Hydro-electric Plant Predictive Maintenance

Customer

Swiss Energy Company

Building a predictive maintenance model to detect abnormal measurements earlier, leading to better maintenance planning and cost reduction

Challenge

  • Currently no reliable long-term anomaly detection system
  • Slow-shifting anomalies are hard to detect with human surveillance as they can take months
  • Goal to have a predictive maintenance system to spot these issues early and plan maintenance more effectively

Solution

  • Unit8 built a predictive machine learning model for physical variables of interest (e.g., temperature or vibration)
  • Define a threshold for values to be throughout the day and sends automatic alerts if a value differs significantly from model predictions

Business impact

  • Engineers able to detect abnormal changes in measurements much earlier
  • Maintenance can now be planned effectively
  • Shorter stops in electricity production leading to lower costs due to plant shutdown

Electricity Price Forecasting

Customer

Energy Company

Developing forecasting model to predict electricity prices and help traders make better decisions resulting in a 25% reduction in deviation compared to previous models in place

Challenge

  • Client needs accurate electricity price forecasts for internal trading team
  • Goal to improve price forecasting using a fixed train/valid/test reference dataset
  • Need for a new probability forecast to be implemented

Solution

  • Built backtesting pipeline and test multiple forecasting configurations (multivariate time series)
  • Developed forecasting solution which generated probabilistic forecasts with consistent prediction intervals

Business Impact

  • Unit8 model significantly improved forecasts compared to previous solutions in place
  • Reduced deviation between forecasted and actual prices
  • by 25% compared to previous model

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