  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.
