Electricity is difficult to store, except at prohibitive cost, and therefore the balance between generation and load must be maintained at all times. Electricity is traditionally managed by anticipating demand and intermittent production (wind, solar) and matching flexible production (hydro, nuclear, coal and gas). Accurate forecasting of electricity load and renewable production is therefore essential to ensure grid performance and stability. Both are highly dependent on meteorological variables (temperature, wind, sunshine). These dependencies are complex and difficult to model. On the one hand, spatial variations do not have a uniform impact because population, industry, and wind and solar farms are not evenly distributed across the territory. On the other hand, temporal variations can have delayed effects on load (due to the thermal inertia of buildings). With access to observations from different weather stations and simulated data from meteorological models, we believe that both phenomena can be modeled together. In today's state-of-the-art load forecasting models, the spatio-temporal modeling of the weather is fixed. In this work, we aim to take advantage of the automated representation and spatio-temporal feature extraction capabilities of deep neural networks to improve spatio-temporal weather modeling for load forecasting. We compare our deep learning-based methodology with the state-of-the-art on French national load. This methodology could also be fully adapted to forecasting renewable energy production.

翻译：电力难以储存，除非付出高昂成本，因此发电与负荷之间的平衡必须时刻保持。传统电力管理通过预测需求与间歇性发电（风能、太阳能），并调节灵活性发电（水电、核电、煤电与燃气发电）来实现平衡。因此，准确预测电力负荷与可再生能源发电量对保障电网性能与稳定至关重要。两者均高度依赖气象变量（温度、风速、日照强度）。这些依赖关系复杂且难以建模：一方面，空间变化的影响并不均匀，因为人口、工业及风光电站的分布存在地域差异；另一方面，时间变化可能对负荷产生延迟效应（源于建筑物的热惯性）。通过整合多气象站观测数据与气象模型模拟数据，我们认为这两种现象可被协同建模。当前最先进的负荷预测模型中，天气的时空建模方式是固定的。本研究旨在利用深度神经网络自动表征与时空特征提取的能力，改进负荷预测中的时空天气建模。我们将基于深度学习的方法与法国国家负荷预测的现有最优模型进行对比。该方法亦可完全适用于可再生能源发电量预测。