算资源访问，云计算能够在机器学习等技术中快速增长，其计算需求产生了高能源成本和相应的碳足迹。结果，最近的奖学金呼吁更好地估计AI的温室气体影响：当今的数据科学家无法轻松或可靠地访问该信息的测量，从而排除了可行策略的发展。向用户提供有关软件碳强度的信息的云提供商是一种基本的垫脚石，以最大程度地减少排放。在本文中，我们提供了一个测量软件碳强度的框架，并建议通过使用每个能量单元使用基于位置和特定时间的边际排放数据来测量运行碳排放。我们为一组自然语言处理和计算机视觉的现代模型提供了操作软件强度的测量，以及各种模型尺寸，包括预处理61亿个参数语言模型。然后，我们评估了一套用于减少Microsoft Azure Cloud Compute平台排放的方法套件：使用不同地理区域中的云实例，在一天中的不同时间使用云实例，并在边际碳强度高于某个阈值时动态暂停云实例。我们证实了先前的结果，即数据中心的地理区域在给定云实例的碳强度中起着重要作用，并发现选择合适的区域可能具有最大的运营排放减少影响。我们还表明，一天中的时间对操作软件碳强度有显着影响。最后，我们最终提出了有关机器学习从业人员如何使用软件碳强度信息来减少环境影响的建议。

预计自动驾驶技术不仅可以提高移动性和道路安全性，还可以提高能源效率的益处。在可预见的未来，自动车辆（AVS）将在与人机车辆共享的道路上运行。为了保持安全性和活力，同时尽量减少能耗，AV规划和决策过程应考虑自动自动驾驶车辆与周围的人机车辆之间的相互作用。在本章中，我们描述了一种通过基于认知层次理论和强化学习开发人的驾驶员行为建模来开发共用道路上的节能自主驾驶政策的框架。

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.

Ridesplitting是合并的骑乘服务的一种形式，具有减轻骑行对环境的负面影响的巨大潜力。但是，大多数现有研究仅根据优化模型和仿真探索其理论环境益处。相比之下，这项研究旨在揭示基于观察到的中国骑车数据的数据及其决定因素的现实世界排放减少。本研究将Trip数据与Copert模型整合在一起，计算了共享乘车的CO2排放量（Ridesplitting）及其取代的单骑行（常规乘车），以估计每次骑行旅行的CO2排放降低。结果表明，并非所有的骑行旅行都减少了现实世界中的骑车的排放。二氧化碳的降低速度降低速率因行程到旅行而异，平均为43.15g/km。然后，应用可解释的机器学习模型，梯度提升机，用于探索二氧化碳排放率降低速度的关系及其决定因素之间的关系。基于Shapley添加剂解释（SHAP）方法，共享乘车的重叠率和弯路率被确定为确定二氧化碳排放率降低乘车率的最重要因素。增加重叠率，共享乘车的数量，平均速度和行驶距离比率，同时降低弯路率，实际行程距离和行驶距离差距可以增加二氧化碳排放率的降低骑行率。另外，通过部分依赖图研究了决定因素的非线性效应和相互作用。总而言之，这项研究为政府和骑车公司提供了一种科学方法，以更好地评估和优化乘车的环境利益。