4.7 Article

Parametric Probabilistic Forecasting of Solar Power With Fat-Tailed Distributions and Deep Neural Networks

Related references

Note: Only part of the references are listed.
Article Energy & Fuels

A deep generative model for probabilistic energy forecasting in power systems: normalizing flows

Jonathan Dumas et al.

Summary: The paper introduces a novel deep learning technique, normalizing flows, to produce accurate scenario-based probabilistic forecasts for power systems applications, addressing the challenges of renewable energy integration.

APPLIED ENERGY (2022)

Article Automation & Control Systems

Automated Deep CNN-LSTM Architecture Design for Solar Irradiance Forecasting

Seyed Mohammad Jafar Jalali et al.

Summary: This article introduces a new deep learning-based multistep ahead approach to improve the forecasting performance of global horizontal irradiance. By using deep neural networks and optimization methods, the performance of the prediction model is enhanced, and its superiority is demonstrated in experiments.

IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS (2022)

Article Energy & Fuels

An interpretable probabilistic model for short-term solar power forecasting using natural gradient boosting

Georgios Mitrentsis et al.

Summary: PV power forecasting models typically rely on machine learning algorithms, and a two-stage probabilistic forecasting framework has been proposed to generate highly accurate and reliable predictions while maintaining full transparency. The framework utilizes NGBoost for probabilistic forecasts and calculates SHAP values to understand predictions, leading to improved accuracy and performance.

APPLIED ENERGY (2022)

Article Green & Sustainable Science & Technology

Spatio-Temporal Graph Neural Networks for Multi-Site PV Power Forecasting

Jelena Simeunovic et al.

Summary: This paper proposes two graph neural network models based on graph signal processing, which achieve higher accuracy in solar power generation forecasting by modeling multi-site photovoltaic (PV) time series. The results show that the proposed models outperform existing methods on different datasets.

IEEE TRANSACTIONS ON SUSTAINABLE ENERGY (2022)

Article Green & Sustainable Science & Technology

Probabilistic Solar Power Forecasting Using Bayesian Model Averaging

Kate Doubleday et al.

Summary: This study introduces a Bayesian model averaging (BMA) post-processing method for forecasting power output from utility-scale photovoltaic (PV) plants. Through a case study, it is demonstrated that this method significantly improves forecast calibration and consistently outperforms raw ensemble forecasts at multiple lead times.

IEEE TRANSACTIONS ON SUSTAINABLE ENERGY (2021)

Article Green & Sustainable Science & Technology

Probabilistic Solar Power Forecasting Based on Bivariate Conditional Solar Irradiation Distributions

Hyeonjin Kim et al.

Summary: The research introduces a two-stage solar power forecasting algorithm that utilizes solar irradiation observations from various locations to make predictions. It first predicts solar irradiation based on numerical weather prediction and then forecasts solar power based on the predicted irradiation. By addressing probabilistic forecasting issues, the model accuracy is enhanced.

IEEE TRANSACTIONS ON SUSTAINABLE ENERGY (2021)

Article Energy & Fuels

Post-processing numerical weather prediction ensembles for probabilistic solar irradiance forecasting

Benedikt Schulz et al.

Summary: Probabilistic energy forecasting is crucial for integrating volatile power sources like solar energy into the electrical grid. Hybrid models combining physical and statistical methods have shown to be effective, with post-processing models proving to significantly improve the forecast performance of ensemble predictions and correct systematic biases.

SOLAR ENERGY (2021)

Article Green & Sustainable Science & Technology

RSAM: Robust Self-Attention Based Multi-Horizon Model for Solar Irradiance Forecasting

Swati Sharda et al.

Summary: This study introduces a novel solar irradiance forecasting model utilizing self-attention mechanism in combination with multiple weather parameters and quantile regression to improve predictive accuracy and model robustness.

IEEE TRANSACTIONS ON SUSTAINABLE ENERGY (2021)

Article Engineering, Electrical & Electronic

Enhanced performance Gaussian process regression for probabilistic short-term solar output forecast

Fatemeh Najibi et al.

Summary: This paper proposes a novel probabilistic framework to predict short-term PV output, taking into account the variability of weather data over different seasons. By using feature selection, clustering, and Gaussian Process Regression, a function relating selected features with solar output is established. Testing on five solar generation plants shows that the proposed method significantly reduces errors compared to existing methodologies.

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS (2021)

Article Green & Sustainable Science & Technology

Convolutional Graph Autoencoder: A Generative Deep Neural Network for Probabilistic Spatio-Temporal Solar Irradiance Forecasting

Mandi Khodayar et al.

