Goto

Collaborating Authors

 Moret, Stefano


Identifying Dealbreakers and Robust Policies for the Energy Transition Amid Unexpected Events

arXiv.org Artificial Intelligence

Disruptions in energy imports, backlash in social acceptance, and novel technologies failing to develop are unexpected events that are often overlooked in energy planning, despite their ability to jeopardize the energy transition. We propose a method to explore unexpected events and assess their impact on the transition pathway of a large-scale whole-energy system. First, we evaluate unexpected events assuming "perfect foresight", where decision-makers can anticipate such events in advance. This allows us to identify dealbreakers, i.e., conditions that make the transition infeasible. Then, we assess the events under "limited foresight" to evaluate the robustness of early-stage decisions against unforeseen unexpected events and the costs associated with managing them. A case study for Belgium demonstrates that a lack of electrofuel imports in 2050 is the main dealbreaker, while accelerating the deployment of renewables is the most robust policy. Our transferable method can help policymakers identify key dealbreakers and devise robust energy transition policies.


Streamlining Energy Transition Scenarios to Key Policy Decisions

arXiv.org Artificial Intelligence

Uncertainties surrounding the energy transition often lead modelers to present large sets of scenarios that are challenging for policymakers to interpret and act upon. An alternative approach is to define a few qualitative storylines from stakeholder discussions, which can be affected by biases and infeasibilities. Leveraging decision trees, a popular machine-learning technique, we derive interpretable storylines from many quantitative scenarios and show how the key decisions in the energy transition are interlinked. Specifically, our results demonstrate that choosing a high deployment of renewables and sector coupling makes global decarbonization scenarios robust against uncertainties in climate sensitivity and demand. Also, the energy transition to a fossil-free Europe is primarily determined by choices on the roles of bioenergy, storage, and heat electrification. Our transferrable approach translates vast energy model results into a small set of critical decisions, guiding decision-makers in prioritizing the key factors that will shape the energy transition.


Identifying Fake News from Twitter Sharing Data: A Large-Scale Study

arXiv.org Machine Learning

Social networks and the web have acted as large enablers of open communication in the society. These platforms have allowed millions of voices to be heard and have further created a network of information dissemination between the people. While such platforms like Facebook and Twitter havethe potential of starting democratic revolutions, when used incorrectly, they also offer misinformation the means to spread, and the resonance chambers where users can consume and reshare them [AG17]. The term fake news has emerged distinctively over the past few years, as the spread of targeted and artificially crafted news has plagued the fundamental information that people consumeregularly.