Satyaki Chatterjee

Students looking for a thesis or project are requested to apply via e-mail with their CV, grades, and relevant projects/git. For an industrial thesis, enclose a detailed research proposal.

Since 10/2021:
Ph.D. Student at the Pattern Recognition Lab, Diehl Metering GmbH
03/2025 – 3/2028:
M.Sc. in Advanced Signal Processing and Communication Engineering
Friedrich-Alexander University Erlangen-Nürnberg

AquaPositive: Data-Driven Strategy Recommendation of Water End-usage

Term: March 1, 2025 – March 31, 2028

Description: Water scarcity is a pressing global issue, exacerbated by factors such as climate change, population growth, and unsustainable water consumption patterns. To mitigate this crisis, it is imperative to introduce effective and sustainable water management strategies that extend to the very end of the consumption chain: the end consumers themselves. The AquaPositive project seeks to develop an intelligent and informed system that empowers end consumers to play a pivotal role in alleviating water scarcity by modifying their consumption behaviors. This approach is anchored in the understanding that informed behavioral changes can significantly contribute to conserving water resources. Educating end consumers about the significance of water conservation and its direct impact on mitigating water scarcity is fundamental. Informed consumers are more likely to adopt responsible water usage practices. However, Analyzing historical consumption patterns and behavioral deviations among different consumer groups provides valuable insights. Understanding when and how end-consumers tend to use excessive water allows for targeted intervention strategies. Tailoring recommendations to individual consumers based on their consumption behavior profiles empowers them to make conscious choices. These recommendations can include insights on optimal usage times and areas where water can be saved without compromising their needs. Therefore, there are two outcomes that the research project would try to address:
1. When and where, how much water is needed to maintain sustainable operations of end consumer groups
2. When and where, how much water can be saved by employing different strategies
The information as an outcome from these two questions would yield an informed strategy as ‘Action Recommendation’ for water end-consumers. The Aqua Positive project aims to develop an AI-driven recommendation system to guide end consumers in efficient water consumption, considering historical consumption, consumption patterns, population growth, climate change, and weather fluctuations.

Data Processing for Utility Infrastructure (DPUI)
Time Series Intelligence (TSI)