Weather Year Modeling

STRIDE uses detailed weather data to adjust electricity load shapes for temperature variations throughout the year. This page explains how weather data are processed and applied to create realistic hourly load profiles.

Overview

Weather-based load adjustments follow this workflow:

Weather BAIT Data
    ↓
Degree Day Calculation (HDD/CDD)
    ↓
Shoulder Month Smoothing (Adjusted HDD/CDD)
    ↓
Temperature Multipliers
    ↓
Load Shape Expansion (Representative → Full Year)
    ↓
Annual Energy Scaling
    ↓
Final Hourly Load Shapes

Input weather data

STRIDE uses Building-Adjusted Integrated Temperature (BAIT) data derived from ERA5 reanalysis weather data. BAIT is a composite temperature metric that accounts for:

• Outdoor dry-bulb temperature

• Surface solar radiation

• Wind speed at 2m

• Humidity

• Building thermal characteristics

The calculation methodology is similar to that described in Staffell, Pfenninger, and Johnson (2023).[1]

The weather data includes:

• Temporal resolution: Daily (averaged from hourly ERA5 data)

• Coverage: Weather years 1995-2024

• Geographic representation: Country-level, based on a single highly or most-populous location per country

• Variables: Temperature, Solar Radiation, Wind Speed, Dew Point, Humidity, BAIT

Degree day calculation

Heating and cooling degree days

Degree days quantify how much heating or cooling is needed on a given day:

Heating Degree Days (HDD):

HDD = GREATEST(0, heating_threshold - BAIT)

Cooling Degree Days (CDD):

CDD = GREATEST(0, BAIT - cooling_threshold)

ModelParameters

These thresholds are configurable through ModelParameters:

Parameter	Description	Default	Unit
heating_threshold	Temperature below which heating is needed	18.0	°C
cooling_threshold	Temperature above which cooling is needed	18.0	°C

Example configuration in project.json5:

{
  project_id: "my_project",
  // ... other config ...
  model_parameters: {
    heating_threshold: 18.0,
    cooling_threshold: 18.0,
  }
}

Degree day grouping

Degree days are aggregated by:

• Geography: Country or region

• Weather Year: Reference year for weather patterns

• Month: Calendar month (1-12)

• Day Type: Weekday or weekend

This grouping enables:

• Seasonal variation analysis

• Weekday/weekend pattern differences

• Representative day selection

Temperature multiplier calculation

Temperature multipliers scale representative day heating/cooling load across days within each group (month + day type) based on relative temperature extremes.

Basic multiplier formula

For a day with HDD value in a month with total HDD:

heating_multiplier = (HDD / total_HDD) × num_days

Similarly for cooling:

cooling_multiplier = (CDD / total_CDD) × num_days

Key property: Multipliers sum to num_days within each group, preserving total energy.

The shoulder month problem

In spring and fall (“shoulder months”), some days may have zero or very low degree days while others have significant heating or cooling needs. Without adjustment, this creates unrealistic load spikes by concentrating all HVAC load on just the extreme days.

Example shoulder month (April):

• Days 1-21, 27-30: HDD = 0 (mild weather)

• Days 22-26: HDD = 5-10 (cold snap)

Without smoothing, all heating load would be assigned to days 22-26, creating artificial spikes.

Shoulder month smoothing

STRIDE applies a minimum threshold to smooth these transitions:

-- Calculate maximum degree days in each group
max_hdd = MAX(hdd) in (month, day_type)
min_threshold = max_hdd / shoulder_month_smoothing_factor

-- Apply threshold
adjusted_hdd = CASE
    WHEN hdd < min_threshold THEN min_threshold
    ELSE hdd
END

This ensures all days in shoulder months experience some HVAC load, preventing unrealistic concentration.

Smoothing parameters

Parameter	Description	Default	Typical Values
enable_shoulder_month_smoothing	Enable/disable smoothing	true	true/false
shoulder_month_smoothing_factor	Divisor for max degree days	10.0	5.0 (aggressive), 10.0 (moderate), 20.0 (gentle)

Example in project.json5:

{
  project_id: "my_project",
  // ... other config ...
  model_parameters: {
    enable_shoulder_month_smoothing: true,
    shoulder_month_smoothing_factor: 10.0,  // Moderate smoothing
  }
}

Effect of smoothing factor:

• Lower values (5): More aggressive smoothing, broader load distribution

• Higher values (20): Gentler smoothing, closer to original pattern

• Disabled: No smoothing, potential for unrealistic spikes

Adjusted multiplier calculation

Final multipliers use adjusted degree days:

heating_multiplier = (adjusted_hdd / adjusted_total_hdd) × num_days

cooling_multiplier = (adjusted_cdd / adjusted_total_cdd) × num_days

This preserves energy conservation (multipliers still sum to num_days) while smoothing shoulder month transitions.

