Forecasting Energy Consumption for an Industrial Park

Challenge

An industrial park gained the ability to purchase electricity directly from suppliers, bypassing intermediaries.
To optimize costs, it needed accurate daily consumption forecasts — since exceeding predicted volumes significantly increased the cost per kWh.
Additionally, forecasting peak loads was crucial for preventing overloads and managing infrastructure stress.

Solution

The Hidden Core team developed a custom AI-driven forecasting system based on historical data from the park’s automated energy monitoring system.
The dataset was enriched with external factors such as weather conditions, calendar events, and operational data influencing electricity consumption.

A hybrid time series architecture was designed, combining transformer-based models and XGBoost to predict both regular and peak consumption patterns.
The trained models were integrated into a unified data pipeline that automates data ingestion, forecasting, and result delivery to operators.

Impact

The solution achieved 95% forecast accuracy, enabling the client to reduce electricity procurement costs by 15–25%.
In addition, weekly forecasts provided better visibility for maintenance planning and load management across the park’s infrastructure.

Hidden Core Contribution

Our team delivered the full AI solution lifecycle — from data preparation and model development to pipeline deployment and integration into the client’s operational ecosystem.