Machine Learning Internship: Applied Renewable Generation Forecasting

Description

Job Summary: Internship in data analysis, operational forecasting, and real-time model improvement, applying data science to optimize technical decisions. Key Highlights: 1. Data analysis and improvement of models applied to real-time processes 2. Familiarization with the integration of predictive models into operational processes 3. Enhancing competencies in data analysis, programming, and collaborative work RED ELECTRICA DE ESPAÑA, SAU **Alcobendas** (Madrid) T/2026/50151 **What does the company offer?** --------------------------- * **1** internship position at RED ELECTRICA DE ESPAÑA, SAU for **3 months**, starting in **July 2026** * Monthly gross stipend of **800.00€** * 6 hours per day, full-time schedule * START Program * Internship location in Spain: Paseo del Conde de los Gaitanes, 177, Alcobendas (Madrid) **What profile is the company seeking?** --------------------------------- * **University undergraduate or master's student:** Bachelor's degree in Mathematics, Bachelor's degree in Data Science, Bachelor's degree in Artificial Intelligence **Proposed Training Plan** ------------------------------- About the company and department The intern will join a team focused on data analysis, operational forecasting, and improvement of models applied to real-time processes. The department works with data from operational systems and recent telemetry to optimize forecast quality and support technical decision-making. Training objectives During the internship, students will be able to: * Learn how to process real operational and high-frequency telemetry data. * Understand how minute-level data are transformed into useful variables for hourly models. * Develop capabilities in constructing explanatory variables related to trends, deviations, and recent errors. * Apply real-time error-correction models to existing forecasts. * Learn robust modeling methodologies, such as regression or gradient boosting. * Analyze and validate results by comparing them against baseline forecasts. * Familiarize themselves with the integration of predictive models into operational processes. * Enhance competencies in data analysis, critical thinking, programming, and collaborative work. Learning activities The intern will collaborate—always under supervision—in an applied project aimed at improving operational forecasts, specifically focusing on correcting the first hours of the forecast using recent telemetry of actual or producible generation. Training activities include: * Supporting the development of a real-time error-correction model. * Processing and cleaning high-frequency data. * Constructing explanatory variables from recent system information. * Implementing and comparing simple yet robust predictive models. * Analyzing accuracy metrics and validating against baseline forecasts. * Participating in learning how these models are used in production environments. * Supporting the gradual integration of the model into operational processes. ‍ Mentorship and follow-up The internship includes guidance from a specialized mentor who will support learning, define training objectives, and conduct periodic progress reviews. Continuous feedback will be provided to facilitate the acquisition of technical skills and ensure the experience remains educationally focused.