Challenge

When Demand Outpaced Data Visibility
A renewable energy company struggled to accurately predict energy consumption across multiple regions. Data was siloed in local systems with limited integration between metering, weather, and customer datasets.

Forecasting models were manually run in Excel, leading to delayed and inconsistent outputs. Decision-makers couldn’t visualize generation-to-demand ratios in real time, affecting energy distribution efficiency.

Solution

Cloud-Driven Forecasting Intelligence
Wilco IT Solutions built a centralized Snowflake data warehouse hosted on Microsoft Azure to consolidate weather feeds, smart meter data, and historical consumption records.

We used Tableau to design interactive dashboards showing load curves, forecast accuracy, and generation efficiency by region. Machine learning models in Snowflake’s native compute layer were implemented to predict hourly demand spikes, enabling pre-emptive energy allocation.

Impact

Smarter Forecasts, Efficient Energy

• Forecast accuracy improved from 72% to 95%.
• Energy distribution decisions now made in real time.
• Enabled proactive resource allocation during peak hours.
• Reduced overproduction waste, supporting sustainability goals.