Principal Investigator: Rajesh Sundaresan
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.
Project Publications
| 1. | Sundaresan, Rajesh Developing a framework for using electricity consumption data to drive energy efficiency in the residential sector Technical Report 2017. BibTeX | Links:  @techreport{Sundaresan2017,
title = {Developing a framework for using electricity consumption data to drive energy efficiency in the residential sector},
author = {Rajesh Sundaresan},
url = {http://www.rbccps.org/wp-content/uploads/2017/06/KeralaEnergyEfficiencyAluva.pdf},
year = {2017},
date = {2017-03-31},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
|
| 2. | Bora, Ashish; Borkar, Vivek S; Garg, Dinesh; Sundaresan, Rajesh 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. Abstract | BibTeX | Links:  @conference{Bora2016,
title = {Edge conductance estimation using MCMC},
author = {Ashish Bora and Vivek S. Borkar and Dinesh Garg and Rajesh Sundaresan},
url = {http://www.rbccps.org/wp-content/uploads/2017/10/07852230.pdf},
doi = {10.1109/ALLERTON.2016.7852230},
year = {2017},
date = {2017-02-13},
booktitle = {Proceedings of the 54th Annual Allerton Conference on Communication, Control, and Computing, 27.-30.09.16, Monticello (USA)},
pages = {202-208},
abstract = {We propose an iterative and distributed Markov Chain Monte Carlo scheme for estimation of effective edge conductances in a graph. A sample complexity analysis is provided. The theoretical guarantees on the performance of the proposed algorithm are weak compared to those of existing algorithms. But numerical experiments suggest that the algorithm might still be effective while offering the advantages of low per iterate computation and memory requirements.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
We propose an iterative and distributed Markov Chain Monte Carlo scheme for estimation of effective edge conductances in a graph. A sample complexity analysis is provided. The theoretical guarantees on the performance of the proposed algorithm are weak compared to those of existing algorithms. But numerical experiments suggest that the algorithm might still be effective while offering the advantages of low per iterate computation and memory requirements. |
| 3. | Khandelwal, T; Rajwanshi, K; Bharadwaj, P; Garani, S S; Sundaresan, Rajesh Exploiting appliance state constraints to improve appliance state detection Conference Proceedings of the 8th ACM International Conference on Future Energy Systems (ACM eEnergy), 16.-19.05.17, Hong Kong (China), (111-120), 2017. Abstract | BibTeX | Links: @conference{Khandelwal2017,
title = {Exploiting appliance state constraints to improve appliance state detection},
author = {T. Khandelwal and K. Rajwanshi and P. Bharadwaj and S. S. Garani and Rajesh Sundaresan},
url = {http://www.rbccps.org/wp-content/uploads/2017/10/p111-Khandelwal.pdf},
doi = {10.1145/3077839.3077859},
year = {2017},
date = {2017-05-19},
booktitle = {Proceedings of the 8th ACM International Conference on Future Energy Systems (ACM eEnergy), 16.-19.05.17, Hong Kong (China)},
number = {111-120},
abstract = {This work deals with non-intrusive load monitoring using a single inexpensive device at the mains. We argue that very low sampling rates (of 1 Hz) may suffice. This enables significant compression and cheaper end-devices. There are challenges when operating at such low sampling rates, of course. To achieve good appliance inference performance we propose improved event detection, feature extraction, and inference algorithms. The inference algorithm exploits state transition constraints and proposes the use of a maximum likelihood sequence detection for improved performance.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
This work deals with non-intrusive load monitoring using a single inexpensive device at the mains. We argue that very low sampling rates (of 1 Hz) may suffice. This enables significant compression and cheaper end-devices. There are challenges when operating at such low sampling rates, of course. To achieve good appliance inference performance we propose improved event detection, feature extraction, and inference algorithms. The inference algorithm exploits state transition constraints and proposes the use of a maximum likelihood sequence detection for improved performance. |
