24 research outputs found
Calibration of wind speed ensemble forecasts for power generation
In the last decades wind power became the second largest energy source in the
EU covering 16% of its electricity demand. However, due to its volatility,
accurate short range wind power predictions are required for successful
integration of wind energy into the electrical grid. Accurate predictions of
wind power require accurate hub height wind speed forecasts, where the state of
the art method is the probabilistic approach based on ensemble forecasts
obtained from multiple runs of numerical weather prediction models.
Nonetheless, ensemble forecasts are often uncalibrated and might also be
biased, thus require some form of post-processing to improve their predictive
performance. We propose a novel flexible machine learning approach for
calibrating wind speed ensemble forecasts, which results in a truncated normal
predictive distribution. In a case study based on 100m wind speed forecasts
produced by the operational ensemble prediction system of the Hungarian
Meteorological Service, the forecast skill of this method is compared with the
predictive performance of three different ensemble model output statistics
approaches and the raw ensemble forecasts. We show that compared with the raw
ensemble, post-processing always improves the calibration of probabilistic and
accuracy of point forecasts and from the four competing methods the novel
machine learning based approach results in the best overall performance.Comment: 15 pages, 5 figure
Parametric model for post-processing visibility ensemble forecasts
Despite the continuous development of the different operational ensemble
prediction systems over the past decades, ensemble forecasts still might suffer
from lack of calibration and/or display systematic bias, thus require some
post-processing to improve their forecast skill. Here we focus on visibility,
which quantity plays a crucial role e.g. in aviation and road safety or in ship
navigation, and propose a parametric model where the predictive distribution is
a mixture of a gamma and a truncated normal distribution, both right censored
at the maximal reported visibility value. The new model is evaluated in two
case studies based on visibility ensemble forecasts of the European Centre for
Medium-Range Weather Forecasts covering two distinct domains in Central and
Western Europe and two different time periods. The results of the case studies
indicate that climatology is substantially superior to the raw ensemble;
nevertheless, the forecast skill can be further improved by post-processing, at
least for short lead times. Moreover, the proposed mixture model consistently
outperforms the Bayesian model averaging approach used as reference
post-processing technique.Comment: 26 pages, 14 figures, 2 table
A two-step machine learning approach to statistical post-processing of weather forecasts for power generation
By the end of 2021, the renewable energy share of the global electricity
capacity reached 38.3% and the new installations are dominated by wind and
solar energy, showing global increases of 12.7% and 18.5%, respectively.
However, both wind and photovoltaic energy sources are highly volatile making
planning difficult for grid operators, so accurate forecasts of the
corresponding weather variables are essential for reliable electricity
predictions. The most advanced approach in weather prediction is the ensemble
method, which opens the door for probabilistic forecasting; though ensemble
forecast are often underdispersive and subject to systematic bias. Hence, they
require some form of statistical post-processing, where parametric models
provide full predictive distributions of the weather variables at hand. We
propose a general two-step machine learning-based approach to calibrating
ensemble weather forecasts, where in the first step improved point forecasts
are generated, which are then together with various ensemble statistics serve
as input features of the neural network estimating the parameters of the
predictive distribution. In two case studies based of 100m wind speed and
global horizontal irradiance forecasts of the operational ensemble pre diction
system of the Hungarian Meteorological Service, the predictive performance of
this novel method is compared with the forecast skill of the raw ensemble and
the state-of-the-art parametric approaches. Both case studies confirm that at
least up to 48h statistical post-processing substantially improves the
predictive performance of the raw ensemble for all considered forecast
horizons. The investigated variants of the proposed two-step method outperform
in skill their competitors and the suggested new approach is well applicable
for different weather quantities and for a fair range of predictive
distributions.Comment: 25 pages, 12 figures, 4 table
Machine learning for total cloud cover prediction
