中期多分辨率电力负荷预测：利用经济数据与基础模型
Medium-Term Multi-Resolution Electric Load Forecasting using Economic Data and Foundation Model
另 5 家 · 查看机构详情
- Atos（源讯公司）
- EDF（法国电力公司）
- Laboratoire des Sciences du Climat & de l’Environnement（气候与环境科学实验室）
- CEA（法国原子能和替代能源委员会）
- CNRS（法国国家科学研究中心）
- UVSQ（凡尔赛大学）
- Université Paris-Saclay（巴黎萨克雷大学）
- Laboratoire de Mathématiques d’Orsay（奥赛数学实验室）
机构由 AI 辅助整理，请以论文原文为准。
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中文总结 AI 辅助
本研究利用表格基础模型，结合经济数据，对法国中期电力负荷进行多分辨率预测，通过特征选择集成方法将预测技能提升20%，并揭示经济特征重要性随视界增长，弥合了中期与长期预测的差距。
中文摘要 AI 辅助
准确的中期（从几个月到几年）电力负荷预测对于发电厂维护调度、负荷调度和电价结算中的明智决策至关重要。中期负荷预测（MTLF）介于长期负荷预测（LTLF）和短期负荷预测（STLF）之间，前者主要使用经济预测和电器发展情景，后者则由天气、日历和自回归模式驱动，因此中期负荷预测需要同时具备外推能力和变异性建模能力。然而，中期负荷预测能否受益于经济指标，尤其是在哪个预测视界和分辨率下受益，仍不清楚。为解决这些挑战，我们研究了社会经济数据对法国以月和日为分辨率、提前1个月至48个月预测的影响，使用了一个表格基础模型（FM）。为研究创建了一个覆盖20年电力负荷、天气变量以及经济特征（如消费价格指数、生产指数、电动汽车数量或就业情况）观测值的数据集。为避免噪声数据，我们使用了一个新的特征选择流程，创建了具有多样化特征子集的专家模型集成，以证明所选经济协变量在2015-2025年间将预测技能提高了20%。这一提升在提前期和分辨率上保持稳定，将月度粒度的平均绝对百分比误差限制在4%，日度粒度为5%。通过特征和上下文重要性研究了模型的可解释性。结果表明，基础模型在其利用的上下文方面存在局限性，这表明通过减少上下文可能节省计算成本，而经济预测因子的特征重要性随预测视界增长而增加。这表明在中期负荷预测中包含经济数据可以弥合与长期负荷预测的差距，从而实现无缝预测。
英文摘要
Accurate medium-term, from a few months to a few years, electricity load forecasts are crucial for informed decision-making in power plant maintenance scheduling, load dispatch and price settlement. Being comprised between Long-Term Load Forecasting (LTLF) which uses mostly economic projections and appliances development scenarios, and Short-Term Load Forecasting (STLF) driven by weather, calendar and autoregressive patterns, Medium-Term Load Forecasting (MTLF) requires both extrapolation capabilities and variability modeling. Yet, it remains unclear if MTLF can benefit from economic indicators, and especially at which forecast horizon and resolution. To address these challenges we investigated the impact of socioeconomic data on predictions issued 1 month and up to 48 months in advance for France at monthly and daily resolution using a tabular Foundation Model (FM). A dataset covering 20 years of observations of electricity load, weather variables and economic features such as consumer price and production indices, electric vehicle counts or employment is created for the study. To avoid noisy data, we used a new feature selection pipeline, creating ensemble of expert models with diverse feature subsets, to demonstrate that selected economic covariates improve forecast skill by 20% over 2015-2025. This enhancement is steady across lead times and resolutions limiting the Mean Absolute Percentage Error to 4% for monthly granularity and 5% for daily granularity. Explainability of the models is investigated through feature and context importance. Results showed that the FM is limited in the context it leverages pointing towards potential computational savings with a reduced context, while feature importance of economic predictors grows with the forecast horizon. This suggests that including economic data in MTLF could bridge the gap with LTLF leading to seamless forecasts.
