Problem
Research questions and friction points this paper is trying to address.
Residential Electricity Consumption
Smart Meter Data
Sri Lanka
Energy Dataset
Household Surveys
Residential Electricity Consumption Dataset for Sri Lanka (RECON-SL)
🤖 AI Summary
This study addresses the lack of multi-source fused residential electricity consumption datasets in Sri Lanka by integrating utility records, high-frequency smart meter data, and longitudinal household surveys to construct a multi-granularity dataset covering 4,063 households. Methodologically, it employs multi-source data fusion, high-frequency time-series acquisition, and longitudinal survey techniques. As the first large-scale resource in Sri Lanka—and a globally scarce one—that links smart meter readings with longitudinal surveys, the dataset comprises over 50 million meter readings and 11,000 interview records. This comprehensive resource effectively supports research in load forecasting, energy policy analysis, and machine learning applications for residential energy systems.
📝 Abstract
This article describes the Residential Electricity Consumption Dataset for Sri Lanka (RECON-SL), a multi-source resource that integrates utility records, high-frequency smart meter data, and household surveys from the service area of Lanka Electricity Company (LECO) in Sri Lanka. The dataset captures electricity use for 4,063 households, collected over the period October 2022 to January 2025, with coverage at monthly, 6-hour, and 15-minute intervals. Monthly billing-cycle data provide complete coverage across all households, while smart meter data are available for a subsample of 1,438 households, offering both fine-grained 15-minute readings and coarser 6-hour records. Three survey waves complement these consumption measures, collecting information on demographics, housing, appliance ownership, and perceptions of energy security.
Across its components, the dataset includes more than 50 million meter readings and detailed survey responses from over 11,000 household interviews. As the first dataset of its kind in Sri Lanka, and among the few worldwide to link smart meter records with longitudinal survey data at this scale, it provides a unique resource for energy, machine learning and policy research, with known gaps in smart meter coverage documented for users. The dataset can support diverse applications, including electricity load forecasting, socio-economic and behavioral energy research, distribution planning, and policy analysis in energy affordability and equity. Access is provided through an open repository, with accompanying documentation and scripts to facilitate reuse.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Multi-source Data Integration
Smart Meter Data
High-frequency Time Series
Longitudinal Survey
Load Forecasting
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