Energy Demand – Predicted with Precision
Accigo AB
Energy Demand – Predicted with Precision is a service offering built on Microsoft Fabric and Azure to deliver accurate, production-grade load forecasts of the energy demand. The solution combines statistical baselines and modern machine learning with robust MLOps to predict customer energy demand using calendar and weather drivers—enabling efficient production planning, reduced imbalance exposure, and improved asset utilization.
Grounded in a power-plant–proven approach to forecasting energy demand, the offering operationalizes best practices for data ingestion, feature engineering, and model training. It also implements monitoring and retraining in Fabric’s Lakehouse and ML environments. Full lifecycle management is handled through MLflow and automated pipelines.
KEY FEATURES AND CAPABILITIES
- Forecasting engine tailored to district heating
- Hybrid approach with statistical baselines and machine learning models (e.g., gradient-boosted trees/XGBoost) for hourly and day-ahead predictions of total energy consumption.
- Rich feature set combining:
- Calendar features: Hour, Day, Month, is_working_day
- Weather drivers: solar irradiance, air temperature, relative humidity, wind speed, and wind direction
- Extensible to additional internal/external signals
- Proven model metrics tracked: MAPE, MAE, RMSE
- Fabric-native data foundation
- Data sources: Your data warehouse, weather forecast, and other internal/external feeds flowing into Lakehouse via pipelines/notebooks.
- Three ML environments for robust Software Development Life Cycle (SDLC)
- Controlled deployment pipeline promoting artifacts Dev → Test → Prod for reliable releases
- Scalable, modular architecture
- Lakehouse as the central, scalable data platform with automated pipelines and modular components enabling extensibility and streamlined maintenance
KEY BENEFITS
- Lower imbalance risk and cost Forecasts that incorporate weather sensitivity and calendar effects help minimize forecast errors that lead to imbalance exposure; built-in drift and performance checks ensure sustained accuracy over time.
- Higher asset efficiency and profitability Better alignment of production to demand reduces over/under-production, supports optimal unit commitment, and improves utilization of power assets.
- Faster, safer path to production Dev/Test/Prod environments, a controlled promotion pipeline, and MLflow experiment tracking accelerate delivery while maintaining rigor and traceability.
- Continuous reliability Automated PSI-based data drift detection and metric-based retraining triggers (e.g., production MAPE > 20% above training) keep models fresh and robust as conditions evolve.
ARE YOU READY….
…to transform your load forecasting into operational precision? We are with you all the way!
Contact us via Azure Marketplace to schedule your discovery workshop and accelerate a production-ready forecasting solution built on Microsoft Fabric—so you can power up, precisely when needed!
ABOUT ACCIGO
We are your partner with deep expertise in Data & AI, helping organizations simplify technology, modernize processes, and build intelligent applications on Azure and Power Platform. Our combination of strategy, facilitation, design thinking, and Azure engineering ensures practical results and a clear path from ideas to agent-driven processes.