4.7 Article

Renewable energy forecasting: A self-supervised learning-based transformer variant

Related references

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

Conformal asymmetric multi-quantile generative transformer for day-ahead wind power interval prediction

Wei Wang et al.

Summary: With the rapid increase in wind power installed capacity, day-ahead wind power interval prediction has become increasingly important. This paper proposes a prediction method based on conformal asymmetric multi-quantile generative transformer to provide higher quality intervals. The experiments show that this method outperforms benchmarks by providing narrower prediction intervals with more accurate empirical coverage probability. The average width is reduced by 19.6% compared to symmetric prediction intervals given by common benchmark quantile long short term memory network.

APPLIED ENERGY (2023)

Article Energy & Fuels

A novel approach based on integration of convolutional neural networks and deep feature selection for short-term solar radiation forecasting

Hakan Acikgoz

Summary: The study proposes a novel deep solar forecasting approach that combines multiple techniques and models to achieve high prediction accuracy. The experimental results show that the proposed method demonstrates accurate and robust forecasting performance, outperforming traditional regression models.

APPLIED ENERGY (2022)

Article Energy & Fuels

A compositive architecture coupling outlier correction, EWT, nonlinear Volterra multi-model fusion with multi-objective optimization for short-term wind speed forecasting

Ping Fang et al.

Summary: Short-term wind speed forecasting plays a positive role in power system dispatch and wind energy utilization. This study proposes an innovative approach that incorporates data preprocessing, multiple individual predictors, and Volterra multi-model fusion to improve accuracy and stability. Experimental results validate the effectiveness of the proposed method.

APPLIED ENERGY (2022)

Article Energy & Fuels

Dynamic spatio-temporal correlation and hierarchical directed graph structure based ultra-short-term wind farm cluster power forecasting method

Fei Wang et al.

Summary: This paper proposes an ultra-short-term wind farm cluster power forecasting method based on dynamic spatio-temporal correlation and hierarchical directed graph structure. It defines different types of nodes, calculates spatio-temporal correlation matrices, and constructs a graph structure for the forecasting model. The proposed method shows excellent performance in a case study.

APPLIED ENERGY (2022)

Article Multidisciplinary Sciences

Deep learning for twelve hour precipitation forecasts

Lasse Espeholt et al.

Summary: The neural network MetNet-2 improves weather forecasting by accurately predicting precipitation up to 12 hours in advance, outperforming traditional physics-based models. This neural network-based approach overcomes limitations of traditional models by learning transformations from data.

NATURE COMMUNICATIONS (2022)

Article Energy & Fuels

Dynamic spatio-temporal correlation and hierarchical directed graph structure based ultra-short-term wind farm cluster power forecasting method

Fei Wang et al.

Summary: This paper proposes an ultra-short-term wind farm cluster power forecasting method based on dynamic spatio-temporal correlation and hierarchical directed graph structure, which effectively addresses the problems in traditional methods by defining different nodes and calculating correlation matrices. In a case study, the proposed method demonstrates excellent performance in improving the accuracy of power forecasting.

APPLIED ENERGY (2022)

Article Energy & Fuels

Ensemble wind speed forecasting with multi-objective Archimedes optimization algorithm and sub-model selection

Lifang Zhang et al.

Summary: This research proposes a novel ensemble forecasting system that integrates decomposition strategy, sub-model selection, and ensemble point and interval prediction based on the multi-objective Archimedes optimization algorithm. The system has been shown to provide reliable wind speed forecasting results, which can be crucial for power system dispatching and management.

APPLIED ENERGY (2021)

Article Thermodynamics

A blended approach incorporating TVFEMD, PSR, NNCT-based multi-model fusion and hierarchy-based merged optimization algorithm for multi-step wind speed prediction

Dongzhen Xiong et al.

Summary: By utilizing TVFEMD and PSR to process wind speed time series data, establishing a multi-model fusion strategy and optimizing weight coefficients using the GHWOADE algorithm, the proposed approach improves the accuracy of wind speed prediction.

ENERGY CONVERSION AND MANAGEMENT (2021)

Article Green & Sustainable Science & Technology

A hybrid approach for multi-step wind speed forecasting based on two-layer decomposition, improved hybrid DE-HHO optimization and KELM

Wenlong Fu et al.

Summary: The study proposes a novel hybrid forecasting approach combining two-layer decomposition, improved optimization algorithm, and machine learning model, which effectively predicts short-term wind speed by establishing models and optimizing internal parameters on training and validation sets.

RENEWABLE ENERGY (2021)

Editorial Material Energy & Fuels

Better seasonal forecasts for the renewable energy industry

Anton Orlov et al.

NATURE ENERGY (2020)

Review Thermodynamics

Taxonomy research of artificial intelligence for deterministic solar power forecasting

Huaizhi Wang et al.

ENERGY CONVERSION AND MANAGEMENT (2020)

Article Green & Sustainable Science & Technology

Hourly forecasting of solar irradiance based on CEEMDAN and multi-strategy CNN-LSTM neural networks

Bixuan Gao et al.

RENEWABLE ENERGY (2020)

Review Thermodynamics

Solar photovoltaic generation forecasting methods: A review

Sobrina Sobri et al.

ENERGY CONVERSION AND MANAGEMENT (2018)

Review Multidisciplinary Sciences

The quiet revolution of numerical weather prediction

Peter Bauer et al.

NATURE (2015)

Review Multidisciplinary Sciences

Deep learning

Yann LeCun et al.

NATURE (2015)