Publication date: Oct 01, 2025
This dataset compiles daily electricity statistics for Bangladesh across national and divisional levels. The data were programmatically scraped from the Bangladesh Power Development Board’s (BPDB) digital archive and processed into a structured, machine-readable format using a custom Python pipeline. The dataset consists of 1867 daily reports, spanning from November 21, 2019, to December 30, 2024. Each record includes key variables such as electricity demand, generation, load shedding, temperature, and supply limitations due to gas shortages, coal availability, and low water levels. The dataset was curated through multiple stages, which include manual verification, holiday classification, missing value imputation, and outlier correction. Five sequential versions are provided, which reflect progressive enhancements from raw extraction to modeling readiness. The data can be used in time series analysis, load forecasting, energy policy research, and machine learning applications in resource-constrained settings. Additionally, the collection spans the COVID-19 pandemic period, offering unique opportunities for studying the impact of external factors on national energy systems.
Open Access PDF
| Concepts | Keywords |
|---|---|
| Bangladesh | Energy policy |
| Coal | Load forecasting |
| Daily | Long-term prediction |
| Pandemic | Seasonality |
| Python | Time series |
Semantics
| Type | Source | Name |
|---|---|---|
| drug | DRUGBANK | Water |
| disease | MESH | COVID-19 pandemic |