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

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