A synchronized daily electricity demand dataset for commercial and residential service areas of Dhaka, Bangladesh, with integrated meteorological and calendar attributes
- 1Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Rupnagar, Mirpur-2, Dhaka, 1216, Bangladesh
- 2Corresponding author.
Abstract
There is limited publicly available data on electricity demand for South Asian cities, and utility records are usually not shared below the national level. This article provides a daily electricity demand dataset for two areas in Dhaka, Bangladesh: Mirpur, which is mainly residential, and Gulshan, which is mainly commercial. The demand records were obtained directly from Dhaka Electric Supply Company PLC (DESCO) with written permission. The data were obtained from the company’s daily control-room log. The log is maintained manually, with one sheet prepared for each calendar date. Each sheet contains the aggregate load of each distribution zone in megawatts (MW) at fixed times of the day. A continuous dataset was constructed by transcribing data from 597 separate sheets. Each individual reading was checked during the quality-control process. For each date, all logged readings were summed to obtain a single daily demand value. This produced a daily aggregate demand index in summed megawatts (MW).The dataset covers 597 consecutive days, from 1 January 2024 to 19 August 2025, for each study area. Six daily weather variables were obtained from the Open-Meteo Historical Weather API and matched with the corresponding observation dates. These variables include maximum, minimum, and mean temperature, rainfall, maximum wind speed, and sunshine duration. Each record also contains the day of the week, climatic season, public holiday, special holiday, and the name of any major national or religious event. Since the Bangladeshi holiday calendar is not available in a machine-readable format, these attributes were manually annotated using the official Bangladesh Gazette. The annotations were cross-checked before the dataset was released. Both areas were processed using the same procedure and covered the same period. Therefore, the two series are aligned by date and correspond to the same weather and calendar conditions. Within this common temporal framework, the two files represent different area-level electricity demand contexts. One area is mainly residential, while the other is mainly commercial. However, the dataset is not intended to provide a normalized comparison between residential and commercial electricity consumption. To facilitate further use of the dataset, it is provided as two CSV files. The dataset is openly available through Mendeley Data under a CC BY 4.0 licence. These data can support electricity demand forecasting, feature construction, unusual-pattern detection, and explainable machine learning. They are particularly useful for research in developing-country settings, where well-documented electricity demand data are often limited.
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BibTeX
@article{hasan2026synchronized,
title = {{A synchronized daily electricity demand dataset for commercial and residential service areas of Dhaka, Bangladesh, with integrated meteorological and calendar attributes}},
author = {MD Sajid Hasan and Md. Ashiqur Rahman and Sanjida Khanom and Mohammod Ashikur Rahman},
journal = {Data in Brief},
year = {2026},
month = aug,
doi = {10.1016/j.dib.2026.113328},
url = {https://doi.org/10.1016/j.dib.2026.113328},
note = {CC BY},
}
RIS
TY - JOUR
TI - A synchronized daily electricity demand dataset for commercial and residential service areas of Dhaka, Bangladesh, with integrated meteorological and calendar attributes
AU - MD Sajid Hasan
AU - Md. Ashiqur Rahman
AU - Sanjida Khanom
AU - Mohammod Ashikur Rahman
PY - 2026
DA - 2026/08/01
JO - Data in Brief
DO - 10.1016/j.dib.2026.113328
UR - https://doi.org/10.1016/j.dib.2026.113328
AB - There is limited publicly available data on electricity demand for South Asian cities, and utility records are usually not shared below the national level. This article provides a daily electricity demand dataset for two areas in Dhaka, Bangladesh: Mirpur, which is mainly residential, and Gulshan, which is mainly commercial. The demand records were obtained directly from Dhaka Electric Supply Company PLC (DESCO) with written permission. The data were obtained from the company’s daily control-room log. The log is maintained manually, with one sheet prepared for each calendar date. Each sheet contains the aggregate load of each distribution zone in megawatts (MW) at fixed times of the day. A continuous dataset was constructed by transcribing data from 597 separate sheets. Each individual reading was checked during the quality-control process. For each date, all logged readings were summed to obtain a single daily demand value. This produced a daily aggregate demand index in summed megawatts (MW).The dataset covers 597 consecutive days, from 1 January 2024 to 19 August 2025, for each study area. Six daily weather variables were obtained from the Open-Meteo Historical Weather API and matched with the corresponding observation dates. These variables include maximum, minimum, and mean temperature, rainfall, maximum wind speed, and sunshine duration. Each record also contains the day of the week, climatic season, public holiday, special holiday, and the name of any major national or religious event. Since the Bangladeshi holiday calendar is not available in a machine-readable format, these attributes were manually annotated using the official Bangladesh Gazette. The annotations were cross-checked before the dataset was released. Both areas were processed using the same procedure and covered the same period. Therefore, the two series are aligned by date and correspond to the same weather and calendar conditions. Within this common temporal framework, the two files represent different area-level electricity demand contexts. One area is mainly residential, while the other is mainly commercial. However, the dataset is not intended to provide a normalized comparison between residential and commercial electricity consumption. To facilitate further use of the dataset, it is provided as two CSV files. The dataset is openly available through Mendeley Data under a CC BY 4.0 licence. These data can support electricity demand forecasting, feature construction, unusual-pattern detection, and explainable machine learning. They are particularly useful for research in developing-country settings, where well-documented electricity demand data are often limited.
SN - 2352-3409
ER -