Student Computer Science, Mathematics, Physics or similar (f/m/x) - Benchmarking Data-Driven Models

Location

Spanien | Germany

Job description

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Course paper / final thesis

Benchmarking Data-Driven Models

Benchmarking data-driven models for solar nowcasting

Starting date

1 April 2024

Duration of contract

6 months

Remuneration

450 € + ERASMUS funding

Type of employment

Full-time

Background: The Institute of Solar Research is dedicated to advancing the application of solar energy on Earth. Access to reliable solar resource information is crucial for our work. Within the solar energy meteorology group, we gather meteorological data and identify parameters essential for the design, operation, and qualification of solar power plants. Predicting future solar irradiance is a key aspect, with cloud dynamics significantly influencing shortwave radiation and, consequently, impacting solar power plant efficiency. The development of advanced solar forecasting models is essential. These models anticipate perturbations in solar irradiance induced by cloud dynamics. They contribute to the seamless integration of solar energy and thereby address the challenges of the energy transition for climate change mitigation.

In recent years, the field has seen significant growth, particularly with the advent of deep learning in computer vision, leading to the publication of numerous data-driven models. However, comparing these models is challenging as they are often trained and tested on proprietary data not publicly shared. To evaluate the true forecast performance, a benchmark study on a common and high-quality database is necessary.

Objective of the Master's Thesis: 

This master's thesis aims to conduct a benchmark of data-driven solar forecasting models based on sky images from so called all-sky imagers and time series data. Utilizing high-quality data from the Plataforma Solar de Almería, the models will be trained on a standardized training set and validated on a versatile test set. In addition to exploring model architectures, the research will focus on data preprocessing and training methodologies of published results. The main tasks include:

• conduct a comprehensive review of data-driven and machine learning-based methods for solar forecasting with all-sky imagers.
• select and implement the most promising approaches from the literature, reproducing published results if data is available.
• prepare training, validation, and test datasets for benchmarking the selected models.
• validate the selected models, including our own model currently under development at DLR.
• analyze benchmark results using standard forecasting metrics (e.g., RMSE) and investigate ramp event detection capabilities.
• visualize results using explainable AI techniques to enhance understanding of model behavior.
• compile findings and insights into a well-structured master's thesis.

Organizational Notes:

• Location: Almería, Spain
• Minimum Period: 6 months (extendable)

• ongoing university studies in computer science, mathematics, physics, or engineering (Diplom/Master, Uni/FH)
• background in machine learning
• strong programming skills in Python (experience in Pytorch desired)
• Familiarity with software versioning tools (git/gitlab) advantageous
• proficient in oral and written English
• German and/or Spanish language skills are beneficial (not required)

Look forward to a fulfilling job with an employer who appreciates your commitment and supports your personal and professional development. Our unique infrastructure offers you a working environment in which you have unparalleled scope to develop your creative ideas and accomplish your professional objectives. Our human resources policy places great value on a healthy family and work-life-balance as well as equal opportunities for persons of all genders (f/m/x). Individuals with disabilities will be given preferential consideration in the event their qualifications are equivalent to those of other candidates.

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