VISDOM

VISDOM is a web-based platform and architecture for load analysis and management. VISDOM enables rich exploration of load data in conjunction with geographic, demographic, weather, survey, and other data sources.

By incorporating standard statistical methods as well as newly developed and published algorithms, VISDOM allows a deeper understanding of load data from unconventional angles. VISDOM’s data exploration capabilities can yield insights for program design and targeting and can assist with program measurement and evaluation.

The goal of VISDOM is to enable researchers and practitioners to more easily interpret and learn from demand side data and to apply those insights to improving demand side management for the smart grid.

VISDOM is now available as open source software

https://github.com/ConvergenceDA/visdom

Learn more about VISDOM

https://tomkat.stanford.edu/innovation-transfer/visdom

Publications

• Kwac, J., Rajagopal, R.
  Demand Response Targeting Using Big Data Analytics
  IEEE International Conference on Big Data (2013).
• Kwac, J., Flora, J., Rajagopal, R.
  Household Energy Consumption Segmentation Using Hourly Data
  IEEE Transactions on Smart Grid (2014), 5(1):420-430.
• Kwac, J., Rajagopal, R.
  Lifestyle model of residential energy consumption
  IEEE Transactions on Power Systems (Submitted).
• Varaiya, P., Rajagopal, R.
  Tailoring demand to match supply: how much flexibility is available in residential loads?
  Virtual Control Conference, IEEE (2013).
• Albert, A., Rajagopal, R.
  Cost-of-Service Segmentation of Energy Consumers
  IEEE Transactions on Power Systems (2014), 29(6): 2795-2803.
• Albert, A., Rajagopal, R.
  Smart Meter Driven Segmentation: What Your Consumption Says About You
  IEEE Transactions on Power Systems (2013), 28(4):4019-4030.
• Albert, A., Rajagopal, R.
  Thermal Profiling of Residential Energy Use
  IEEE Transaction on Power Systems (2014),30(2): 602-611.
• Albert, A., Rajagopal, R.
  Building dynamic thermal profiles of energy consumption for individuals and neighborhoods
  IEEE International Conference on Big Data (2013).
• Kavousian, A., Rajagopal, R.
  Data-Driven Benchmarking of Building Energy Efficiency Utilizing Statistical Frontier Models
  Journal of Computing in Civil Engineering (2013), 28(1): 79-88.
• Kavousian, A., Rajagopal, R., Fischer, M.
  Determinants of residential electricity consumption: Using smart meter data to examine the effect of climate, building characteristics, appliance stock, and occupants' behavior
  Energy (2013), 55: 184-194.
• Kavousian, A., Rajagopal, R., Fischer, M.
  Ranking appliance energy efficiency in households: Utilizing smart meter data and energy efficiency frontiers to estimate and identify the determinants of appliance energy efficiency in residential buildings
  Energy and Buildings (2015), 99: 220-230.
• Sevlian, R., Rajagopal, R.
  Value of Aggregation In Smart Grids
  2013 IEEE International Conference on Smart Grid Communications (SmartGridComm), 2013: 714-719.
• Noh, H. Y., Rajagopal, R.
  Data-Driven Forecasting Algorithms for Building Energy Consumption
  Sensors and Smart Structures Technologies for Civil, Mechanical and Aerospace Systems, 2013: 8692.
• Patel, S., Sevlian, R., Zhang, B., Rajagopal, R.
  Aggregation for Load Servicing
  2014 IEEE PES General Meeting| Conference & Exposition. IEEE, 2014.
• Patel, S., Sevlian, R., Rajagopal, R.
  Distribution System Load and Forecast Model
  arXiv preprint arXiv:1407.3322 (2014).
• Sevlian, R., Rajagopal, R.
  Short Term Electricity Load Forecasting on Varying Levels of Aggregation
  arXiv preprint arXiv:1404.0058 (2014).
• Li, P. , Zhang, B. ,Weng Y., Rajapogal R.
  Autoregressive Model for Individual Consumption Data -LASSO Selection and Significance Test
  arXiv preprint arXiv:1511.01853v1 (2015)
• Weng Y., Rajagopal R.
  Probabilistic Baseline Estimation via Gaussian Process
  2015 IEEE Power & Energy Society General Meeting. IEEE, 2015.
• Kalathil, D., Rajagopal, R.
  Online Learning For Demand Response
  53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton). IEEE, 2015.
• Sevlian, R., Rajagopal, R.
  A Model For The Effect of Aggregation on Short Term Load Forecasting
  IEEE Power and Energy Society General Meeting. 2014.
• Patel, S., Sevlian, R., Zhang, B., Rajagopal, R.
  Pricing Residential Electricity Based on Individual Consumption Behaviors
  arXiv preprint arXiv:1312.1243 (2013).
• Patel, S., Borgeson, S., Rajagopal, R., Spurlock, A., Jin,L., Todd, A.,
  Time Will Tell: Using Smart Meter Time Series Data to Derive Household Features and Explain Heterogeneity in Pricing Programs
  In progress, 2016.

The VISDOM Team

VISDOM Partners

Funding Agencies