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Exploration of Smart Meter Data for Household Composition Analysis

This seminar in Geneva, Switzerland, discusses the integration of smart meter data to investigate household composition using machine learning algorithms. The session covers data, methodology, results, resources, and review, emphasizing the exploration of new statistical data sources.

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Exploration of Smart Meter Data for Household Composition Analysis

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  1. Exploration of electricity usage data from smart meters to investigate household composition Topic (v): Integration and management of new data sources Seminar on Statistical Data Collection Geneva, Switzerland, 25-27 September 2013 Paula.Carroll@ucd.ie John.Dunne@cso.ie Michael.Hanley@ucdconnect.ie Tadhg.Murphy.1@ucdconnect.ie

  2. Overview • Setting the scene • The data • Problem statement • The methodology • Some results • The resources • Team review • CSO review • Concluding remarks

  3. Setting the Scene -the players

  4. The data • Over 5000 households in pilot • 3 months baseline data (reading every 30 mins) • Pre-trial survey using CATI Purpose : Consumer Behaviour Trials in 2009 and 2010

  5. Problem statement To determine household composition using smart metering data

  6. The methodology • Machine learning algorithms for classifier • (learning and testing || generalisation) • Neural Networks used • Binomial and Multinomial classification • Unbalanced data • Data reduction/ dimension reduction • Used 21 explanatory variables as input to classifier • Variables normalised

  7. Some results – balanced multinomial classifier “Confusion matrix”

  8. The resources • Project team of two persons for 3 months • Significant amount of time spent manipulating data • Software: R with nnet and neuralnet packages • Hardware: Required considerable computer resources for manipulating full dataset (Stokes at ICHEC)

  9. Team review Problem statement too specific - broaden to household characteristics Alternative approach (cluster analysis and then describe clusters) Other techniques – PCA or signal processing

  10. CSO review – forward looking Assuming go live 1.5m household meters linked to statistical household register in 2019 Existing statistical needs • Field force management • Auxiliary information • Sample selection /Representivity analysis New statistical products? • Energy consumption patterns by location, household etc • Quality of life (time to rise, time to bed)

  11. Concluding remarks 3 V’s + V for Value – Is there value in SMD Access v Privacy • Legal, moral, proportionality Infrastructure for Big data (1.5m data points every 30 mins) • Outsourcing, downsampling New tools, skills, approaches Roadmap – collaboration with suitable partners

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