We propose a novel demand response framework based on two similarities in building electricity consumption patterns for curtailment service providers (CSP)s to optimally manage the aggregated building energy. We find the first similarity in spatio-temporal correlations of building demand. To avoid conflicts of interests among buildings, we synthesize building demand and renewable energy scenarios based on that similarity by using the modified generalized dynamic factor model. We find the second similarity in hidden cost functions of discomfort indices for temperature and light. The cost function of a new building is extracted through the collaborative filtering based on the second similarity. Extracted cost functions allow us to align electricity and discomfort costs in the objective function and to cluster buildings to reduce computation time. Two similarities are used to improve the co-optimized day-ahead and real-time market decision process and respond to regulation signals through a two-stage stochastic optimization. Based on building energy statistics, we verify the optimality, efficiency, and comfortability of our strategy by showing that it has a lower cost, longer time period when residents feel comfortable, and lower computation time than existing strategies, particularly when a new building is aggregated.

中文翻译：

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通过协同过滤和广义动态因子模型为削减服务提供商提供基于相似性的优化框架

我们基于建筑用电模式的两个相似性提出了一种新颖的需求响应框架，供削减服务提供商 (CSP) 优化管理聚合建筑能源。我们在建筑需求的时空相关性中发现了第一个相似性。为了避免建筑物之间的利益冲突，我们通过使用改进的广义动态因素模型，基于相似性综合了建筑需求和可再生能源情景。我们发现温度和光的不适指数的隐藏成本函数的第二个相似性。通过基于二次相似度的协同过滤提取新建筑物的成本函数。提取的成本函数使我们能够在目标函数中对齐电力和不适成本，并将建筑物聚类以减少计算时间。两个相似之处用于改进共同优化的日前和实时市场决策过程，并通过两阶段随机优化来响应监管信号。基于建筑能源统计数据，我们验证了我们的策略的最优性、效率和舒适性，证明它比现有策略具有更低的成本、更长的居民感到舒适的时间和更少的计算时间，特别是当新建筑聚合时.