Accurate mid-term (weeks to one year) hourly electricity load forecasts are essential for strategic decision-making in power plant operation, ensuring supply security and grid stability, and energy trading. While numerous models effectively predict short-term (hours to a few days) hourly load, mid-term forecasting solutions remain scarce. In mid-term load forecasting, besides daily, weekly, and annual seasonal and autoregressive effects, capturing weather and holiday effects, as well as socio-economic non-stationarities in the data, poses significant modeling challenges. To address these challenges, we propose a novel forecasting method using Generalized Additive Models (GAMs) built from interpretable P-splines and enhanced with autoregressive post-processing. This model uses smoothed temperatures, Error-Trend-Seasonal (ETS) modeled non-stationary states, a nuanced representation of holiday effects with weekday variations, and seasonal information as input. The proposed model is evaluated on load data from 24 European countries. This analysis demonstrates that the model not only has significantly enhanced forecasting accuracy compared to state-of-the-art methods but also offers valuable insights into the influence of individual components on predicted load, given its full interpretability. Achieving performance akin to day-ahead TSO forecasts in fast computation times of a few seconds for several years of hourly data underscores the model's potential for practical application in the power system industry.

翻译：精确的中期（数周至一年）小时级电力负荷预测对于发电厂运营的战略决策、保障供电安全与电网稳定以及能源交易至关重要。尽管已有众多模型能有效预测短期（数小时至数天）的小时负荷，但中期预测解决方案仍然稀缺。在中期负荷预测中，除了日度、周度和年度的季节性及自回归效应外，如何捕捉天气与节假日效应以及数据中的社会经济非平稳性，构成了显著的建模挑战。为应对这些挑战，我们提出一种新颖的预测方法，该方法采用基于可解释P样条构建的广义可加模型，并通过自回归后处理进行增强。该模型以平滑化温度、误差-趋势-季节性模型描述的非平稳状态、包含工作日差异的精细化节假日效应表征以及季节性信息作为输入。所提模型在24个欧洲国家的负荷数据上进行了评估。分析表明，得益于其完全可解释性，该模型不仅较现有先进方法显著提升了预测精度，还能为各分项对预测负荷的影响提供有价值的洞见。该模型能在数秒内对多年小时级数据完成快速计算，其预测性能媲美日前输电系统运营商预测水平，这凸显了其在电力系统行业实际应用的潜力。