- Background -

Electricity load characterization of districts

Supported by:

ELISE (ISA2 project) and Joint Research Center (Ispra)

Journal: Energy Reports

JIF (2020): 6.870 - Q1

Goals

To design and implement a visual tool that characterises energy consumption at a local scale (postal code) based on forecasted weather data

• Accounting for different conditions:
  • Building characteristics
  • Activity sectors
  • Weather
  • Socio-demographic
• Using interoperable datasets:
  • INSPIRE-harmonised datasets for cadastral data
  • Weather datasets coming from DEDL
• Geographical scope: Catalonia (a region of Spain)

Data inputs (2018-2023)

• Aggregated hourly electricity consumption
  • By tariff
  • By similar-to-district geographical level (Spain: postal code)
  • By type of usages (Industrial, residential, tertiary)
  • Indexed prices for PVPC energy tariff
• Building information (INSPIRE harmonised)
  • Typology
  • Built area
  • Year of construction
• Weather data
  • ERA5Land (historical data)
  • Digital Twin extreme weather forecast

Optional data inputs

• Aggregated socio-economical information
  • Annual net incomes per household
  • Incomes sources (salary, pension, benefits,...)
  • Gini index (inequality)
  • Population age
  • People per household
  • Population

Consumption data      Socio-economic data       Buildings information

Postal code         ~          Census tract          >        Building level

Aggregation

Energy characterisation method

Once datasets are harmonised to postal code level: 

1. Data cleaning
2. Inference of usage patterns
3. Modelling the electricity consumption using ML techniques
4. Outcomes of disaggregate consumption
   • Baseload
   • Space heating
   • Space cooling
5. Calculate KPIs
6. Use the model to predict multiple weather scenarios

Conclusions

This methodology will be:

• Capable to infer the drivers of consumption in the building sector and their occupants.
• Predict electricity consumption of large regions using extreme weather forecast.
• Demonstrates the potential of using DT, open data and INSPIRE harmonised datasets

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