Predicting the Power Load of a Market Area in Italy at the Seasonal Scale

Estimating power load, a crucial variable, is essential for optimizing power grid management, especially when forecasts are made months in advance. Weather conditions significantly influence power load; for example, high temperatures lead to increased energy demand for cooling during the summer. Utilizing seasonal weather forecasts to predict future power load represents a promising research direction in this field.

This study utilizes the European Centre for Medium-Range Weather Forecasts (ECMWF) SEAS5 model, which is currently one of the most advanced in seasonal forecasting, to predict power load in a large region of Italy. Given the coarse spatial resolution (~30 km) of SEAS5, developing an application that forecasts the monthly aggregated power load for a large region such as the North Italy market area was appropriate. The method involves calculating degree-days from the predicted temperature and other predictors and then employing a multiple linear regression model to estimate the power load.

The monthly aggregated power load for North Italy is estimated using seasonal forecast data from the ECMWF SEAS5 model at a 0.25° resolution, covering the period from July 2017 to June 2024. The ECMWF SEAS5 system has been providing operational forecasts since 2017, and forecasts are made for horizons ranging from 2 to 7 months ahead. The earlier period (1993-2016) is used for bias correction of the SEAS5 forecasts by comparing them with the ERA5 reanalysis dataset.

Measured load data are retrieved from the European Network of Transmission System Operators for Electricity (ENTSO-E) portal. The data from 2020 are excluded, as they are considered an anomaly, to avoid negatively impacting the training of the forecasting system.

Daily forecast data, predicted 2 to 7 months in advance, are used to calculate degree days and other predictors, which are then translated into predicted power load on a seasonal scale by the multi-linear regression. While daily forecasts at the seasonal scale typically exhibit very low or no skill, we managed to retain some skill by aggregating them over a one-month period. Specifically, this application used forecasts with daily time resolution to estimate monthly cumulative degree days derived from the SEAS5 model data.

The meteorological variables considered include daily maximum and minimum temperatures as well as daily cumulative solar irradiance, spatially aggregated for the area of interest (Northern Italy). To calculate Heating Degree Days (HDD) and Cooling Degree Days (CDD), thresholds of 18°C and 21°C, respectively, were used, reflecting the characteristics of the selected region.

The load forecasting system was evaluated using commonly used metrics, including the mean absolute percentage error (MAPE), mean error, and correlation. The system demonstrates highly promising results, proving to be more skillful up to 7 months ahead compared to climatology and persistency approaches. In these alternative methods, mean meteorological data are used as predictors instead of SEAS5 data (climatology), or the previous year's monthly load observations are directly used as load predictions (persistency).