About this training
Generation forecasting, smart grid, predictive maintenance and EPDK-compliant AI architecture — an end-to-end hands-on program tailored to Türkiye's energy sector.
This training is designed for: Digital transformation and data team leads at generation and distribution companies Renewable plant operators (wind, solar, hydro) and YEKA tender winners EPDK compliance, licensing, regulatory and algorithmic-supervision readiness units SCADA, OT, EMS/DMS software teams and engineers working at the OT-IT bridge Energy trading desks (balancing market, GÖP, GİP, bilaterals) and traders OSB energy management units, cogeneration operators and industrial energy managers
Why this course matters: A rare AI training program in Türkiye dedicated specifically to the energy sector Concrete preparation for EPDK's 2026 algorithmic supervision and reporting expectations End-to-end AI data strategy for Net Zero 2053, CBAM and Türkiye ETS compliance Use-cases that unlock the real value of smart meter (AMI), SCADA and OT data Direct ROI scenarios for generation forecasting + balancing market profitability KVKK- and EU AI Act-compliant architecture, anonymization and data residency guidance
Learning outcomes by the end of the programme: Ability to design an end-to-end smart grid AI architecture Selecting, training and measuring MAPE of generation forecasting models for wind, solar and hydro A predictive maintenance pilot roadmap and a sensor-data strategy An approach to enrich SCADA + AMI + weather + EPİAŞ data with AI Building a compliant architecture under EPDK, KVKK, the EU AI Act and CBAM Designing AI-based signal and bid optimization for the Balancing Market, GÖP and GİP
Prerequisites and recommended background: General familiarity with energy sector basics (generation, transmission, distribution, trading) Use of Excel or basic data analysis tools A laptop for the training (lab exercises run in the cloud) Basic English reading for model documentation A rough sense of your company's AI maturity (we run a short pre-training survey)
- Content framed around Türkiye's energy market (EPDK, EPİAŞ, TEİAŞ)
- AI architecture guidance aligned with EPDK and EU AI Act requirements
- Hands-on labs on smart meter (AMI) and SCADA data
- Generation forecasting models and MAPE improvement for wind, solar and hydro
- Predictive maintenance and RUL scenarios for transformers, turbines and inverters
- AI signal generation for Balancing Market, GÖP and GİP optimization
Key Takeaways
- Ability to design an end-to-end smart grid AI architecture
- Selecting, training and measuring MAPE of generation forecasting models for wind, solar and hydro
- A predictive maintenance pilot roadmap and a sensor-data strategy
- An approach to enrich SCADA + AMI + weather + EPİAŞ data with AI
- Building a compliant architecture under EPDK, KVKK, the EU AI Act and CBAM
- Designing AI-based signal and bid optimization for the Balancing Market, GÖP and GİP
AI for Energy: Smart Grid, Generation Forecasting & Predictive Maintenance Training
Generation forecasting, smart grid, predictive maintenance and EPDK-compliant AI architecture — an end-to-end hands-on program tailored to Türkiye's energy sector.
About This Course
About This Training
Türkiye is entering a critical digital transformation period for the energy sector — driven by the Net Zero 2053 target, rapid renewable capacity growth, the maturing balancing market, and EPDK's algorithmic supervision agenda. Generation companies, distribution operators, industrial-zone (OSB) energy units and YEKA investors are turning AI from a "nice to have" into an operational necessity for generation forecasting, predictive maintenance, non-technical loss analytics and balancing market optimization.
This 2-day hands-on training is designed for technical and management teams across both generation (wind, solar, hydro, cogeneration, gas) and distribution sides of the energy sector. It teaches — through applied scenarios — how to enrich smart meter (AMI), SCADA, OT sensor, EPİAŞ market price and weather data streams with AI; which forecasting models best fit Türkiye's climate and grid characteristics; how to build a compliant architecture under KVKK + EPDK + EU AI Act; and how to take an AI pilot to production in 90 days.
