BACHELOR’S THESIS - MAPPING CLOUD SCHEDULING AND OPTIMIZATION ALGORITHMS TO CUSTOMER ENERGY MAN[...]

FORTISS GMBH; München; Vor Ort; EUR 40.000 - 60.000; Vollzeit

Zusammenfassung

FORTISS GMBH sucht einen neuen Teammitglied für eine Bachelorarbeit, die sich mit der Anpassung von Cloud-Algorithmen im Bereich des Energiemanagements befasst. Die Arbeit beinhaltet eine detaillierte Literaturrecherche, die Implementierung eines Algorithmus und die Untersuchung von KI-Techniken. Seien Sie Teil eines innovativen Umfelds und tragen Sie zur digitalen Transformation der Energiebranche bei.

Leistungen

Internationale und dynamische Arbeitsumgebung

Mitarbeit an spannenden Lösungen im Bereich Energiemanagement

Qualifikationen

• Interesse an Cloud-Computing und Energiemanagement.
• Grundkenntnisse über Cloud- oder Energiesysteme sind vorteilhaft.
• Gute Kommunikationsfähigkeiten in Englisch sind erforderlich.

Aufgaben

• Durchführung einer umfassenden Literaturrecherche zu Algorithmen.
• Mapping eines repräsentativen Scheduling-Algorithmus.
• Implementierung des angepassten Algorithmus im CommonPower-Simulationsrahmen.

Kenntnisse

Programmierung

Optimierungsalgorithmen

Cloud-Computing

Energieinformatik

Künstliche Intelligenz

Maschinelles Lernen

Ausbildung

Studium im Bereich Informatik oder verwandtes Fach

Jobbeschreibung

Wer sind wir?

fortiss is the state research institute of the Free State of Bavaria for the development of software-intensive systems, based in Munich. The scientists at the institute work in research, development and transfer projects with universities, research institutions and technology leaders in Bavaria, Germany, and Europe. They research and develop methods, techniques, and tools for reliable, secure and comprehensible software solutions and artificial intelligence applications. fortiss is organised in the legal form of a non-profit limited liability company. The shareholders are the Free State of Bavaria (majority shareholder) and the Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V. (Fraunhofer Society for the Promotion of Applied Research).In this project, we search for a new team member:Bachelor’s Thesis - Mapping Cloud Scheduling and Optimization Algorithms to Customer Energy Management (CEM) Systems (m/f/d)As the energy sector undergoes a digital transformation, efficient scheduling and resource management are becoming increasingly crucial for integrating renewable energy sources and balancing supply and demand. Concepts from cloud computing—especially hierarchical scheduling and optimization—have proven effective in distributed computing environments and present a promising opportunity for improving energy system operations.This thesis focuses investigating scheduling and optimization algorithms applied in cloud-computing (e.g. hypervisor) and map them to Customer Energy Management (CEM) systems in the energy domain. It explores how scheduling techniques from cloud environments can be adapted to local energy management systems. Aside from a deterministic algorithm, the work will look into the role of artificial intelligence (AI) and machine learning (ML) for optimizing scheduling tasks.A key outcome of the thesis will be a proof of concept, where selected scheduling algorithms from cloud computing are adapted and implemented using the CommonPower simulation framework to demonstrate its applicability in a CEM scenario und a qualitative evaluation.Goals of the Thesis:

• Conduct an in-depth literature review on scheduling and optimization algorithms in cloud computing (and CEM systems).
• Categorize different scheduling approaches based on optimization objectives (e.g., cost, response time, flexibility or deterministic, metaheuristic, machine learning).
• Investigate the application of AI and ML techniques in scheduling for both cloud (and energy systems).
• Map at least two cloud scheduling schemes to CEM systems, identifying synergies and limitations.
• Develop a conceptual framework for adapting cloud-based scheduling to CEM.
• Implement and evaluate a selected scheduling approach in the CommonPower simulation framework.

Your tasks:

1. Comprehensive Literature Review

• Identify and compare relevant scheduling and optimization algorithms from both cloud computing and energy management domains.
• Categorize techniques by objectives such as latency, energy efficiency, cost, and adaptability.
• Analyze the contribution of AI and ML techniques in enhancing scheduling performance—especially in cloud computing contexts.

2. Algorithm Mapping & Framework Development

• Select a representative scheduling algorithm from the cloud domain.
• Map it to CEM requirements and constraints (e.g., household-level energy consumption, DER coordination).
• Define hierarchical scheduling structures where applicable (e.g., local vs. community-level coordination).

3. Implementation & Evaluation

• Implement the adapted algorithm using the CommonPower simulation framework.
• Simulate different scenarios to evaluate effectiveness, scalability, and potential benefits for CEM systems.

Your profile:

This thesis is well-suited for students with interests in cloud computing, energy informatics, optimization algorithms, and smart grid technologies. Prior experience with programming and basic knowledge of cloud or energy systems is beneficial. Also, Good communication skills in English are essential.

Our offer:

• Get involved with novel and exciting solutions in the context of Energy Management System and Software Defined Solutions, and in alignment with industrial partners.
• An international and dynamic work environment with highly qualified and motivated colleagues.

Did we catch your interest?

Please submit your application with a motivational statement, a detailed CV and a current transcript of records.Job-ID: ASCI-SH-01-2025Contact: Mahsa Faraji Shoyari / Dr. Markus Duchon