AI-based energy consumption forecasting system

Status:

Completed

Duration:

4 months

Service:

AI/ML system development

Integrations:

LightGBM and Python-based infrastructure, integrated with meteorological data sources and energy APIs

Project Objective

Build an advanced ML model that uses historical energy consumption data, weather conditions, customer segmentation, and contextual factors — such as air raid alerts — to produce highly accurate monthly forecasts with confidence scores for over 700 electricity consumers

Client

Ukrainian energy company (under NDA)

Location:

Kyiv, Ukraine

Project Goal

Minimize financial losses from inaccurate energy consumption forecasting by increasing prediction accuracy from 45–50% MAPE to under 30% and introducing a comprehensive forecast risk evaluation system

More than 40 engineered features

Resilience in wartime conditions

Seasonal trend adaptation

Automatic anomaly detection

Hybrid recognition approach with accuracy assessment

Features

High prediction accuracy

Forecasting accuracy improved from 45–50% MAPE to 29.9% thanks to gradient boosting techniques and advanced feature engineering

Prediction uncertainty estimation

Confidence scores between 0.1 and 0.9 enable automatic detection of high-risk forecasts, ensuring that expert review is prioritized for the most uncertain predictions

Adaptation to the Ukrainian context

The system incorporates the influence of air raid alerts into forecasts, with special consideration for educational facilities and industrial energy consumers

Category-based segmentation

The model incorporates the unique profiles of 26 consumer categories — from educational institutions to industrial facilities — ensuring more accurate and context-aware energy forecasts

Equipment

LightGBM framework, Python ML stack, meteorological APIs, and energy data platforms

Equipment

Every project is powered by the latest in technological innovation

Results

We built an AI-driven forecasting system that reduced prediction error from 45–50% MAPE to 29.9%. The model automatically processes 65% of forecasts with high confidence and routes 35% for expert validation. This improvement enables a 15–20% reduction in imbalance penalty costs

Automated processing of 65% of forecasts.

May 2025 – September 2025

Result BEFORE

Result AFTER

Результат ПІСЛЯ

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What Our Clients Say

“The team deeply immersed itself in the specifics of the Ukrainian energy market and the wartime context. This was evident in every aspect — from feature selection to incorporating air raid alerts into the forecasts. Thanks to the solution, forecasting accuracy nearly doubled, and imbalance penalty costs were significantly reduced. We’ve found a reliable partner who understands the business and thinks one step ahead.”