|Professor||Robert Bosch Centre for Cyber-Physical Systems
Electrical Communication Engineering
|Shayan Garani Srinivasa||Assistant Professor||Electronic Systems Engineering|
|Shuvashish Chatterjee||Head of Operations||clytics|
|Viney Kaushik||Head of Product Development and Analytics||clytics|
|Priti Bharadwaj||Project Assistant||Electrical Communication Engineering|
|Tarun Khandelwal||Project Assistant||Electrical Communication Engineering|
|Nakul Saxena||Project Assistant||Electrical Communication Engineering|
|Harshavardhan Ramanna||Project Assistant||Electrical Communication Engineering|
|Divyansh Khanna||Project Assistant||Electrical Communication Engineering|
|Karan Rajwanshi||Project Assistant||Electrical Communication Engineering|
|Shahid Mehraj||Project Assistant||Electrical Communication Engineering|
The project aimed to provide personalised feedback to about 25,000 electricity consumers in the town of Aluva, Kerala. The goal of these personalised feedback inputs was to effect positive changes to consumer behaviour. The data from these 25,000 was compared with the consumption of a control group of another 25,000 from the same town.
The challenge was that we cannot instrument these 25,000 houses with smart meters rightaway because of budgetary reasons. So personalised feedback had to be based on cheaper means.
We obtained two years consumption data from the Kerala State Electricity Board for these consumers. We have used this data to cluster the households into various abstract categories. We have surveyed representative households – we have completed about 1,100 surveys of households – to get information on the number of individuals in the household, age-groups, basic appliances and numbers, building material types, floor, etc. The unsurveyed users have been associated with a certain number of ‘nearest neighbour’ surveyed consumers, and the factors are imputed based on the factors of these nearest neighbours. We have also arrived at disaggregation algorithms to identify consumptions for various categories, such as lighting, coooling, heating, refrigerator, others. The latest reports sent to consumers along with bills will contain this input and is the third in the set of reports sent to consumers along with their bills. The first report introduced the programme to the consumers, while the second report indicated comparisons within the cluster.
Additionally, to aid in the diaggregation, we have actually installed loggers in a few of the surveyed homes.
The final report of the project is available here: KeralaEnergyEfficiencyAluva
Developing a framework for using electricity consumption data to drive energy efficiency in the residential sector Technical Report
Edge conductance estimation using MCMC Conference
Proceedings of the 54th Annual Allerton Conference on Communication, Control, and Computing, 27.-30.09.16, Monticello (USA), 2017.
Exploiting appliance state constraints to improve appliance state detection Conference Forthcoming
Proceedings of the 8th ACM International Conference on Future Energy Systems (ACM eEnergy), 16.-19.05.17, Hong Kong (China), (111-120), Forthcoming.