Probabilistic forecasting of high-resolution clear-sky index time-series using a Markov-chain mixture distribution model

@article{Munkhammar2019ProbabilisticFO,
  title={Probabilistic forecasting of high-resolution clear-sky index time-series using a Markov-chain mixture distribution model},
  author={Joakim Munkhammar and Dennis van der Meer and Joakim Wid{\'e}n},
  journal={Solar Energy},
  year={2019}
}
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23 Citations
Background Citations
2
Methods Citations
2

23 Citations

Probabilistic forecasting of the clear-sky index using Markov-chain mixture distribution and copula models
TLDR
Two probabilistic forecasting models for the clear-sky index, based on the Markov-chain mixture distribution (MCM) and copula clear-Sky index generators, are presented and evaluated and show that the copula model generally outperforms the PeEn, while the MCM and QR models are superior in all tested aspects.
A New Approach for Satellite-Based Probabilistic Solar Forecasting with Cloud Motion Vectors
TLDR
Results show that the proposed model shows to largely surpass the baseline probabilistic forecast Complete History Persistence Ensemble (CH-PeEn), reducing the Continuous Ranked Probability Score (CRPS) between 37% and 62%, depending on the forecast horizon.
Very short term load forecasting of residential electricity consumption using the Markov-chain mixture distribution (MCM) model
Probabilistic Solar Power Forecasting Using Bayesian Model Averaging
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A Bayesian model averaging post-processing method suitable for forecasting power from utility-scale photovoltaic (PV) plants at multiple time horizons up to at least the day-ahead timescale and consistently outperforming an ensemble model output statistics (EMOS) parametric approach from the literature.
Probabilistic solar forecasting benchmarks on a standardized dataset at Folsom, California
Assessment and Day-Ahead Forecasting of Hourly Solar Radiation in Medellín, Colombia
The description and forecasting of hourly solar resource is fundamental for the operation of solar energy systems in the electric grid. In this work, we provide insights regarding the hourly…
Short-Term Solar Irradiance Forecasting Using Calibrated Probabilistic Models
TLDR
This work develops a variety of state-of-the-art probabilistic models for forecasting solar irradiance, and demonstrates that the best model, NGBoost, achieves higher performance at an intra-hourly resolution than the best benchmark solar irradiances forecasting model across all stations.
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The analysis of solar irradiance has important applications in predicting solar energy production from solar power plants. Although the sun provides every day more energy than we need, the…
Benchmark probabilistic solar forecasts: Characteristics and recommendations
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Solar energy has found increasing applications in recent years, and the demand will continue to grow as society redirects to a more renewable development path. However, the required high-frequency…
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References

SHOWING 1-10 OF 19 REFERENCES
A spatiotemporal Markov-chain mixture distribution model of the clear-sky index
An N-state Markov-chain mixture distribution model of the clear-sky index
An autocorrelation-based copula model for generating realistic clear-sky index time-series
Probabilistic Solar Forecasting Using Quantile Regression Models
In this work, we assess the performance of three probabilistic models for intra-day solar forecasting. More precisely, a linear quantile regression method is used to build three models for generating…
Assessing probabilistic forecasts of multivariate quantities, with an application to ensemble predictions of surface winds
We discuss methods for the evaluation of probabilistic predictions of vector-valued quantities, that can take the form of a discrete forecast ensemble or a density forecast. In particular, we propose…
A benchmarking of machine learning techniques for solar radiation forecasting in an insular context
Stochastic simulation of daily precipitation, temperature, and solar radiation
Long samples of weather data are frequently needed to evaluate the long-term effects of proposed hydrologic changes. The evaluations are often undertaken using deterministic mathematical models that…
A novel hybrid (Mycielski-Markov) model for hourly solar radiation forecasting
An analog ensemble for short-term probabilistic solar power forecast
Machine learning methods for solar radiation forecasting: A review
...
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