GCA-BULF: A Bottom-Up Framework for Short-Term Load Forecasting Using Grouped Critical Appliances

GCA-BULF：一种基于关键电器分组的自下而上短期负荷预测框架

Abstract: With the rise of time-of-use and tiered electricity pricing, energy consumers are encouraged to adopt peak-shifting strategies by automatically controlling high-power appliances. These help lower energy costs while enhancing the power grid’s stability. To support such energy management with high resilience and responsiveness, reliable short-term load forecasting (STLF) plays a critical role.

摘要： 随着分时电价和阶梯电价的兴起，能源用户被鼓励通过自动控制高功率电器来采取削峰填谷策略。这些措施有助于降低能源成本，同时增强电网的稳定性。为了支持这种具有高弹性和响应能力的能源管理，可靠的短期负荷预测（STLF）发挥着至关重要的作用。

STLF predicts electricity consumption over time horizons ranging from minutes to days, using historical data, temporal patterns, and contextual factors. Traditional top-down forecasting methods struggle to capture the complex consumption patterns of diverse and mixed appliance loads. Although bottom-up methods improve forecasting accuracy by integrating appliance-level data, monitoring all appliances is costly, and many do not meaningfully impact total load prediction.

STLF 利用历史数据、时间模式和上下文因素，预测从几分钟到几天不等的时间跨度内的电力消耗。传统的自上而下预测方法难以捕捉多样化和混合电器负荷的复杂消耗模式。尽管自下而上的方法通过整合电器级数据提高了预测精度，但监测所有电器成本高昂，且许多电器对总负荷预测并无显著影响。

Therefore, we propose GCA-BULF, a bottom-up short-term load forecasting framework based on grouped critical appliances, supported by three key designs. First, the Critical Appliance Filtering module ranks appliances according to their power consumption, switching frequency, and usage pattern periodicity, and identifies critical ones through iterative load decomposition.

因此，我们提出了 GCA-BULF，这是一种基于关键电器分组的自下而上短期负荷预测框架，并由三个关键设计提供支持。首先，“关键电器过滤”模块根据电器的功耗、开关频率和使用模式周期性对电器进行排序，并通过迭代负荷分解识别出关键电器。

Next, the Related Appliance Grouping module clusters these appliances based on spatial and temporal correlations for group-level forecasting. Finally, the Collaborative Load Forecasting module refines the total load prediction by combining multiple group-level forecasts.

接下来，“相关电器分组”模块根据空间和时间相关性对这些电器进行聚类，以进行组级预测。最后，“协同负荷预测”模块通过结合多个组级预测结果来优化总负荷预测。

We evaluate GCA-BULF on residential and office building load forecasting tasks. Experimental results reveal that GCA-BULF improves hourly total load forecasting by 20.85%-57.88% compared to existing top-down methods and by 33.03%-92.48% compared to bottom-up methods.

我们在住宅和办公楼的负荷预测任务上对 GCA-BULF 进行了评估。实验结果表明，与现有的自上而下方法相比，GCA-BULF 将小时总负荷预测精度提高了 20.85%-57.88%；与自下而上的方法相比，提高了 33.03%-92.48%。