Revenue Estimation & Demand Forecasting for Electricity Company

Better projections and decisions can be made based on the data you have. An example from the Energy industry.

Energy

Data Science

Deep Learning

Industry

Energy

Service

Custom AI Development

UTE is the national company responsible for the generation, distribution, and commercialization of electric power in Uruguay. The company serves millions of residential and commercial customers and seeks to integrate modern technologies like smart meters to improve its operations.

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The Challenge

UTE needed to anticipate revenue and operational needs by using their client's data. Key challenges included:

Variability in Consumption: Household energy use is heavily influenced by a wide array of fluctuating factors, including temperature, humidity, and rainfall.

Cultural Influences: Consumer behavior shifts significantly during specific times such as holidays or cultural events.

Plan Complexity: Translating raw consumption data into accurate revenue projections required accounting for multiple residential and non-residential plans.

Strategic Planning Gaps: Without precise mid-to-long-term forecasts, it was difficult to optimize infrastructure investment and annual budgeting.

The Solution

Our team built a system that enabled UTE to transform their existing data into actionable projections.

1. Strategic Data Grouping (Clustering)

To manage data variance and improve accuracy, the system groups customers into clusters based on similar consumption patterns and habits. This approach allows for more precise modeling than broad global averages or highly uncertain individual estimates.

2. Advanced Predictive Modeling

The system employs a hybrid modeling approach mixing traditional statistical methods, advanced deep learning architectures and even incorporates external variables such as weather forecasts and cultural calendars to refine prediction

3. Dynamic Revenue Projection

The consumption forecasts are mapped against existing subscription plan structures to generate:

Monthly Projections

Annual Projections

Probabilistic Scenarios

4. Integrated Dashboard

A user-friendly dashboard was implemented to allow staff to visualize historical data, compare projections, and automatically update forecasts as new data becomes available.