Global Hourly Electricity Demand Dataset Segregated by Season and Day Type: Tracking Changes Across COVID-19 Lockdown Phases

This dataset provides a cross-country time series of hourly typical electricity usage designed for comparative analysis of consumption patterns before, during, and after the strictest COVID-19 lockdown periods. The dataset aggregates and normalises raw usage data to present the typical hourly demand for [Number] countries, segmented by season and day type (weekday vs. weekend).

The primary utility of this data is to facilitate standardised analyses of the non-pharmaceutical intervention (NPI) impact on energy consumption globally. The data processing pipeline uses external master data, including government response metrics derived from the Oxford COVID-19 Government Response Tracker (OxCGRT), to precisely define and standardise the "strictest lockdown period" for each country.

The dataset is shared in two primary formats to maximise reusability and accessibility:

PostgreSQL Database Dump Files (.sql):

This is the recommended format for researchers seeking to replicate the full data environment or integrate the data into an existing pipeline. The files were generated from a PostgreSQL [Insert PG Version] database.

dataset_schema_only: Contains the six table definitions, including all primary keys, foreign key relationships, and constraints, but no data. This is ideal for those who wish to populate the schema with their own data.

dataset_full: Contains the full schema and all underlying data. This file can be used to instantly instantiate a complete, working PostgreSQL database for immediate analysis.

Comma-Separated Value Files (.csv):

Six individual CSV files are provided, with each file corresponding exactly to one table in the relational schema.

This format offers the most direct access to the data, allowing users to easily import the records into any statistical software or alternative database management system without requiring a PostgreSQL environment setup.

The database is composed of six interrelated tables, featuring the core typical_daily_usage_patterns table and five supporting tables.