Elevating Energy Demand Accuracy through Machine Learning Pipelines

The Solution

Big Blue AI developed an advanced Machine Learning forecasting pipeline and custom web intelligence dashboard for PPC (Public Power Corporation), evaluating external provider predictions against actual grid data to refine short-term nationwide power consumption forecasts.

• Automated Multi-Source Ingestion Pipeline: Built automated data pipelines via APIs and web scrapers storing real-time hourly load forecasts from five external providers alongside actual national consumption metrics in a centralized PostgreSQL database hosted on MS Azure.
• Smart Forecast Combination Model: Created an AI prediction model that automatically blends forecasts from multiple providers with calendar and holiday data to generate a single, highly accurate master forecast.
• Interactive Provider Evaluation Dashboard: Implemented a lightweight, web-based UI to continuously monitor provider accuracy, calculate error metrics across peak and off-peak hours, and visualize real-time demand.
• Feature Extraction Engine: Engineered temporal indicators including hour of day, day of week, day of month, and festive-day flags to account for holiday demand shifts and seasonal consumption patterns.

The Challenge

PPC, Greece’s largest power producer and supplier, relies heavily on short-term electricity load forecasts to make high-stakes operational and trading decisions. PPC routinely received conflicting forecasts from multiple third-party prediction providers, making it difficult to determine which source was most reliable at any given hour. To minimize financial exposure in energy markets, PPC required an automated platform to continuously audit provider accuracy and construct a superior, unified machine learning forecast that outperformed all individual third-party inputs.

The Impact

The Machine Learning solution significantly surpassed traditional third-party predictions, delivering measurable accuracy gains for PPC's grid and trading operators: 

• 22% Error Reduction: Lowered the forecasting error from 3.45% to 2.67% with the XGBoost model, cutting prediction errors by over 22%.
• Precision Load Margin: Achieved a global model error margin of just 1.38% during validation, representing an error window of only 55 to 110 Megawatt-hours on national grid loads ranging between 4,000 and 8,000 Megawatt-hours.
• Proproved Financial Outcomes: Delivered a higher-precision decision-making tool that directly reduces grid balancing costs and financial risk in day-ahead energy trading.
• Automated Provider Auditing: Replaced manual evaluations with an automated dashboard that dynamically highlights the most accurate forecasting provider for any specific hour or calendar condition.

How Big Blue AI helped 

Big Blue AI led the data pipeline design and Machine Learning modeling, orchestrating automated ETL routines on MS Azure, feature engineering for festive and temporal variables, and hyperparameter tuning of XGBoost regressors. By converting fragmented provider feeds into a unified AI ensemble model and delivering an intuitive Dash web application, Big Blue AI equipped PPC with a scalable decision-support ecosystem for national energy forecasting.