ABSTRACT
The electric grid is uniquely susceptible to extreme events through both power supply and consumption pathways. Extreme events - like heatwaves and droughts - are expected to increase in frequency and severity due to climate change and are already causing consequences on power system operations and stability. Additionally, non-climate related events like the COVID-19 pandemic have had dramatic impacts on energy consumption patterns globally. We apply modern machine learning methods to model electricity consumption in Brazil, one of the largest generators of hydropower, to better understand the consumption-side effects of extreme national and regional events. After training on 20 years of historical data, we verify an R2of 0.848 and a MAPE of 2.6% for our counterfactual model and use it to assess impacts of historical events on electricity consumption. We then discuss how this approach can be applied toward measuring energy system responsiveness and resiliency on present and future scenarios. © 2022 IEEE.
ABSTRACT
Covid-19 is forcing medical doctors to deal with the urgent need to find clinical answers in an area where, unfortunately, there are still therapies with no evidence of efficacy and safety. In any case, they must make decisions and face the dilemma of immediately using any therapeutic options that has some plausibility of treatment or instead making clinical trial. However, the latter require formal steps and procedures. How do medical doctors understand which of these are essential and necessary? Is it really important to register the new clinical trials on the official platforms? Is this an unnecessary bureaucratic passage that can be avoided during the emergency phase? The present article tries to answer these questions by also addressing other critical issues related to clinical trials at a time when the good rules of clinical research must not be an obstacle to accessing the best available treatments.