High-Resolution Active Power and Energy Measurements of Washing Machines and Dishwashers Under Different Operating Modes

Dataset for “Coordinated Load Management in Residential Neighborhoods: Real-Measurement Based Neighborhood Energy Management System” This dataset accompanies the study entitled “Coordinated Load Management in Residential Neighborhoods: Real-Measurement Based Neighborhood Energy Management System”. The study proposes a real-measurement-based Neighborhood Energy Management System (NEMS) framework for coordinated load management in residential neighborhoods. The proposed approach uses the Grey Wolf Optimizer (GWO) to schedule shiftable household appliances and to evaluate coordinated load management strategies based on real appliance measurements. 1. Related Publication Manuscript Title:Coordinated Load Management in Residential Neighborhoods: Real-Measurement Based Neighborhood Energy Management System DOI:[Insert article DOI after publication] 2. How to Cite This Work Dataset: Tüysüz, M., & Çavdar, B. (2026). Dataset for “Coordinated Load Management in Residential Neighborhoods: Real-Measurement Based Neighborhood Energy Management System” [Data set]. Zenodo. https://doi.org/[insert dataset DOI] Article: Tüysüz, M., & Çavdar, B. (2026). Coordinated load management in residential neighborhoods: Real-measurement based neighborhood energy management system. [Journal Name], [Volume(Issue)], [Page range/article number]. https://doi.org/[insert article DOI] BibTeX: @dataset{tuysuz2026coordinated_dataset, author = {T{\"u}ys{\"u}z, Metin and {\c{C}}avdar, Bora}, title = {Dataset for ``Coordinated Load Management in Residential Neighborhoods: Real-Measurement Based Neighborhood Energy Management System''}, year = {2026}, publisher = {Zenodo}, doi = {[insert dataset DOI]}, url = {https://doi.org/[insert dataset DOI]}} 3. Dataset Content This dataset contains 1-second real-time measurement data obtained from selected residential appliances used in the coordinated load management study. The current version includes measurements of washing machines and dishwashers operating under different working modes/programs. The dataset includes the following electrical quantities: Active Power (P) Active Energy (E) The measurements are provided with 1-second temporal resolution and represent real appliance operation profiles rather than synthetic or simulation-based load models. 4. Purpose and Novelty The purpose of this dataset is to support realistic residential load profile modeling and coordinated appliance scheduling studies by providing high-resolution real measurement data from household appliances. In the related study, these data were used as part of a real-measurement-based NEMS framework in which shiftable residential appliances were scheduled using the Grey Wolf Optimizer (GWO). The dataset contributes to the development and validation of coordinated demand-side management strategies by reflecting the actual operating characteristics of appliances under different working modes. Compared with simplified rated-power-based appliance models, the use of 1-second real measurement data enables more accurate representation of appliance behavior, including variations in active power and accumulated active energy during operation. 5. Experimental Context The measurements were obtained from residential appliances under real operating conditions. Washing machines and dishwashers were operated under different working modes/programs, and their active power and active energy values were recorded at 1-second intervals. 6. Key Contributions Reflected in the Dataset This dataset provides: 1-second real-time appliance-level measurements. Active power and active energy profiles of washing machines and dishwashers. Measurements under different working modes/programs. Real appliance operation data suitable for residential load profile generation. A data source for coordinated load management, demand-side management, appliance scheduling, and NEMS studies. A basis for future extensions including additional residential appliance measurements and generated neighborhood-level load profiles. 7. Ethics & Privacy No personal, behavioral, or sensitive residential information was collected. The dataset includes only electrical measurements obtained from residential appliances.