Thesis subject
IoT for Sustainable Energy Management in Smart Greenhouses (MSc)
Energy consumption is a significant factor in the lifecycle of greenhouses. To make greenhouses more sustainable, greenhouses must use as less energy as possible.
Short description
Energy consumption is a significant factor in the environmental lifecycle and operation costs of greenhouses. To make the operation of greenhouses more sustainable, greenhouses must use as less energy as possible for operations, such as heating, lighting and ventilation. To cope with this challenge, Artificial Intelligence (AI) can help. By leveraging sensing data from Internet of Things (IoT), the aim is to develop AI-powered systems for managing smart greenhouses and reducing their energy consumption and carbon footprint.
Objectives
This project is connected to the "the Greenhouse of the Future" project. In comparison, this project has a stronger focus on designing a digital twin for monitoring and management solutions for a greenhouse environment and its energy usage. Specifically, the following key points are of interest:
- Energy model in smart greenhouses: This component aims to build energy models of multiple types of sustainable energy resources and electric assets, such as solar photovoltaics and heat pumps. Meanwhile, these models will be incorporated in the digital twin design of smart greenhouse in the project.
- Explore dependencies of sustainable energy consumption based on IoT sensing data: This research does NOT focus on exploring the optimal growing environment for crops or on how to control smart greenhouses to achieve such an environment. Instead, our objective is to investigate the relationships between sustainable energy consumption and IoT sensing data. For example, we need to examine (1) the correlation between solar electricity harvesting/consumption and indoor temperature; (2) the connection between heat consumption or CO2 injection and the opening of all greenhouse windows.
- Design IoT schematic structure for collecting energy consumption data: We will explore what kind of sensors are needed for collecting data, and which parameters must be measured in a greenhouse. This will help to define the requirements for building a real IoT system to be incorporated in the digital twin design.
Tasks
The work in this master thesis entails:
- Investigate energy consumption models of the most energy-intensive operations in greenhouses by literature study.
- Identify critical sensing data and actuation features of smart greenhouses that can enable sustainable energy consumption.
- Utilize AI solutions, such as DNN models, to establish correlations between energy consumption and IoT sensing data, including crop growth and environmental factors.
- Define the IoT system requirements, analyze differences compared to existing IoT solutions and incorporate that in the digital twin design for the greenhouse of the future.
Literature
- Maraveas, Chrysanthos. Incorporating Artificial Intelligence Technology in Smart Greenhouses: Current State of the Art. Applied Sciences. 2022.
- Maraveas, Chrysanthos and Loukatos, Dimitrios and Bartzanas, Thomas and Arvanitis, Konstantinos G and Uijterwaal, Johannes Franciscus. Smart and solar greenhouse covers: recent developments and future perspectives. Frontiers in Energy Research. 2021.
Requirements
- Courses: Programming in Python (INF-22306), (Optional), Data Science Concepts (INF-34306), Big Data (INF-33806) or Machine Learning (FTE-35306)
- Required skills/knowledge: Basic data analytics/machine learning and willingness to learn new software tools, interest about greenhouses and sustainable energy transition
Key words: Internet of Things, Artificial Intelligence, Sustainable Energy, Smart Greenhouse
Contact person(s)
- Qingzhi Liu (qingzhi.liu@wur.nl)
- Tarek Alskaif (tarek.alskaif@wur.nl)
- Önder Babur (onder.babur@wur.nl)