Comparison of numerical weather prediction solar irradiance forecasts in the US, Canada and Europe

By Richard Perez, E. Lorenz, S. Pelland, M. Beauharnois, G. Knowe, Karl Hemker, D. Heinemann, J. Remund, S. Müller, Wolfgang Traunmüller and 7 more. 2013

340 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/fa9929e31d64fc04ca4b1f6db009000a41aff70e

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In Artificial Intelligence

In Renewable Energy, Sustainability and the Environment

Citations in the corpus

WRF-Solar: Description and Clear-Sky Assessment of an Augmented NWP Model for Solar Power Prediction (2016, background): “This is known as the aerosol indirect effect (e.g., Rap et al. 2013) and is usually only considered in NWP models that explicitly resolve atmospheric chemistry, if it is considered at all.”
Energy Forecasting: A Review and Outlook (2020, background): “For a review on the NWPmodels that are commonly used in solar forecasting, the reader is referred to [91].”
Machine Learning at the Network Edge: A Survey (2019, neutral): “Since sunlight is the main source of renewable energy, they used a numerical weather prediction (NWP) model [86] to predict weather impacting solar energy generation.”
Photovoltaic and solar power forecasting for smart grid energy management (2015, background): “that predict solar irradiance and PV generation [16], [17].”
History and trends in solar irradiance and PV power forecasting: A preliminary assessment and review using text mining (2018, background): “(2003) and Pritchard et al. (2000), which have a combined citation number of over 40,000.”
Cloud-tracking methodology for intra-hour DNI forecasting (2014, background): “From 6 h up to several days ahead, solar irradiance forecasts rely on Numerical Weather Prediction (NWP) models (Lorenz et al., 2009; Perez et al., 2010; Mathiesen and Kleissl, 2011; Perez et al., 2013).”