How Opower used Deep Learning-Based Software to Segment the Energy MarketEstimated Reading Time: just 2 min

Artificial Intelligence (AI) is the intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other living beings.

For a machine acting like a human means being able to perform 5 macro-functions:

  • Perception (recognizing and interpreting reality)
  • Reasoning (drawing new conclusions)
  • Planning (detecting patterns and adapting to new environments)
  • Knowledge Representation (extract and store knowledge)
  • Natural Language Processing (communicate successfully in a human language)

AI research started in 1956 after a workshop at Dartmouth College and its fathers where:

  • Allen Newell and Herbert Simon from CMU
  • John McCarthy and Marvin Minsky from MIT
  • Arthur Samuel from IBM

Dartmouth College group focused on Restricted AI that solves only one of the 5 acting like a human macro-functions at a time.

Recently, a huge breakthrough came with the birth of Machine Learning.

Machine Learning is a field of artificial intelligence that uses statistical techniques to give computer systems the ability to learn from data, without being explicitly programmed.

This step ahead allowed systems to solve problems that involve:

  • Supervised Learning
  • Clustering
  • Dimensionality Reduction
  • Structured Prediction
  • Anomaly Detection

Another breakthrough came with Deep Learning.

Deep Learning is a subset of Machine Learning that, using architectures based on Neural Networks, allows machines to learn a large variety of activities as humans do.

This step ahead allowed systems to solve problems that involve:

  • Supervised/Semi-Supervised/Unsupervised Learning
  • Image/Audio Recognition
  • Natural Language Processing

Nowadays, there are 3 main problems that can be solved with AI

  1. Regression Problems: this could be applied whenever the output of a subset of inputs is a continuous function
  2. Classification Problems: this could be applied whenever the output of a subset of inputs is a discrete function
  3. Clustering Problems: this could be applied whenever the output of a subset of inputs is not predictable/known

The 3 problems are solved by using sequences of algorithms called Neural Networks.

Usually, every Neural Network performs 4 activities:

  1. Data Collection
  2. Data Cleaning
  3. Patterns Identification
  4. Prediction Making

One of the most interesting applications of Artificial Intelligence is Opower’s Energy Clustering.

Opower was a company that provided a SaaS customer engagement platform for utilities and it is now part of Oracle Corporation since its acquisition in 2016.

One of the biggest issues an energy company faces is to forecast the energy consumption of its customers.

In fact, everyone has a different usage of energy and this is shown in huge peaks and valleys in the daily energy demand.

To cope with this up and down request it is not possible to use any kind of battery since they have a cost and a duration that doesn’t justify them.

In 2014, Opower used Artificial Intelligence in order to perform a clusterization of data related to weather-normalized hourly electricity consumption for a typical weekday.

The data from a random sample of 1,000 residential utility customers are displayed in the graph below. Opower performed an analysis of 812,000 utility customers.

The idea was to use unsupervised Machine Learning for solving a Clustering Problem since the output was unknown.

As an outcome, the algorithm was able to identify 5 clusters of typical customers as per the analysis displayed in the graph below.

Such a tool is not only super-human since it able to analyse a huge amount of data very quickly, but it also allows companies to:

  • Segment their customers to propose them tailored offers
  • Forecast their customers’ consumptions in order to size their capacity

This will potentially change the way Energy business runs.

Nicola Zaffonato Administrator

Business Strategy | Product Marketing | Executive Master eCommerce Management | Business Innovation Master | MSc

I am driven by my personal growth and of people/contexts that surround me.

I followed a professional path in Valentino Fashion Group and Luxottica during which, thanks to the ability to understand different businesses and interests, I was able to succeed in Operations, Merchandising and Retail.

These organizations have exploited my ability to mediate and translate needs/constraints into practice, assigning me to Project Management roles.

Luxottica relied on my ability to analyze, to anticipate things and to imagine/implement solutions by appointing me in Supply Chain Management department and assigning me to the Product Management of IoT solutions for Anti-counterfeiting and Retail digitalization.

During this professional path, I also developed my leadership by managing teams to build Processes, Organizations, Systems and Governance Tools.

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