What You Will Learn
- Generation forecasting for wind (RES), solar (GES) and hydro (HES) plants — ARIMA, Prophet, LSTM, Temporal Fusion Transformer
- Predictive maintenance and Remaining Useful Life (RUL) estimation for transformers, turbines, inverters, breakers and switchgear
- Non-technical loss anomaly detection and fault location using AMI and SCADA data
- Price forecasting and bid optimization for the Balancing Market, Day-Ahead (GÖP) and Intra-Day (GİP) markets
- AI-supported architecture for EPDK reporting, CBAM, Türkiye ETS and ESG disclosures
- Compliant energy-AI system design under KVKK and the EU AI Act, with on-prem / hybrid data residency
- Enterprise architecture for joining SCADA + AMI + weather + ERP + EPİAŞ data
- A 90-day pilot roadmap and KPI framework (MAPE, RUL accuracy, loss ratio, balancing penalty)
Sectoral Focus
- Generation: EÜAŞ; renewable IPPs (Enerjisa Generation, Aksa, Akenerji, Zorlu, Bereket, Aydem, Alarko); cogeneration and YEKA tenders
- Transmission & Distribution: TEİAŞ; operators of the 21 distribution regions (BEDAŞ, AYEDAŞ, Enerjisa Distribution, Toroslar, Başkent, GDZ, Vangölü, Çoruh, Fırat, Boğaziçi, etc.)
- Trading & Market: EPİAŞ members, balancing market participants, GÖP/GİP traders, bilateral contract parties
- Industrial & OSB: Cogeneration operators, OSB energy management units, captive-generation industrials (cement, steel, glass, textile)
- Renewables & Hydrogen: Unlicensed producers, rooftop PV, YEKA RES/GES winners, hydrogen-economy investors
Hands-On Labs
Throughout the training, participants work on real (anonymized) datasets and sectoral scenarios: improving the daily MAPE of a GES generation forecast, customer-level anomaly scoring on AMI data, early warning from turbine vibration data (vibration-based RUL), RAG-based document summarization for EPDK monthly reports, AI-supported architecture for CBAM Scope 1-2 emission calculation, and bid-signal generation to minimize balancing market penalties.
Why This Training Matters for Türkiye
EPDK's expected algorithmic supervision framework for 2026, the EU AI Act entering into force for high-risk systems on 2 August 2026, CBAM reporting becoming a trigger for Türkiye's energy-intensive exporters, and the Net Zero 2053 roadmap together make AI investment in energy companies a non-deferrable agenda. This training prepares applicable AI solutions within the real constraints of the Türkiye energy ecosystem — high non-technical losses, rapid post-YEKDEM transition, and the profitability pressure of the balancing market.
Training Methodology
Content framed around Türkiye's energy market (EPDK, EPİAŞ, TEİAŞ)
AI architecture guidance aligned with EPDK and EU AI Act requirements
Hands-on labs on smart meter (AMI) and SCADA data
Generation forecasting models and MAPE improvement for wind, solar and hydro
Predictive maintenance and RUL scenarios for transformers, turbines and inverters
AI signal generation for Balancing Market, GÖP and GİP optimization
Who Is This For?
Why This Course?
A rare AI training program in Türkiye dedicated specifically to the energy sector
Concrete preparation for EPDK's 2026 algorithmic supervision and reporting expectations
End-to-end AI data strategy for Net Zero 2053, CBAM and Türkiye ETS compliance
Use-cases that unlock the real value of smart meter (AMI), SCADA and OT data
Direct ROI scenarios for generation forecasting + balancing market profitability
KVKK- and EU AI Act-compliant architecture, anonymization and data residency guidance
Learning Outcomes
Requirements
Course Curriculum
36 LessonsInstructor
Şükrü Yusuf KAYA
AI Architect | Enterprise AI & LLM Training | Stanford University | Software & Technology Consultant
Şükrü Yusuf KAYA is an internationally experienced AI Consultant and Technology Strategist leading enterprise AI integration in data-critical sectors including energy, banking, e-commerce, retail and logistics. His technical expertise in generative AI and large language models (LLMs) helps organizations build scalable, future-proof architectures.
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