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I Tried to Verify That on a Public Dataset. James Sanders • Data Scientist & ML Engineer • jamesaksanders.com The pitch for battery digital twins right now is consistent across the industry: combine physics models with AI and real telemetry, and you get predictions that are both mathematically valid and physically possible. That’s a near-direct…
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Smart Meter Imputation and the 98% Problem Arcadia’s Udit Garg recently put a sharp number on a problem the energy data industry has danced around for years: generic AI achieves 90–95% accuracy in energy data tasks, but production energy management requires 98–99%. That 4–8 point gap sounds modest. At the scale of millions of interval…
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Aaron Epel, Senior Data Scientist at Stem, discusses the impact of machine learning on fault prediction, asset failure prediction, and preventive maintenance scheduling for energy grid operators. The conversation covers the challenges of data science in handling rare events and imbalanced datasets, as well as the future potential of ML in this field.
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In this installment of the AI Coffee Chats podcast, Claire Longo and I discuss the role of math in Data Science, Artificial Intelligence and Machine Learning. We get stuck in to probability, calculus, linear algebra and statistics. I ask Claire for her take on the next big thing in AI. Math resources for machine learning…
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Welcome to the Coffee Chats about AI Podcast! My guest James Horine, lead applied scientist at Bayer, shares pearls of wisdom on the opportunities and frustrations of navigating the Individual Contributor (IC) and Manager roles in data science. Podcast Notes Connect with James Horine… LinkedIn: https://www.linkedin.com/in/jameshorine/ GitHub: https://github.com/jameshorine James H’s TidyAgronomy R Package for “Tidy”…
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Breast cancer accounts for 30% of new cancer in women in the United States, according to the American Cancer Society. Survival analysis is used to evaluate the effectiveness of different treatments, identify factors that influence survival, and understand the probability of survival over time. I set out to train a number of machine learning models…
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I set out to build a deep learning model to improve day-ahead demand forecasting accuracy for electricity market participants. After testing several algorithms against a baseline forecasting benchmark, the best model beat the benchmark accuracy by 24%.
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James Sanders 