Work

Descriptor: Multi-Circuit Electric Consumption Thailand Home Dataset (MCEC-Thai)

Year: 2025

Type: article

Abstract: This paper describes a dataset for evaluating the effectiveness of energy disaggregation techniques, specifically Non-Intrusive Load Monitoring (NILM) algorithms. The dataset comprises real-world energy consumption data from a residential household in Bangkok, Thailand. It includes one aggregate circuit and ten active sub-circuits, including lights, plug-outlets, a water pump, a water heater, and an air conditioner, sampled at a one-minute interval. The dataset provides voltage, current, active power, reactive power, and power factor measurement data, facilitating experimentation with multiple energy disaggregation scenarios. The inclusion of the absolute and signed reactive power (Q) data allows greater flexibility in feature selection for model training. This dataset has been validated and utilized in previous research, demonstrating its robustness and high accuracy in appliance classification and power state detection. The data can also be applied to diagnose appliance faults and assess energy efficiency.

Source: IEEE data descriptions.

DOI: 10.1109/ieeedata.2025.3558179

Authors: Bundit Buddhahai, Waranyu Wongseree, Pattana Rakkwamsuk

Institutions: Walailak University, King Mongkut's University of Technology North Bangkok, King Mongkut's University of Technology Thonburi

Language: English

Topic: Smart Grid Energy Management

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $600