Structural statistical learning of heterogeneous preferences for smart energy choices with a case study on coordinated electric vehicle charging

该项目正在开发用户友好的工具，以指导清洁能源的最佳选择，特别是在充电电动汽车（ev）方面。他们的工作重点是创建简单高效的计算机算法，能够从现实世界的数据中学习，为电动汽车提供理想的充电时间和条件。其意义在于用户适应日益复杂的能源环境，采取更可持续的选择和行为，获得对能源使用的更多控制。该项目以电动汽车为中心，将其作为减少交通运输碳排放的组成部分。尽管电池和充电技术取得了重大进步，但在规划电动汽车充电方式方面仍存在许多挑战，而且随着电气化交通的预期普及，这种情况将会恶化。其目的是确保所有电动汽车用户都能使用这些工具，重点不仅在于提出促进可再生能源使用的充电建议，还在于考虑个人偏好和对整个能源系统的潜在好处。最终目标是简化决策，使用户更容易以最佳方式管理能源消耗，而不会感到不知所措。采用先进的统计技术，利用高效的计算机算法，确保速度、灵活性、可解释性和现实世界的政策相关性。该系统可实时适应和学习充电偏好，在考虑电力需求、定价和电网稳定性等因素的情况下，为电动汽车协调充电提供能源管理建议。本文提出的激励兼容推荐系统的系统模块化马尔可夫链快速可扩展采样器是通过集成：扩展Ultimate Pólya-Gamma数据扩展，用于多指标选择模型，以创建不存在的共轭，用于效率的平销和非因式变分推理，用于跨服务特征的复杂非参数权衡的Choquet聚合（放松线性补偿行为），贝叶斯内生性控制，用于调整超参数的贝叶斯优化以及与系统平衡算法的集成。以及战略代理人之间和内部偏好和动机异质性的灵活半参数表示。研究团队通过一个实际的住宅项目来实现电动汽车充电的协调调度，例如通过离散目标束的自我选择价格歧视来避免有效的价格内生性和约束。简单地说，就是为驾驶电动汽车的个人创建一个有用的指南，根据他们的喜好和驾驶模式，建议理想的充电时间和需求。采用智能技术收集信息，为用户提供个性化建议。该项目的影响超出了个人援助，有助于能源系统的整体可持续性，并为能源相关组织、公用事业和政策制定者提供有价值的工具。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This project is developing user-friendly tools to guide optimal choices about clean energy, especially regarding charging electric vehicles (EVs). Efforts focus on creating simple and efficient computer algorithms capable of learning from real-world data to suggest ideal charging times and conditions for EVs. The significance lies in users adapting to increasingly complex energy environments, adopting more sustainable options and behaviors, and gaining more control over energy use. The project centers on EVs as integral to reducing carbon emissions from transportation. Despite significant advancements in battery and charging technologies, there are many challenges in planning how EVs are charged, and the situation is poised to worsen with the expected increasing penetration of electrified transportation. The aim is to ensure these tools will be accessible to all EV users, focusing not only on making charging recommendations that promote the use of renewable energy but also on considering individual preferences and potential benefits to the entire energy system. The ultimate objective is to simplify decision-making, making it easier for users to manage energy consumption in optimal ways without feeling overwhelmed. Advanced statistical techniques are employed, utilizing highly efficient computer algorithms that will ensure speed, flexibility, interpretability, and real-world policy relevance. The proposed system adapts and learns charging preferences in real-time, providing energy management recommendations in the context of coordinated EV charging while considering factors such as electricity demand, pricing, and grid stability. The methodical modular Markov chain fast and scalable sampler of the proposed incentive-compatible recommender system is designed by integrating: an expansion of Ultimate Pólya-Gamma data augmentation for multi-index choice models to create conjugacy where it does not exist, amortized and non-factorized variational inference for efficiency, Choquet aggregation for complex nonparametric tradeoffs across service features (relaxing linear compensatory behavior), Bayes endogeneity controls, Bayesian optimization for tuning hyperparameters and for integration with a system equilibrium algorithm, and flexible semiparametric representation of heterogeneity in preferences and motives between and within strategic agents. The research team implements the project through an actual residential program for coordinated scheduling of electric-vehicle charging, such as avoiding in-force price endogeneity and constraints via self-selection price discrimination through discrete targeted bundles. In simpler terms, a helpful guide is created for individuals driving electric vehicles, suggesting ideal charging times and needs based on their preferences and driving patterns. Smart technology is employed to gather information, enabling the provision of personalized suggestions to users. The impact of the project extends beyond individual assistance, contributing to the overall sustainability of energy systems and providing valuable tools for energy-related organizations, utilities, and policymakers.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.