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Automatic hourly solar forecasting using machine learning models
پیش بینی خودکار خورشیدی ساعتی با استفاده از مدل های یادگیری ماشین-2019 Owing to its recent advance, machine learning has spawned a large collection of solar forecasting works. In particular,
machine learning is currently one of the most popular approaches for hourly solar forecasting. Nevertheless,
there is evidently a myth on forecast accuracy—virtually all research papers claim superiority over others.
Apparently, the “best” model can only be selected with hindsight, i.e., after empirical evaluation. For any new
forecasting project, it is irrational for solar forecasters to bet on a single model from the start. In this article, the
hourly forecasting performance of 68 machine learning algorithms is evaluated for 3 sky conditions, 7 locations, and
5 climate zones in the continental United States. To ensure a fair comparison, no hybrid model is considered, and
only off-the-shelf implementations of these algorithms are used. Moreover, all models are trained using the automatic
tuning algorithm available in the R caret package. It is found that tree-based methods consistently perform
well in terms of two-year overall results, however, they rarely stand out during daily evaluation. Although no
universal model can be found, some preferred ones for each sky and climate condition are advised. Keywords: Automatic machine learning | Solar forecasting | R caret package |
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