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• Post-detrending cleanup: Additional outliers detected only after detrending are removed.
• Selection of training and validation periods: For each country, separate time periods were selected for training and validation of the model. Years strongly influenced by external socio-economic anomalies (e.g., 2009 financial crisis or 2020 COVID-19 pandemic) were sometimes excluded to avoid distorting the model.

Table 1.1 summarises the training/validation periods, excluded years, and notes for each country. As mentioned, a total of 42 countries were initially considered; however, 8 countries (highlighted in red in the table) were excluded due to insufficient data coverage, irregular or incoherent distributions, or very short time series that were deemed unsuitable for model training or validation.

Table 1.1: Overview of electricity load data availability and selection by country. 

Image ModifiedImage Modified

Figure 1.1: Examples of preliminary data cleaning for electricity load data, here shown for Germany, Great Britain, Ireland, and Slovenia. For Germany, although data were available for the entire period from 2006 to 2024, only the values from 2015 onwards were retained, as they appeared more reliable and still ensured a sufficiently long historical period for modelling. For Great Britain, the time series was shorter and characterised by a clear jump between 2014 and 2015. While anomalously low values were removed, additional adjustments were required to realign the two segments of the time series. In the case of Ireland, all available data were initially retained, as the series showed a consistent and coherent structure throughout. For Slovenia, the data showed an abrupt degradation in quality starting in 2020. In this case, only the period up to 2019 was used for model calibration and validation, discarding the more recent, less reliable values.

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The daily electricity demand

Y_t

​ is expressed as:

Y_t = \beta_0 + \sum_{i=1}^{n} f_i(X_{i,t}) + \varepsilon_t

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is the detrended daily electricity load at day t, 

\beta_0

​ is the intercept, 

f_i(\cdot)

...

and 

\varepsilon_t

​ is the residual error term. 

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f_j(x_j) = \sum_{q=1}^{k_j} b_{j,q}(x_j) \, \beta_{j,q}

with

b_{j,q}

​ being the spline basis functions and

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Figure 1.5: Top: Time series of actual vs. predicted load in France from 2008 to 2014, comparing the GAM model (green) with the actual load (red) and a simplified model (dashed blue).
Bottom: Daily percentage error, computed as the difference between the green and red curves in the top panel, over the same period.

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The Electricity Demand Model estimates electricity demand time series for 34 European countries, aggregated at the national level (ADM0). Data are available at daily, monthly, seasonal, and annual resolutions, following the Temporal Aggregation Procedure.

Energy Demand Model (Global Domain)

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1. Monthly climatology calculation over a selected reference period (e.g. 2000-2019):
   

   Mathdisplay
   \text{climatology}_m = \frac{\sum_{y=1}^N x_m^y}{N}

   where x is the HDD or CDD value for month m and year y, N is the number of years (e.g. 20).
2. Spatial aggregation to national level (ADM0) (for more details on this please refer to Spatial Aggregation Procedure).
3. Weighting by population to ensure that the indicator is representative of the amount of people actually living in that area.
4. Summation of HDD and CDD to produce the final EDD index:
   

   Mathdisplay
   EDD = HDD_{weighted} + CDD_{weighted}

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Table 2.1 Minima and maxima of HDD and CDD bias (C3S – IEA) for selected months (January, April, July and October). 

Table 2.2: Pearson correlation coefficients among the ENTSO-E, IEA loads and the EDD proxy for selected countries.

Output Data

The model provides national-level (ADM0) time series of Heating Degree Days (HDD), Cooling Degree Days (CDD), and their sum (Energy Degree Days, EDD). Data are available at monthly, seasonal, and annual resolutions, following the Temporal Aggregation Procedure.

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For the references, please refer to the References section in the Product User Guide.

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