Student Projects

Diploma Theses, Master Theses, Bachelor Theses, Research Projects and Student Reports

Student Projects

The following projects can be adapted for Diploma Theses, Master Theses, Bachelor Theses, Research Projects (Studienarbeiten) and Student Reports (Fachstudien). Contact Prof. Dr. Marco Aiello in case you have further questions or would like to submit your own proposals.

Note: Concepts and implementation produced in any project must be compatible and published under the MIT license.


 

Designing and implementing the capabilities of AI planning as services

Artificial Intelligence (AI) planning provides powerful techniques for searching and selecting actions that, when executed, achieve a given goal [1]. Even though AI planning has made significant progress in terms of advanced algorithms and representations, it hardly takes part in real-world applications. To integrate planning in an application, a developer must get bogged down in theory and detail. This is because most AI planning systems are not aware of any
software design principles.

A common way to transparently accomplish interoperability, reusability, distribution, and reduction of information technology burden is by considering the key elements of Service-Oriented Computing (SOC) [2, 3]: service orientation, services, service composition, and service-oriented architecture. Among these key elements, service orientation provides a set of design principles to be used as guidelines for realising specific but common characteristics of the applications of interest [4].

The aim of this master thesis is to explore the application of service orientation in AI planning, find a classification of planning services depending on their tasks and entities they use, design and implement a set of planning services, and demonstrate the interoperability, flexibility in terms of arbitrary application compositions, efficiency in terms of rapid application prototyping, and the potential of reuse of such designed planning services.

Literature

[1] M. Ghallab, D. S. Nau, and P. Traverso, Automated planning: Theory & practice. Morgan Kaufmann Publishers Inc., 2004.

[2] M. P. Papazoglou and D. Georgakopoulos, “Introduction: Service-oriented computing,” Commun. ACM, vol. 46, no. 10, pp. 24–28, 2003.

[3] T. Erl, SOA principles of service design. Prentice Hall PTR, 2007.

[4] I. Georgievski, “Coordinating services embedded everywhere via hierarchical planning,” Ph.D. dissertation, University of Groningen, October 2015.

Contact: Dr. Ilche Georgievski


 

CO2-efficient demand response: potential benefits in varying energy mix scenarios

Due to high energy consumptions, residential and office buildings are responsible for one third of total CO2 emissions in the European Union (European Commission).  The need for electricity has raised significantly due to the increasing ownership of appliances (Haines, 2010), and, as a consequence, its environmental impact, which varies according to weather conditions, time of the day, production sources, and location. Traditional demand-response programs aim to control the residential electric use in response to price signals or incentive payments (Siano, 2014). Another possible approach is to schedule generation and consumptions according to CO2 signals (Fiorini & Aiello, 2019). Yet, the potential benefits of such programs depend on how the electricity is generated  (Claire Bergaentzlé, 2014; Stoll, 2014).

The research proposed here is to investigate to what extent CO2-efficient demand response programs could enable emission reductions in various countries. The comparison should include countries with different energy generation parks, e.g., France with high penetration of nuclear, Germany with high penetration of coal and wind, The Netherlands with high penetration of natural gas. A quantitative assessment of the potential benefits should also be estimated in varying energy mix scenarios.

Literature

Claire Bergaentzlé, C. C. (2014). Demand-side management and European environmental and energy goals: An optimal complementary approach. Energy Policy, 858-869.

ENTSO-E. (n.d.). Transparency Platform. Retrieved from https://transparency.entsoe.eu/

European Commission. (n.d.). Buildings. Retrieved from https://ec.europa.eu/energy/en/topics/energy-efficiency/buildings

Fiorini, L., & Aiello, M. (2019). Predictive CO2-efficient scheduling for hybrid electric and thermal loads. IEEE International Conference on Energy Internet, (pp. 392-397). Nanjing.

Haines, V. L. (2010). How Trends in Appliances Affect Domestic CO2 Emissions: A Review of Home and Garden Appliances. Department of Energy and Climate Change.

Siano, P. (2014). Demand Responde and Smart Grids--A Survey. Renewable and Sustianable Energy Reviews, 461--478.

Stoll, P. B. (2014). Including dynamic CO2 intensity with demand response. Energy Policy, 490-500.

Contact: Laura Fiorini, M. Sc.Prof. Dr. Marco Aiello


 

Forecasting models for CO2 emissions

Due to high energy consumptions, residential and office buildings are responsible for one third of total CO2 emissions in the European Union (European Commission).  The need for electricity has raised significantly due to the increasing ownership of appliances (Haines, 2010), and, as a consequence, its environmental impact, which varies according to weather conditions, time of the day, production sources, and location. Traditional demand-side management programs aim to control the residential electric use in response to price signals or incentive payments. Another approach is to schedule generation and consumptions according to CO2 signals (Fiorini & Aiello, 2019).

The research proposed here is to build a service oriented architecture to acquire and manipulate data about the generation of electricity in European countries, using the information available on the ENTSOE-E Transparency Platform (ENTSO-E). Based on historical data, the architecture will be able to predict the carbon emissions for the coming hours and to use these information as signals to schedule residential loads accordingly. Different regression models (e.g., OLS, Lasso, Elastic Net) will be investigated and compared.

The architecture will be written in Python and thesis in English.

Prerequisites
  • Service-Oriented Architecture
  • Object-oriented programming in Python
  • Supervised learning
Literature

ENTSO-E. (n.d.). Transparency Platform. Retrieved from https://transparency.entsoe.eu/

European Commission. (n.d.). Buildings. Retrieved from https://ec.europa.eu/energy/en/topics/energy-efficiency/buildings

Fiorini, L., & Aiello, M. (2019). Predictive CO2-efficient scheduling for hybrid electric and thermal loads. IEEE International Conference on Energy Internet, (pp. 392-397). Nanjing.