IEEE TRANSACTIONS ON SUSTAINABLE ENERGY (2020)

Article Green & Sustainable Science & Technology

Multi-Stage Stochastic Programming to Joint Economic Dispatch for Energy and Reserve With Uncertain Renewable Energy

Runzhao Lu et al.

IEEE TRANSACTIONS ON SUSTAINABLE ENERGY (2020)

Article Economics

DeepAR: Probabilistic forecasting with autoregressive recurrent networks

David Salinas et al.

INTERNATIONAL JOURNAL OF FORECASTING (2020)

Article Green & Sustainable Science & Technology

Unsupervised Clustering-Based Short-Term Solar Forecasting

Cong Feng et al.

IEEE TRANSACTIONS ON SUSTAINABLE ENERGY (2019)

Article Energy & Fuels

Verification of solar irradiance probabilistic forecasts

Philippe Lauret et al.

SOLAR ENERGY (2019)

Article Green & Sustainable Science & Technology

Parametric methods for probabilistic forecasting of solar irradiance

Seyyed A. Fatemi et al.

RENEWABLE ENERGY (2018)

Article Engineering, Environmental

Non-crossing nonlinear regression quantiles by monotone composite quantile regression neural network, with application to rainfall extremes

Alex J. Cannon

STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT (2018)

Article Economics

Comparison of intraday probabilistic forecasting of solar irradiance using only endogenous data

Mathieu David et al.

INTERNATIONAL JOURNAL OF FORECASTING (2018)

Article Economics

Ensemble forecast of photovoltaic power with online CRPS learning

J. Thorey et al.

INTERNATIONAL JOURNAL OF FORECASTING (2018)

Article Meteorology & Atmospheric Sciences

Online learning with the Continuous Ranked Probability Score for ensemble forecasting

J. Thorey et al.

QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY (2017)

Article Engineering, Electrical & Electronic

Probabilistic Forecasting of Photovoltaic Generation: An Efficient Statistical Approach

Can Wan et al.

IEEE TRANSACTIONS ON POWER SYSTEMS (2017)

Article Computer Science, Artificial Intelligence

LSTM: A Search Space Odyssey

Klaus Greff et al.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2017)

Article Green & Sustainable Science & Technology

A Probabilistic Competitive Ensemble Method for Short-Term Photovoltaic Power Forecasting

Antonio Bracale et al.

IEEE TRANSACTIONS ON SUSTAINABLE ENERGY (2017)

Article Green & Sustainable Science & Technology

Short-term probabilistic forecasts for Direct Normal Irradiance

Yinghao Chu et al.

RENEWABLE ENERGY (2017)

Article Engineering, Electrical & Electronic

Very Short-Term Nonparametric Probabilistic Forecasting of Renewable Energy Generation-With Application to Solar Energy

Faranak Golestaneh et al.

IEEE TRANSACTIONS ON POWER SYSTEMS (2016)

Article Economics

Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond

Tao Hong et al.

INTERNATIONAL JOURNAL OF FORECASTING (2016)

Article Engineering, Electrical & Electronic

Adaptive Robust Optimization With Dynamic Uncertainty Sets for Multi-Period Economic Dispatch Under Significant Wind

Alvaro Lorca et al.

IEEE TRANSACTIONS ON POWER SYSTEMS (2015)

Article Energy & Fuels

PV power forecast using a nonparametric PV model

Marcelo Pinho Almeida et al.

SOLAR ENERGY (2015)

Proceedings Paper Automation & Control Systems

Gearbox Fault Diagnosis Based on Gray Relevancy Degree

Zhu-ting Yao et al.

2015 INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND ARTIFICIAL INTELLIGENCE (CAAI 2015) (2015)

Article Green & Sustainable Science & Technology

Geostrophic Wind Dependent Probabilistic Irradiance Forecasts for Coastal California

Patrick Mathiesen et al.

IEEE TRANSACTIONS ON SUSTAINABLE ENERGY (2013)

Article Computer Science, Interdisciplinary Applications

Quantile regression neural networks: Implementation in R and application to precipitation downscaling

Alex J. Cannon

COMPUTERS & GEOSCIENCES (2011)

Article Engineering, Electrical & Electronic

Statistical analysis of wind power forecast error

Hans Bludszuweit et al.

IEEE TRANSACTIONS ON POWER SYSTEMS (2008)

Review Statistics & Probability

Strictly proper scoring rules, prediction, and estimation

Tilmann Gneiting et al.

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION (2007)

Article Meteorology & Atmospheric Sciences

The continuous ranked probability score for circular variables and its application to mesoscale forecast ensemble verification

E. P. Grimit et al.

QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY (2006)

Article Meteorology & Atmospheric Sciences

Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation

T Gneiting et al.

MONTHLY WEATHER REVIEW (2005)