It's Different

Abstract

Residential buildings contribute significantly to the overall energy usage across the world. Real deployments, and collected data thereof, play a critical role in providing insights into home energy consumption and occupant behavior. Existing datasets from real residential deployments are all from the developed countries. Developing countries, such as India, present unique opportunities to evaluate the scalability of existing research in diverse settings. Building upon more than a year of experience in sensor network deployments, we undertake an extensive deployment in a three storey home in Delhi, spanning 73 days from May-August 2013, measuring electrical, water and ambient parameters. We used 33 sensors across the home, measuring these parameters, collecting a total of approx. 400 MB of data daily. We discuss the architectural implications on the deployment systems that can be used for monitoring and control in the context of developing countries. Addressing the unreliability of electrical grid and internet in such settings, we present Sense Local-store Upload architecture for robust data collection. While providing several unique aspects, our deployment further validates the common considerations from similar residential deployments, discussed previously in the literature. We also release our collected data-Indian data for Ambient Water and Electricity Sensing (iAWE), for public use.

Robust energy disaggregation using appliance-specific temporal contextual information

“…Three different publicly available databases were used, namely the ECO [50], the REDD [51], and the iAWE [52] database. The ECO and REDD databases consist of different datasets with each of them containing power consumption recordings from different houses, while iAWE database consists of recordings from one house.…”

“…The utilized datasets are available online and free to download under common creative licence (ECO [50] (10.5905/ethz-1007-35), REDD (http:// redd.csail.mit.edu/) [51] and iAWE [52] (https://iawe.github.io/)).…”

Nilmtk

“…The Almanac of Minutely Power dataset (AMPds) [15] was also released that year containing both aggregate and sub-metered power data from a single household. Subsequently, the Indian data for Ambient Water and Electricity Sensing (iAWE) [16] was released, which contains both aggregate and sub-metered power data from a single house. Most recently, the UK Domestic Appliance-Level Electricity data set [17] (UK-DALE) was released which contains data from four households using both aggregate meters and individual appliance sub-meters.…”

“…Batra et al showed voltage fluctuates from 180-250 V in the iAWE data set collected 5 http://nilmtk.github.io/nilmtk/stats.html in India [16], while the voltage in the Smart* data set varies across the range 118-123 V. Hart suggested to account for these voltage fluctuations as they can significantly impact power draw [1]. Therefore, NILMTK provides a voltage normalisation function based on Hart's equation:…”

Metadata for Energy Disaggregation

“…In 2011, researchers at MIT released the first public dataset for energy disaggregation research [10]. Since then, over ten more datasets have been released [1]- [3], [9], [12], [14] and, in March 2014, a project called Wiki Energy 1 launched to share datasets online. These datasets have been well received but, because each dataset uses a different file format, it is time consuming to import multiple datasets.…”