Interpretable Net Load Forecasting Using Smooth Multiperiodic Features Article Swipe

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Mehmet Öğüt , Bennet Meyers , Stephen Boyd ·

YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.2172/2283726

We consider the problem of forecasting net load over a horizon such as one day,\nusing a trailing window of past net load values as well as date and time. We focus\non three variations on this problem: point forecasts, marginal quantile forecasts,\nand generating conditional samples of the future value. We propose a method\nthat relies on linear regression using some custom engineered time-based features\nto capture multiple periodicities, such as daily, weekly, and seasonal, and their\ninteractions. Our proposed models are readily interpretable, and rely on efficient\nand reliable convex optimization [1] to fit. We illustrate our method on four years\nworth of hourly net load data, comparing predictions made with various subsets of\nthe features.

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Type: article
Language: en
Landing Page: https://doi.org/10.2172/2283726
OA Status: green
Cited By: 2
Related Works: 10
OpenAlex ID: https://openalex.org/W4391521032

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DOI: https://doi.org/10.2172/2283726

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Title: Interpretable Net Load Forecasting Using Smooth Multiperiodic Features

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Language: en

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Publication year: 2024

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Publication date: 2024-01-30

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Authors: Mehmet Öğüt, Bennet Meyers, Stephen Boyd

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Landing page: https://doi.org/10.2172/2283726

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OA URL: https://www.osti.gov/biblio/2283726

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Concepts: Computer science, Net (polyhedron), Artificial intelligence, Real-time computing, Mathematics, Geometry

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Cited by: 2

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