Energía y Ambiente (ITBA-KIT)

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Automation and optimization of agricultural soil tillage applying machine learning based on machine- and process sensor systems
(2021) Kazenwadel, Benjamin; Geimer, Marcus; Stein, Alexander; Becker, Simon
Climate change and cost pressure lead to new environmental and economic challenges that increase the demand for innovative control systems to automate and optimize agricultural tasks. Automating speed control during power-intensive soil tillage can increase eciency and sustainability and counteract the lack of qualied personnel in agriculture. A survey was carried out focused on tillage by cultivating to obtain an overview of the challenges farmers face during their work, including their target preferences. Based on the obtained requirements for tillage by cultivating, a system was developed automating working depth control by online Lidar plane detection to ensure tillage quality and establish a basis for good plant growth. Automated speed control is realized based on an online-parameterized draft force and traction model combined with the usage of a neural network for fuel rate prediction. The network is trained oine and adaptable to the individual preferences of the farms and varying implements. Thereby, the operator can choose and customize optimization objectives such as fuel eciency, performance, or total cost. During the evaluation, the control system was tested with various objectives against a human driver and was able to perform optimization on the individual objective. Furthermore, the transferability of the system was demonstrated with the usage of another implement.
Machine learning for spatial disaggregation of regional transport data in the EU
(2023-06-23) Fernandez, Juan R.; Ates, Cihan; Patil, Shruthi
The European Union (EU) is actively working to combat climate change and promote sustainable development by reducing greenhouse gas (GHG) emissions. The transport sector, a major contributor of GHG emissions, was at the forefront of these initiatives. After experiencing steady growth from 2013 until 2019, there was an abrupt decrease in 2020 due to the COVID-19 pandemic. However, preliminary estimates indicated a rebound of 7.7% for transport emissions in 2021, according to the [Agency (2021)]. Nonetheless, further research is necessary in order to devise effective strategies for regional decarbonization within this challenging sector. An analysis of the transport sector in Europe reveals significant disparities in emission trends across different regions. According to [Eurostat (2021)], Western European countries have generally experienced greater decreases in transport emissions compared to Central and Eastern European nations, which have made slower progress. Furthermore, the European Environment Agency [Agency (2021)] points out that urban areas tend to have higher emissions due to higher population densities and greater demand for transportation. These discrepancies underscore the necessity for spatial disaggregation when developing tailored decarbonization strategies for different regions. To address the intricacies of regional decarbonization potentials, this research aims to apply machine learning techniques to enhance the accuracy of estimating transport-related metrics at a regional level. This, in turn, will facilitate the identification of decarbonization opportunities within the transport sector. More precisely, the study seeks to establish a framework that utilizes machine learning methodologies for spatial disaggregation, a critical process for understanding the factors that influence emissions on a regional scale and devising efficient mitigation strategies.
Machine learning-based analysis of residential electricity consumption behavior for consumers and prosumers
(2021) Werner, Tamo; Jiao, Jiao
With the shift towards a more sustainable energy system, the need for a better understanding of the behavior development over time of consumers and prosumers arises. Despite the growing penetration of smart meter infrastructure, the availability of energy usage behavior data is still limited, due to privacy and security concerns. Thus, connecting and comparing existing datasets is the key to observe the user behavior shifts as well as enhancing the utility of the available data. In the present work, a novel work!ow for combined analysis on multiple smart meter datasets is proposed, which links datasets with diferent scopes, temporal origins and speci#cations. In general, there are 4 steps: data preprocessing, clustering, location dependency check and dataset linking. First, the meteorological seasons combined with weekdays and weekends are picked for data segmentation in the data preprocessing, followed by missing value validation and normalization based on the maximum and minimum consumption value of each household. Thereafter, K-means clustering algorithm is applied to group the user behaviors, which stands out by 0.8186 Silhouette coe$cient (SIL) and 0.2884 Davies-Bouldin Index (DBI) among Fuzzy C-Means and hierarchical clustering approach. Subsequently, two validation approaches on the location dependency, cluster center correlation (0.8048) and location share among clusters (4.99 % variability), prove the minor impact of the household location on the electricity consumption behavior within Germany. Based on the location dependency check, ultimately, the combined analysis of the two datasets reveals the temporal development of the residential consumption behaviors. It shows that new technologies, especially Photovoltaics (PV), Electric Vehicles (EV) and heat pumps, have in!uence on the user behavior shift and the energy consumption level.

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