Presented By O’Reilly and Cloudera
Make Data Work
September 11, 2018: Training & Tutorials
September 12–13, 2018: Keynotes & Sessions
New York, NY

Predicting residential occupancy and hot water usage from high-frequency, multivector utilities data

Cris Lowery (Baringa), Marc Warner (ASI)
11:20am–12:00pm Thursday, 09/13/2018
Data science and machine learning
Location: 1A 08 Level: Intermediate
Secondary topics:  Temporal data and time-series analytics
Average rating: ****.
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Who is this presentation for?

  • Data scientists, engineers, and energy and utility experts

Prerequisite knowledge

  • A basic understanding of unsupervised and supervised learning algorithms

What you'll learn

  • Learn an approach to building a big data predictive model and the key design considerations to take into account
  • Understand nonintrusive means to predict occupancy and hot water usage in residential properties

Description

In EU households, heating and hot water alone account for 80% of energy usage. If we could predict when a resident requires heating or hot water, we could then optimize energy use to meet these needs without the need for direct interaction by the householder, taking account of real-time price signals. This could deliver significant financial, societal, and environmental benefits.

Cristobal Lowery and Marc Warner share a nonintrusive machine learning approach to predict the residents’ needs. The project, led by the UK’s Energy Technologies Institute, uses data from electricity and water utility meters together with internal humidity and temperature measurements from five residential properties. The data was recorded at very high frequency, up to 200,000 readings a second for electricity. This generates 3 TBs of data a month for a property. Hence, a fully exploratory approach to developing the data features is unrealistic. As such, the algorithm was structured using a more traditional modeling approach. A key consideration in the work was how to achieve data compression while minimizing information loss, which forced the team to extrapolate domain knowledge to unexplored territories.

Another key consideration of the work was how such a system could be productionized given the significant data volumes, which pose both storage and memory challenges for a reasonably priced device. Cristobal and Marc outline a potential solution and approach, which uses cheap hardware to achieve the necessary compression, starting with a Fourier transform, which can be achieved through a relatively cheap chip.

Photo of Cris Lowery

Cris Lowery

Baringa

Cristobal Lowery is team lead for Baringa’s modeling and machine learning centre of excellence, where he led the creation of Baringa’s data science and analytics team and supported our clients in their journeys to become leaders in artificial intelligence. Previously, he was an independent data science consultant in an investment bank and for a leading Formula 1 team. Cristobal is a passionate advocate of artificial intelligence and its potential to transform businesses. He holds two first-class master’s degrees in quantitative subjects and has published and patented a machine learning system.

Photo of Marc Warner

Marc Warner

ASI

Marc Warner is the cofounder and CEO of ASI Data Science. He founded ASI in the belief that the benefits of AI should extend to everyone and has shaped the company so that it can support organizations of all shapes and sizes to take advantage of rapid advances in this field. In the two years since founding ASI, Marc has overseen its growth to more than 50 employees and expanded its scope from a small fellowship scheme to a cutting-edge range of software, training, project, and advisory services. He has led over 50 data science projects for clients ranging from multinational companies like EasyJet and Siemens to the UK government and NHS. His work has been covered by the BBC, the Telegraph, the Independent, and many more. Previously, Marc was the Marie Curie Fellow of Physics at Harvard University, studying quantum metrology and quantum computing. His PhD research, in the field of quantum computing, was awarded the Stoneham prize and was published in Nature and covered in the New York Times.