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, planning and building energy storage systems, 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, capturing the multifaceted characteristics of load, including daily, weekly and annual seasonal patterns, as well as autoregressive effects, weather and holiday impacts, and socio-economic non-stationarities, presents significant modeling challenges. To address these challenges, we propose a novel forecasting method using Generalized Additive Models (GAMs) built from interpretable P-splines that is enhanced with autoregressive post-processing. This model incorporates smoothed temperatures, Error-Trend-Seasonal (ETS) modeled and persistently forecasted non-stationary socio-economic states, a nuanced representation of effects from vacation periods, fixed date and weekday holidays, and seasonal information as inputs. The proposed model is evaluated using load data from 24 European countries over more than 9 years (2015-2024). 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 Transmission System Operator (TSO) forecasts, with computation times of just a few seconds for several years of hourly data, underscores the potential of the model for practical application in the power system industry.

翻译：准确的中期（数周至一年）小时级电力负荷预测对于电厂运营的战略决策、保障供电安全与电网稳定、规划建设储能系统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，但中期预测方案仍然稀缺。在中期负荷预测中，如何捕捉负荷的多维度特征——包括日度、周度和年度季节性模式，以及自回归效应、天气与节假日影响、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、经误差-趋势-季节性模型处理并持续预测的非平稳社会经济状态、精细化表征的假期时段效应、固定日期与工作日节假日以及季节性信息作为输入。使用来自24个欧洲国家超过9年（2015-2024）的负荷数据对所提模型进行评估。分析表明，该模型不仅相较于现有先进方法显著提升了预测精度，且因其完全可解释性，能够为各分项对预测负荷的影响机制提供有价值的洞见。该模型在长达数年的小时数据上仅需数秒计算时间即可达到与日前输电系统运营商预测相当的精度，凸显了其在电力系统行业实际应用的潜力。