Renewable Energy + Time Series Forecasting2025 · Deployed Installation

# Windenergy

Wind power forecasting system that predicts turbine power output with uncertainty intervals. Combines SCADA observations with weather covariates to answer: what power should operators expect, and how uncertain is that forecast?

Wind PowerTime SeriesForecastingConformal PredictionStreamlitFastAPIPython

Windenergy forecasting system architecture showing SCADA data fusion with weather covariates and conformal prediction intervals.

Problem

Wind farm operators need reliable power forecasts for grid integration, maintenance scheduling, and trading. Naive predictions lack uncertainty quantification, making it hard to plan for variability and assess operational risk.

Approach

Fuse SCADA data with weather context using PatchTST transformers and gradient boosting models. Apply conformal prediction for calibrated P10/P50/P90 intervals. Deploy via FastAPI (port 8765) and Streamlit dashboard (port 8766) with Docker containerization.

Impact

* Deployed on VPS with Docker Compose, accessible at skumyol.com/wind/.
* Clean Architecture separates domain, application, infrastructure, and interface layers.
* Chronological train/val/test splits prevent temporal data leakage.
* API provides /forecast, /risk/ramps, /risk/assess endpoints with structured JSON responses.

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