A Kyiv Private Household Smart Plug Dataset for AI-Driven Digital Twin Modeling of Sustainable Residential Energy Systemsdoi:10.48788/DVUA/SXITMWDataverseUA2026-09-231Boiko, Olha; Anton, Komin; Parfenenko, Yuliia, 2026, "A Kyiv Private Household Smart Plug Dataset for AI-Driven Digital Twin Modeling of Sustainable Residential Energy Systems", https://doi.org/10.48788/DVUA/SXITMW, DataverseUA, V1, UNF:6:7De53OI0uklpWD5HTNMzWw== [fileUNF]A Kyiv Private Household Smart Plug Dataset for AI-Driven Digital Twin Modeling of Sustainable Residential Energy Systemsdoi:10.48788/DVUA/SXITMWBoiko, OlhaAnton, KominParfenenko, YuliiaPythonNo. 0126U000866DataverseUABoiko, OlhaOlha Boiko2026-09-22Computer and Information ScienceEnergy consumption--ForecastingArtificial intelligenceReal-time data processingMachine learningTime-series analysisDigital twins (Computer simulation)This dataset contains energy consumption data collected for research on energy consumption forecasting and intelligent energy management in hybrid energy systems. The dataset can be used to develop, train, validate, and evaluate machine learning models for short-term electricity consumption forecasting and energy management. It is intended for research in energy informatics, edge AI, TinyML, and smart energy systems.Time Series DataThe dataset consists of three CSV files plus a README, comprising a plug-level hourly energy consumption dataset from a private household in Kyiv, Ukraine. The primary file, export_energy_plain.csv, contains 26,263 hourly readings (datetime in UTC, consumption in kWh, and entity_id) collected between 27 August 2025 and 2 September 2026 from three Shelly Smart Plugs: one in the living room (TV, Apple TV, and LED night light; 8,857 records), one in the bathroom (towel dryer and washing machine; 8,629 records), and one in the home office (monitor, laptop, speakers, and the Raspberry Pi 5 that also served as the Home Assistant/InfluxDB data-collection hub; 8,777 records), with an overall missing-data rate of 1.69%. This is supplemented by export_outdoor_temp_plain.csv, containing 8,888 hourly outdoor temperature readings in degrees Celsius that can be joined to the energy data by UTC timestamp, and by vacation_periods.csv, which records two known household absence periods (a winter vacation from 22–28 February 2026 and a summer vacation from 15–24 July 2026) for occupancy-aware analysis. The README also notes qualitative context on scheduled and emergency power outages in Kyiv between 15 October 2025 and 1 March 2026, though these are not provided as exact day-level labels.Komin, A., & Boiko, O. (2025). A privacy-preserving edge data aggregation for TinyML energy forecasting in households. Technology Audit and Production Reserves, 6(2(86), 31–38. https://doi.org/10.15587/2706-5448.2025.33927710.15587/2706-5448.2025.339277Komin, A., & Boiko, O. (2025). A privacy-preserving edge data aggregation for TinyML energy forecasting in households. Technology Audit and Production Reserves, 6(2(86), 31–38. https://doi.org/10.15587/2706-5448.2025.339277export_energy_plain.tab262633text/tab-separated-valuesUNF:6:EuOrLva31wab1y7cAd2yJw==export_outdoor_temp_plain.tab88883text/tab-separated-valuesUNF:6:fWB4cJqyVkkVX8GVm5hXPg==vacation_periods.tab24text/tab-separated-valuesUNF:6:aByXksokKcMOufKuk1uQoA==datetimeUNF:6:pK/xD1hrml42LOsOLh5+dw==Consumption0.0446055520948397426263.01.44241099999999280.0073666666666980520.0.0.090964646874826030.0UNF:6:cUrD92kYZwzqh+GlmHArMw==entity_idUNF:6:+UJM8Y9m5QquYuU+v/5HOw==datetimeUNF:6:xmMkjykKeqXYU4daJhiAvw==outdoor_temp-23.436.88888.09.22145589558966310.9503644936193869.40.0.UNF:6:n4KoKUikha9xcURyNLCygA==entity_idUNF:6:cH2DcIjF6PuxlPQ4Ibs6mg==vacation_nameUNF:6:nkohokprXb8ko7P3qgrIuA==start_dateUNF:6:noIJdQezKIt+8IK7+vWpNQ==end_dateUNF:6:3RzlmQT0RDM9W7uTjBdfjQ==seasonUNF:6:3oUru49PTD7dHzudn4VhaA==README.txttext/plain