A domain knowledge framework bridging energy engineering fundamentals with data science and machine learning for building energy optimization. Developed as part of ongoing research toward a methodological contribution: A Framework for ML Deployment in Building Energy Management Systems.
Focus: Systematic documentation of German heating standards (DIN, VDI, GEG) with applied ML methodologies for residential heating system optimization. Structured using Take AI Bite, a framework for human-AI collaboration powered by the Deliberate Systematic Methodology (DSM).
The energy sector faces a critical skills gap. According to the IEA World Energy Employment 2025 report, 60% of energy companies report labor shortages, with particular demand for data analytics and ML expertise. Meanwhile, academic reviews note that resources bridging ML research and building energy applications remain fragmented across disciplines.
This repository addresses that gap through:
- Comprehensive documentation of German heating standards for data scientists, ML engineers, and data engineers
- ML methodologies specifically adapted for energy time series analysis
- Production-grade MLOps and data engineering patterns for IoT and sensor data systems
- Applied case studies demonstrating real-world optimization approaches
| Part | Topic | Scope |
|---|---|---|
| Part I | Domain Fundamentals | Thermodynamics, heating system types, German standards |
| Part II | Data Science & ML | Time series analysis, forecasting, anomaly detection, optimization |
| Part III | Production & MLOps | Data pipelines, deployment patterns, monitoring |
| Part IV | Technical Stack | Python, SQL, GraphQL, API design |
| Part V | Applied Scenarios | Case studies, system design, cross-functional collaboration |
| References | References | Academic sources, regulations, technical glossary |
- Data Scientists, ML-, and Data-Engineers transitioning into energy and building optimization domains
- Energy Engineers integrating ML and data-driven approaches into practice
- Researchers in building science, smart buildings, and energy efficiency
- Graduate Students in energy systems, building physics, or applied ML
Systematic review of residential heating system engineering:
- Chapter 1: Thermodynamic principles (heat transfer mechanisms, thermal mass, U-values)
- Chapter 2: Heating system typology (gas boilers, heat pumps, district heating, CHP)
- Chapter 3: Control parameters (Heizkennlinie, Vorlauf/Rücklauf temperature management)
- Chapter 4: Hydraulic balancing methodology (Hydraulischer Abgleich)
- Chapter 5: Sector coupling (PV + heat pump + storage integration)
Applied machine learning methodologies for energy optimization:
- Chapter 6: Time series fundamentals for energy data
- Chapter 7: Heat demand forecasting and energy production prediction
- Chapter 8: Anomaly detection frameworks for heating systems
- Chapter 9: Control and optimization algorithms (MPC, reinforcement learning)
- Chapter 10: Supervised learning applications
- Chapter 11: Unsupervised learning (building clustering, load profile segmentation)
Deployment and operational considerations for ML systems:
- Chapter 12: Data pipeline architecture for IoT/energy systems (MQTT, TimescaleDB)
- Chapter 13: Production-grade algorithm development
- Chapter 14: MLOps frameworks (MLflow, experiment tracking)
- Chapter 15: Deployment patterns (batch inference, real-time scoring, edge deployment)
Implementation and data engineering reference for energy systems:
- Chapter 16: Python for energy data science (Pandas, NumPy, scikit-learn)
- Chapter 17: Data access patterns (SQL window functions, GraphQL, REST APIs)
Case study analysis and system design:
- Chapter 18: Case study walkthroughs (district heating, heat pump systems, building portfolios)
- Chapter 19: System design exercises
- Chapter 20: Cross-functional collaboration patterns
| German | English | Technical Context |
|---|---|---|
| Heizkennlinie | Heating curve | Flow temperature as function of outdoor temperature |
| Vorlauftemperatur | Flow/supply temperature | Water temperature leaving the heat source |
| Rücklauftemperatur | Return temperature | Water temperature returning to heat source |
| Spreizung | Temperature spread | Difference between flow and return (Vorlauf - Rücklauf) |
| Brennwertnutzung | Condensing operation | Recovering latent heat from flue gas |
| Hydraulischer Abgleich | Hydraulic balancing | Optimizing flow rates across heating circuits |
| Nachtabsenkung | Night setback | Reducing heating during unoccupied hours |
| Wärmepumpe | Heat pump | Device using refrigeration cycle for heating |
| Fernwärme | District heating | Centralized heat distribution network |
| BHKW | CHP (Combined Heat & Power) | Simultaneous electricity and heat generation |
| Anschlussleistung | Connection capacity | Contracted power for district heating |
| Wärmegestehungskosten | Heat generation costs | Total cost per kWh of useful heat |
See 06_References.md for complete glossary.
| Metric | Typical Range | Significance |
|---|---|---|
| Flow temperature at 4°C outdoor | 50-70°C | Lower values indicate higher efficiency |
| Return temperature for condensing | < 55°C | Required threshold for gas boiler condensing operation |
| Heat pump COP | 3-5 | Coefficient of performance; higher = more efficient |
| Optimization savings potential | 10-20% | Achievable through control optimization |
| Systems without night setback | ~70% | Represents immediate optimization opportunity |
| Heat-pump-ready buildings | ~17% | Without major renovation requirements |
| Regulation | Scope |
|---|---|
| GEG (Gebäudeenergiegesetz) | Building energy efficiency standards |
| GEG §60b | Mandatory heating system inspection requirements |
| GEG §60c | Hydraulic balancing requirements |
| DIN EN 12831 | Heat load calculation methodology |
| VDI 2067 | Economic calculation for energy systems |
| VDI 6030 | Radiator sizing and selection |
This project is part of a three-repository system:
| Repository | Role | Content |
|---|---|---|
| Residential Heating DS Guide (this repo) | Framework & Theory | Domain knowledge, methodology, German standards documentation |
| Residential Energy Apps (Live App) | Implementation | Heating curve simulator, interactive Streamlit applications |
| Take AI Bite (DSM) | Meta-Methodology | Human-AI collaboration framework powered by the Deliberate Systematic Methodology |
Integration: Theory (this repo) is validated by Practice (code repo), executed using the Meta-methodology (DSM).
Contributions are welcome. This repository aims to serve as a professional reference for the energy transition.
Contribution areas:
- Technical corrections and clarifications
- Code examples and implementation notebooks
- Translation to other languages
- Anonymized case studies
See CONTRIBUTING.md for guidelines.
Alberto Diaz-Durana Freelance Data Scientist & ML Engineer | 10+ Years Experience
- MSc Process, Energy & Environmental Systems Engineering (TU Berlin)
- PhD Candidate, Energy Planning & Machine Learning (TU Berlin) -- CPOTE 2020 publication
- Specialization in building energy systems, thermo-economic modeling, and applied ML for energy optimization
- Production ML experience across energy, cement manufacturing, and process mining domains
- GitHub | LinkedIn
- Website | Blog
This work is licensed under Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0).
You are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose
Under the following terms:
- Attribution — Give appropriate credit and indicate if changes were made
- ShareAlike — Distribute contributions under the same license
- German heating standards documentation based on DIN, VDI, and GEG regulations
- Weather data patterns informed by DWD (Deutscher Wetterdienst) open data
- Load profiles referenced from BDEW standard load profiles
Status: Active development Last Updated: March 2026