History and trends in solar irradiance and PV power forecasting: A preliminary assessment and review using text mining
Text mining is an emerging topic that advances the review of academic literature. This paper
presents a preliminary study on how to review solar irradiance and photovoltaic (PV) power …
presents a preliminary study on how to review solar irradiance and photovoltaic (PV) power …
[HTML][HTML] Pairing ensemble numerical weather prediction with ensemble physical model chain for probabilistic photovoltaic power forecasting
Under the two-step framework of photovoltaic (PV) power forecasting, that is, forecasting first
the irradiance and then converting it to PV power, there are two chief ways in which one can …
the irradiance and then converting it to PV power, there are two chief ways in which one can …
[HTML][HTML] Diffusion probabilistic modeling for video generation
Denoising diffusion probabilistic models are a promising new class of generative models
that mark a milestone in high-quality image generation. This paper showcases their ability to …
that mark a milestone in high-quality image generation. This paper showcases their ability to …
[HTML][HTML] Deep learning for twelve hour precipitation forecasts
Existing weather forecasting models are based on physics and use supercomputers to
evolve the atmosphere into the future. Better physics-based forecasts require improved …
evolve the atmosphere into the future. Better physics-based forecasts require improved …
Accurate uncertainties for deep learning using calibrated regression
V Kuleshov, N Fenner, S Ermon - … conference on machine …, 2018 - proceedings.mlr.press
Methods for reasoning under uncertainty are a key building block of accurate and reliable
machine learning systems. Bayesian methods provide a general framework to quantify …
machine learning systems. Bayesian methods provide a general framework to quantify …
[HTML][HTML] SEAS5: the new ECMWF seasonal forecast system
In this paper we describe SEAS5, ECMWF's fifth generation seasonal forecast system, which
became operational in November 2017. Compared to its predecessor, System 4, SEAS5 is a …
became operational in November 2017. Compared to its predecessor, System 4, SEAS5 is a …
A generative deep learning approach to stochastic downscaling of precipitation forecasts
L Harris, ATT McRae, M Chantry… - Journal of Advances …, 2022 - Wiley Online Library
Despite continuous improvements, precipitation forecasts are still not as accurate and
reliable as those of other meteorological variables. A major contributing factor to this is that …
reliable as those of other meteorological variables. A major contributing factor to this is that …
Sub‐seasonal forecasting with a large ensemble of deep‐learning weather prediction models
We present an ensemble prediction system using a Deep Learning Weather Prediction
(DLWP) model that recursively predicts six key atmospheric variables with six‐hour time …
(DLWP) model that recursively predicts six key atmospheric variables with six‐hour time …
[HTML][HTML] Reduced global warming from CMIP6 projections when weighting models by performance and independence
The sixth Coupled Model Intercomparison Project (CMIP6) constitutes the latest update on
expected future climate change based on a new generation of climate models. To extract …
expected future climate change based on a new generation of climate models. To extract …
Recent advances in electricity price forecasting: A review of probabilistic forecasting
J Nowotarski, R Weron - Renewable and Sustainable Energy Reviews, 2018 - Elsevier
Since the inception of competitive power markets two decades ago, electricity price
forecasting (EPF) has gradually become a fundamental process for energy companies' …
forecasting (EPF) has gradually become a fundamental process for energy companies' …