Accurate and reliable forecasting of total cloud cover (TCC) is vital for many areas such as astronomy, energy demand and production, or agriculture. Most meteorological centres issue ensemble forecasts of TCC; however, these forecasts are often uncalibrated and exhibit worse forecast skill than ensemble forecasts of other weather variables. Hence, some form of post-processing is strongly required to improve predictive performance. As TCC observations are usually reported on a discrete scale taking just nine different values called oktas, statistical calibration of TCC ensemble forecasts can be considered a classification problem with outputs given by the probabilities of the oktas. This is a classical area where machine learning methods are applied. We investigate the performance of post-processing using multilayer perceptron (MLP) neural networks, gradient boosting machines (GBM) and random forest (RF) methods. Based on the European Centre for Medium-Range Weather Forecasts global TCC ensemble forecasts for 2002–2014, we compare these approaches with the proportional odds logistic regression (POLR) and multiclass logistic regression (MLR) models, as well as the raw TCC ensemble forecasts. We further assess whether improvements in forecast skill can be obtained by incorporating ensemble forecasts of precipitation as additional predictor. Compared to the raw ensemble, all calibration methods result in a significant improvement in forecast skill. RF models provide the smallest increase in predictive performance, while MLP, POLR and GBM approaches perform best. The use of precipitation forecast data leads to further improvements in forecast skill, and except for very short lead times the extended MLP model shows the best overall performance
Machine learning for total cloud cover prediction
Accurate and reliable forecasting of total cloud cover (TCC) is vital for
many areas such as astronomy, energy demand and production, or agriculture.
Most meteorological centres issue ensemble forecasts of TCC, however, these
forecasts are often uncalibrated and exhibit worse forecast skill than ensemble
forecasts of other weather variables. Hence, some form of post-processing is
strongly required to improve predictive performance. As TCC observations are
usually reported on a discrete scale taking just nine different values called
oktas, statistical calibration of TCC ensemble forecasts can be considered a
classification problem with outputs given by the probabilities of the oktas.
This is a classical area where machine learning methods are applied. We
investigate the performance of post-processing using multilayer perceptron
(MLP) neural networks, gradient boosting machines (GBM) and random forest (RF)
methods. Based on the European Centre for Medium-Range Weather Forecasts global
TCC ensemble forecasts for 2002-2014 we compare these approaches with the
proportional odds logistic regression (POLR) and multiclass logistic regression
(MLR) models, as well as the raw TCC ensemble forecasts. We further assess
whether improvements in forecast skill can be obtained by incorporating
ensemble forecasts of precipitation as additional predictor. Compared to the
raw ensemble, all calibration methods result in a significant improvement in
forecast skill. RF models provide the smallest increase in predictive
performance, while MLP, POLR and GBM approaches perform best. The use of
precipitation forecast data leads to further improvements in forecast skill and
except for very short lead times the extended MLP model shows the best overall
performance.Comment: 24 pages, 7 figure
Statistical post-processing of heat index ensemble forecasts: is there a royal road?
We investigate the effect of statistical post-processing on the probabilistic
skill of discomfort index (DI) and indoor wet-bulb globe temperature (WBGTid)
ensemble forecasts, both calculated from the corresponding forecasts of
temperature and dew point temperature. Two different methodological approaches
to calibration are compared. In the first case, we start with joint
post-processing of the temperature and dew point forecasts and then create
calibrated samples of DI and WBGTid using samples from the obtained bivariate
predictive distributions. This approach is compared with direct post-processing
of the heat index ensemble forecasts. For this purpose, a novel ensemble model
output statistics model based on a generalized extreme value distribution is
proposed. The predictive performance of both methods is tested on the
operational temperature and dew point ensemble forecasts of the European Centre
for Medium-Range Weather Forecasts and the corresponding forecasts of DI and
WBGTid. For short lead times (up to day 6), both approaches significantly
improve the forecast skill. Among the competing post-processing methods, direct
calibration of heat indices exhibits the best predictive performance, very
closely followed by the more general approach based on joint calibration of
temperature and dew point temperature. Additionally, a machine learning
approach is tested and shows comparable performance for the case when one is
interested only in forecasting heat index warning level categories.Comment: 29 pages, 12 figure
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