High-Resolution Power, Energy, and Harmonic Measurements for Individual and Aggregated Residential Appliances

Dataset for “Real-Time Residential Energy Management: Evaluating Demand Side Management Strategies and Harmonic Impact Using High-Resolution Appliance Data” This dataset accompanies the article Tüysüz et al. (2025) published in the Journal of Building Engineering. It provides a high-resolution residential appliance dataset specifically designed for research on demand-side management (DSM), load scheduling, harmonic analysis, and power quality evaluation. 1. Related Publication DOI: https://doi.org/10.1016/j.jobe.2025.114660 2. How to Cite This Work Article (APA): Tüysüz, M., Okumuş, H. İ., Çavdar, B., & Ustübioğlu, A. (2025). Real-time residential energy management: Evaluating demand side management strategies and harmonic impact using high-resolution appliance data. Journal of Building Engineering, 114660. https://doi.org/10.1016/j.jobe.2025.114660 Article (APA): @article{tuysuz2025real, title={Real-Time Residential Energy Management: Evaluating Demand Side Management Strategies and Harmonic Impact Using High-Resolution Appliance Data}, author={T{\"u}ys{\"u}z, M and Okumu{\c{s}}, H{\.I} and {\c{C}}avdar, B and Ust{\"u}bio{\u{g}}lu, A}, journal={Journal of Building Engineering}, pages={114660}, year={2025}, publisher={Elsevier} } 3. Dataset Content The dataset contains 1-second real-time measurements obtained from 26 electrical appliances operating in a residential building in Trabzon, Türkiye. Unlike traditional datasets that only include basic power metrics, this dataset provides: Active Power (P) Active Energy (E) Total Harmonic Distortion of Current (THDi) Individual Current Harmonics (1st–50th order) Appliance operational timelines (start/end cycles, usage patterns) Aggregate total load power & energy data This enables realistic, high-fidelity load profile modeling without relying on synthetic or simulated appliance signatures. 4. Purpose and Novelty Existing load profiling datasets often include only a few appliances and typically ignore harmonic behavior. This dataset fills that gap by offering: Large-scale appliance coverage (26 devices) High temporal resolution (1 s) Simultaneous inclusion of power, energy, and harmonic characteristics Real-world field measurements instead of laboratory or simulated data The dataset was used to evaluate DSM strategies with Mayfly Algorithm (MA) and Artificial Rabbit Optimizer (ARO), two multi-objective metaheuristic optimization methods adapted to operate on 1-second resolution data and 1-minute scheduling intervals. 5. Experimental Context The measurements were collected across 24 hours for each scenario. DSM-based load schedules generated by MA and ARO were physically implemented in the residential environment, and the resulting: Active power, Active energy, THDi, Current harmonic components were re-measured in real time with 1-second resolution. This transforms the study from a purely theoretical DSM simulation into a field-validated, real-world DSM experiment, making the dataset one of the few residential-scale resources capturing DSM–harmonic interactions. 6. Key Contributions Reflected in the Dataset This dataset represents several original contributions: 1-second appliance-level measurements of active power, energy, THDi, and up to 50th-order harmonics for 26 appliances, providing rare, up-to-date, and realistic load profiles. Unlike existing datasets focusing solely on load profiles, this dataset simultaneously includes harmonic behavior, enabling dual-domain DSM and power-quality analysis. DSM strategies optimized using advanced methods (MA, ARO) were implemented in practice, and field measurements were taken for every generated schedule. Real-time results demonstrated that effective DSM strategies not only reduce PAR, billing cost, and energy consumption, but also improve harmonic behavior, revealing a previously underexplored DSM–harmonic relationship. The dataset provides a valuable benchmark for future studies in DSM, non-intrusive load monitoring (NILM), automatic load control, and power quality assessment. The study integrates high-resolution real-world DSM applications, bridging theoretical models with practical energy management implementations. 7. Ethics & Privacy No personal, behavioral, or sensitive residential information was collected. Data includes only electrical measurements.