• UA

    Utility Analytics

    Utility Analytics

    Providing the intelligence to make informed decisions.

    Providing the intelligence to make informed decisions.

With the rapidly increasing volumes of data available in the business landscape today, specialised skills and techniques are essential to mine and extract the inherent value.

We’ve been at the forefront of utility analytics since the late 1990’s.

Our specialist team comprises highly experienced and qualified statisticians, engineers, mathematicians and IT professionals who are passionate about scientific, statistical and technological advancements to support customer decision-making.

We manage extremely large data sets using state-of-the-art agile concepts, and adopt an iterative delivery process to ensure quick demonstration of value. Applying these techniques minimises the traditional gaps between modeller, client and end-user, to simplify problems and solve operational challenges in the utility, energy regulator, telecommunication and retail environments.

Services

  • Losses Analytics and Revenue Assurance.
  • Load Research.
  • DSM Programme Impact Analysis.
  • Tariff Impact Modelling and Analysis.
  • Load Forecasting
    Short, long-term and geo-based.
  • Primary Energy
    Coal stockpile simulation; Constraints and scheduling; Renewable modelling; Losses statistics.
  • Network Planning
    Standardised profiles; Growth curves; Energy densities; Co-incidence and diversity modelling.
  • Pricing
    Tariff impacts; Momentum; Technology and economic impact modelling; Simulation; Cost of supply studies; Price, income and substitution elasticities.
  • Engineering
    Quality of supply; Power flow modelling; Losses clustering; Tracking.
  • Demand Side Management
    Technology impact modelling.
  • Market Intelligence
    Textual mining; Sentiment analysis; Auto summarisation; Spatial geocoding.
  • Asset Management
    Simulation of stock levels; Asset failure rates.
  • Customer Services
    Customer behaviour; Response and monitoring; Load profile; Disaggregation and segmentation modelling.
  • Visual Analytics
    Information visualisation; Interactive and graphic design; Data transformations and representation.
  • Multivariate Statistics
    Principle components; Factor analysis; Cluster analysis; Discriminate analysis.
  • Time Series Analysis
    ARIMA; Spectral analysis; Error correction models; Various exponential smoothing methods.
  • Machine Learning
    Decision trees; Artificial neural networks; Genetic programming; Bayesian networks; Random forest.
  • Correspondence Analysis
    Contingency tables; Multiple correspondence analysis; Categorical data.

Output Examples

Example 1
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