My research interests are transversal to several topics in the multi-disciplinary field of data science, including, but not limited to, instrumentation and data acquisition, public data, machine learning, and information visualization. My research is driven by curiosity, persistence, and a strong desire to leverage the potential of data to address different societal challenges like environmental sustainability and smart cities. A brief outline of my current research topics is provided below.
Performance and Value Proposition of Disaggregated Energy Consumption Data
I am very interested in understanding the real-world applicability of non-intrusive energy monitoring and eco-feedback technologies and my goals in this research field are twofold: i), assess how such technologies perform from a technological point of view (e.g., algorithms performance) when deployed outside of the laboratory settings, and ii) determine the value proposition of such technologies as tools for smartening the electric energy grid hence reducing its carbon footprint.
Integration of Renewable Energy Sources (RES) and Electric Vehicles (EV)
Under this topic, my research goals are three-fold: i) understand how individual appliance consumption data, daily routines, and Battery Energy Storage Systems (BESS) can be leveraged to maximise the self-consumption from RES in households and small businesses, ii) understand how driving and daily routines, individual appliance consumption data, and BESS can be leveraged to develop frameworks for charging EVs in individual households and residential buildings, and iii) understand how BESS and EVs can be combined to mitigate voltage and frequency control issues that may occur due to the intermittent nature of RES.
