Industrial Energy Forecasting
The plant suffered from direct financial penalties due to deviations (both in excess and defect) in the electricity consumption forecasts communicated to the provider. The existing manual estimation process lacked a predictive system capable of automatically integrating critical variables such as production history, future manufacturing orders, materials, and scheduled technical stops. This lack of data integration limited the accuracy of estimates and exposed the company to recurrent, unnecessary operational costs.
We architected a Machine Learning regression framework (Tree-based algorithms) to forecast future consumption by modeling the specific energy intensity of each manufacturing order. The solution acts as an intelligent layer that fuses historical consumption patterns with forward-looking production variables to estimate electrical demand with high granularity. This adaptive model allows for the precise allocation of energy loads to specific production windows, enabling the simulation of various planning scenarios to optimize financial budgeting and eliminate penalty risks.
The system drastically reduces operating costs by minimizing or eliminating economic penalties for deviations in contracted energy, thanks to highly forecasting precision. Additionally, it facilitates more reliable financial and budgetary planning and allows for simulating the energy impact of different production plans. This helps in making more efficient decisions regarding the sequencing of manufacturing orders and proactively optimizing plant operations.
Turbine Uptime Engine
SkyPredict
