IEDL: Indian Energy Dataset with Low frequency for NILM

Abstract

Energy conservation has received a lot of attention in recent ten years. Adoption of sustainable energy is important for meeting energy demand, to address this Non-Intrusive Load Monitoring (NILM) technology is now being developed all over the world. Several energy consumption datasets have been released; each dataset has different properties, uses, and limitations. Thus, a solid comprehension of the relevant datasets will help to improve NILM system. This work is dedicated to detailed study of low frequency (data sampling rate lower than Alternating Current (AC) fundamental frequency) residential datasets, a total eighteen datasets are compared according to their measurement features, collected location, nature of sampling, data collection duration, data development platform etc. Furthermore, datasets are classified depending upon the appliance level and aggregated level data. To full fill the literature gap a new low-cost Indian Energy Dataset with Low frequency (IEDL) has been developed with detailed system deployment. This low frequency dataset collects data from aged appliances (older than ten years), which is allowed to adopt NILM applications in energy saving, recommendation system, appliance behavior, demand prediction area.

Keywords

• Low frequency NILM
• Energy disaggregation
• Energy datasets
• Dataset development
• Sustainable energy