Haines, V. L. (2010). How Trends in Appliances Affect Domestic CO2 Emissions: A Review of Home and Garden Appliances. Department of Energy and Climate Change.

Stoll, P. B. (2014). Including dynamic CO2 intensity with demand response. Energy Policy, 490-500.

Contact: Laura Fiorini, M. Sc.Prof. Dr. Marco Aiello


 

Energy Management System for a Microgrid

As the transition towards a more sustainable society progresses, the use of renewables in different areas and at different scales increases. Consequently, the energy sector is facing a transition from a centralised network of large power plants to a decentralised grid of renewables. In this context, microgrids represent a key solution to integrate renewables, flexible loads and storage systems in an isolated mode or connected to the rest of the power grid. 

The goal of this thesis is to develop an energy management system whose main objective is to simulate and optimally operate a microgrid. Optimal energy management is important to improve the efficiency and reliability of microgrids [1]. The system should consider renewables, such as photovoltaic panels and wind turbines, a battery energy storage system, and loads coming from households and commercial buildings. The energy management system should provide a near real-time optimal planning and operation of the microgrid so that the operational costs are minimised, the greenhouse emissions are reduced, and smart use of the battery is performed. A demand-side management [2] should be employed to manage controllable loads to match availability of renewables and minimise costs paid for the energy imported from the main power grid by considering dynamic pricing [3]. The performance of the energy management system should be evaluated in different scenarios.

Literature

[1] Abrishambaf, O., Faria, P., Gomes, L., Spínola, J., Vale, Z. and Corchado, J.M., Implementation of a Real-Time Microgrid Simulation Platform Based on Centralized and Distributed Management. Energies 2017, 10, 806.

[2] S., Pierluigi, Demand response and smart grids—A survey, Renewable and Sustainable Energy Reviews, Volume 30, 2014, pp. 461-478.

[3] Pagani, G. A.  and Aiello, M., Generating Realistic Dynamic Prices and Services for the Smart Grid, IEEE Systems Journal, vol. 9, no. 1, pp. 191-198, 2015.

[4] Shirazi, E. and Jadid, S., Optimal residential appliance scheduling under dynamic pricing scheme via HEMDAS, Energy and Buildings, vol 93, pp. 40-49, 2015.

Contact: Dr. Ilche Georgievski


 

The Integration of Electric Vehicles in the Smart Grid

Electric Vehicles (eV) have a battery size that can vary from few KWh to almost 100 KWh. These are frequently connected to the power grid to charge. Several researchers have proposed the idea that these batteries can also be used to store grid electricity and provided it back to grid during peak demand periods. The research proposed here is to assess technical solutions to make the eV an integral part of the power grid, to study individual strategies based on dynamic pricing for incentivising eV integration, and to assess the economic viability of such approaches.

Literature

A. P. Lopes, F. J. Soares and P. M. R. Almeida, "Integration of Electric Vehicles in the Electric Power System," in Proceedings of the IEEE, vol. 99, no. 1, pp. 168-183, Jan. 2011.

Francis Mwasilu, Jackson John Justo, Eun-Kyung Kim, Ton Duc Do, Jin-Woo Jung, “Electric vehicles and smart grid interaction: A review on vehicle to grid and renewable energy sources integration,” in Renewable and Sustainable Energy Reviews, vol. 34, no. 6, pp 501-516, Jun. 2014.

Contact: Prof. Dr. Marco Aiello


 

Service Composition for Personal Assistants

Personal Assistants like Siri, Viv, Alex, Cortana, rely on online services to satisfy the requests of a user. Most often a request cannot be satisfied with just one service, but it needs the composition of multiple services. In this project you will investigate techniques to intelligently compose services  and you will implement prototypes connecting to Web services to satisfy complex user requests. 

Literature

Kaldeli, A. Lazovik, and M. Aiello (2016) Domain-Independent Planning for Services in Uncertain and Dynamic Environments, Artificial Intelligence, Elsevier, 236(7): 30-64.

Contact: Prof. Dr. Marco Aiello


 

Modelling and Simulating Research Funding Distribution in Computer Science

Today researchers heavily rely on research grants and subsidies to finance their research. The attribution of such subsidies is usually based on a peer-evaluation process that awards grants to the most promising proposals. In this project you will perform a statistical analysis of grant acquisition. You will provide a model to predict grant distribution and simulate various scenarios to identify the effects of specific policies on the probabilistic distribution of grants and on their impact on research and innovation. Bibliometric data for complex network analysis will be part of the developed model.

Contact: Prof. Dr. Marco Aiello


 

Cellular Automata for Modelling Resource Allocation

Cellular automata are simple models that can represent complex even chaotic systems. The goal of this project is to provide simple models for resource allocation that help identifying efficient allocation rules. The project entails both a modelling and simulation component to it.

Contact: Prof. Dr. Marco Aiello


 

A Complex Network Analysis of Models to be Checked

Model Checking is the field concerned with the automatic process of checking whether a given model (or family of models) meets certain specifications. Model checkers work in various ways usually exploring large models explicitly represented as states or symbolically represented. Your task is to evaluate the impact of using complex network analysis techniques to improve and extend model checking approaches.

Contact: Prof. Dr. Marco Aiello


 

Contact

Dieses Bild zeigt Aiello
Prof. Dr.

Marco Aiello

Head of Department

Dieses Bild zeigt Ibach
 

Elisabeth Ibach

Secretary

Dieses Bild zeigt Georgievski
Dr.

Ilche Georgievski

Lead, Postdoc and Research Associate

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