Founder at Seita Energy Flexibility BV
This is the story of how we made sure that tracking local school holidays in Python becomes possible for data scientists, using workalendar (code example below). But let’s start at the beginning: When we write models that interpret and forecast meter data, we often encounter demand patterns that change drastically during holiday periods. When this…
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Seita developed an energy balancing platform for Jeju island: a multi-user web app handling meter data from diverse assets and automated scheduling.
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To help utilities enable new services that save drinking water, Seita is developing a pattern detection and classification toolbox to provide real-time automated analysis for monitoring platforms.
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Probabilistic forecasts give you insights into possible futures. In this post, I suggest that the best plot type to show them all at a glance is a ridgeline plot. Areas of doubt and uncertainty In data-driven operations, decisions are based on numeric expectations about what the future holds. Usually, these expectations come from point forecasts…
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Seita creates open-source tools and visuals to streamline the innovation of predictive analytics and to bring state-of-the-art machine learning models to work (e.g. for renewable energy production forecasts).
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Seita (together with PNGK) developed an online knowledge platform to visualize how the most pressing legal issues are distributed over a region’s population.
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