Application to load shapes

Load shapes for representative days

Load shapes from the IMAGE Integrated Assessment Model (Castillo et al. 2022)[2] provide hourly consumption profiles. The dataset includes:

• One weekday and one weekend day per month (24 total representative days)

• 24 hourly values per day (e.g., hour 0 = midnight-1am, hour 23 = 11pm-midnight)

• Segmentation by: End use (Heating, Cooling, Other), sector (Residential, Commercial, Industrial, Transportation), geography, model year

Expansion to full year

The load_shapes_expanded dbt model expands these 24 representative days into 8760 hours (365 days × 24 hours) by:

1. Matching each calendar day of the selected weather year to its representative profile:

   • Days are matched by month (January → January representative day) and day type (weekday/weekend)

   • Example: Tuesday, January 15 uses the January weekday profile

2. Applying temperature multipliers to adjust for weather:

   adjusted_value = load_shape_value * multiplier
   
   -- Multiplier depends on end use:
   multiplier = CASE
       WHEN enduse = 'heating' THEN heating_multiplier
       WHEN enduse = 'cooling' THEN cooling_multiplier
       ELSE 1.0  -- Non-HVAC end uses (lighting, equipment, etc.)
   END
   

3. Repeating the 24-hour pattern for each day with its specific temperature multiplier

Result: Full-year hourly load shapes that preserve:

• Original hourly patterns from IMAGE (morning/evening peaks, daily cycles)

• Monthly seasonal variation (via representative days)

• Weekday/weekend differences

• Historical weather patterns (via weather-driven adjustments for heating/cooling end uses based on ERA5)

Scaling to annual consumption

The final step scales weather-adjusted hourly shapes to match annual energy projections.

Annual energy projection

For each sector/subsector/model year, STRIDE calculates annual energy demand from:

• Energy intensity regressions (energy per unit GDP, or population x HDI)

• Energy use driver projections (GDP, HDI, population)

This produces annual totals in MWh for each sector.

Scaling factor calculation

# Sum all hourly values for the year
load_shape_annual_total = SUM(expanded_hourly_values)

# Calculate scaling factor
scaling_factor = projected_annual_energy / load_shape_annual_total

Final hourly values

final_hourly_load = expanded_hourly_value * scaling_factor

This ensures:

• Hourly values sum to the projected annual total

• Weather-based daily/seasonal patterns are preserved

• Realistic load profiles throughout the year

dbt models

The weather year modeling pipeline is implemented in these dbt models:

Model	Purpose
weather_bait_daily	Pivots weather data from long to wide format and extracts date components
weather_degree_days	Calculates daily HDD and CDD from BAIT
weather_degree_days_grouped	Aggregates degree days by geography, weather year, month, and day type
temperature_multipliers	Computes daily multipliers with shoulder month smoothing
load_shapes_expanded	Applies temperature multipliers to expand representative days to full year
energy_projection_*	Combines expanded load shapes with energy intensity to produce projections

Logging and diagnostics

When computing energy projections, STRIDE logs temperature multiplier statistics:

INFO: Computing energy projection with model parameters: 
      heating_threshold=18.0, cooling_threshold=18.0, 
      enable_shoulder_month_smoothing=True, shoulder_month_smoothing_factor=10.0
INFO: Running scenario=baseline with weather_year=2018, 
      shoulder_month_smoothing=enabled (factor=10.0)
INFO: Temperature multiplier ranges for scenario=baseline: 
      heating=[0.234, 3.456], cooling=[0.123, 4.567], other=[1.000, 1.000]

Related Topics

• dbt Computation - Overall dbt transformation pipeline

• Create a project - Tutorial on creating a stride project

• Browse the dbt project - Tutorial on browsing the dbt portion of a stride project

• Process results with Python - Tutorial on accessing and processing result data using Python

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[1]

Staffell, I., Pfenninger, S., & Johnson, N. (2023). A global model of hourly space heating and cooling demand at multiple spatial scales. Nature Energy, 8, 1328-1344. https://doi.org/10.1038/s41560-023-01341-5

[2]

Castillo, R., van Ruijven, B.J., Pfenninger, S., van Vuuren, D.P., Carrara, S., & Patel, M.K. (2022). Future global electricity demand load curves. Energy, 259, 124857. https://doi.org/10.1016/j.energy.2022.124857