Direct Quantile Regression for Nonparametric Probabilistic Forecasting of Wind Power Generation

Wan, Can; Lin, Jin; Wang, Jianhui; Song, Yonghua; Zhang, Dong

doi:10.1109/tpwrs.2016.2625101

Cited by 238 publications

(106 citation statements)

References 44 publications

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“…The installed capacity of wind farms worldwide has increased 30 times to a total of 435 GW, with 17% cumulative growth in the last few years. In 2020, wind energy is expected to supply approximately 12% of the total worldwide requirement [1,2].…”

Section: Introductionmentioning

confidence: 99%

A New Hybrid Approach to Forecast Wind Power for Large Scale Wind Turbine Data Using Deep Learning with TensorFlow Framework and Principal Component Analysis

2019

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Wind power forecasting plays a vital role in renewable energy production. Accurately forecasting wind energy is a significant challenge due to the uncertain and complex behavior of wind signals. For this purpose, accurate prediction methods are required. This paper presents a new hybrid approach of principal component analysis (PCA) and deep learning to uncover the hidden patterns from wind data and to forecast accurate wind power. PCA is applied to wind data to extract the hidden features from wind data and to identify meaningful information. It is also used to remove high correlation among the values. Further, an optimized deep learning algorithm with a TensorFlow framework is used to accurately forecast wind power from significant features. Finally, the deep learning algorithm is fine-tuned with learning error rate, optimizer function, dropout layer, activation and loss function. The algorithm uses a neural network and intelligent algorithm to predict the wind signals. The proposed idea is applied to three different datasets (hourly, monthly, yearly) gathered from the National Renewable Energy Laboratory (NREL) transforming energy database. The forecasting results show that the proposed research can accurately predict wind power using a span ranging from hours to years. A comparison is made with popular state of the art algorithms and it is demonstrated that the proposed research yields better predictions results.

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Section: Introductionmentioning

confidence: 99%

A New Hybrid Approach to Forecast Wind Power for Large Scale Wind Turbine Data Using Deep Learning with TensorFlow Framework and Principal Component Analysis

2019

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“…The knowledge of NWP forecast uncertainty is crucial in decision making and optimization processes involved in many applications. Wind power production [2][3][4][5][6][7][8], dynamic thermal rating of transmission lines [9][10][11][12][13], and extreme weather event prediction [14] are few applications where information about forecast uncertainty is often regarded as significant as the forecast values themselves [15,16]. In transmission line thermal rating applications, ambient temperature and wind data are used along with line current to determine the temperature of power conductors that limits the thermal capacity of the line.…”

Section: Introductionmentioning

confidence: 99%

“…In quantile regression based forecast uncertainty models, no distribution is assumed for the forecast error and each individual quantile is modeled independently [4,27,31]. Target quantiles are modeled as a function of influential feature sets through an optimization process.…”

Section: Introductionmentioning

confidence: 99%

Quantile Regression and Clustering Models of Prediction Intervals for Weather Forecasts: A Comparative Study

2019

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Information about forecast uncertainty is vital for optimal decision making in many domains that use weather forecasts. However, it is not available in the immediate output of deterministic numerical weather prediction systems. In this paper, we investigate several learning methods to train and evaluate prediction interval models of weather forecasts. The uncertainty models of weather predictions are trained from a database of historical forecasts/observations. They are developed to investigate prediction intervals of weather forecasts using various quantile regression methods as well as cluster-based probabilistic forecasts using fuzzy methods. To compare and verify probabilistic forecasts, a novel score is developed that accounts for sampling variation effects on forecast verification statistics. The impact of various feature sets and model parameters in forecast uncertainty modeling is also investigated. The results show superior performance of the non-linear quantile regression models in comparison with clustering methods.

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“…PIs is a range (difference between upper and lower bounds) with corresponding coverage probability for a random variable in future [16]. When compared with deterministic forecasting, PIs cannot only provide point forecasting value, but also can provide reliabilities information of the estimation value [17]. Therefore, the PIs are more valuable and informative for decision makers to make well preparation for the worst and the best possible condition ahead [18].…”

Section: Introductionmentioning

confidence: 99%

An Optimized Prediction Intervals Approach for Short Term PV Power Forecasting

Zhuang

Sheng

et al. 2017

Energies

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High quality photovoltaic (PV) power prediction intervals (PIs) are essential to power system operation and planning. To improve the reliability and sharpness of PIs, in this paper, a new method is proposed, which involves the model uncertainties and noise uncertainties, and PIs are constructed with a two-step formulation. In the first step, the variance of model uncertainties is obtained by using extreme learning machine to make deterministic forecasts of PV power. In the second stage, innovative PI-based cost function is developed to optimize the parameters of ELM and noise uncertainties are quantization in terms of variance. The performance of the proposed approach is examined by using the PV power and meteorological data measured from 1kW rooftop DC micro-grid system. The validity of the proposed method is verified by comparing the experimental analysis with other benchmarking methods, and the results exhibit a superior performance.

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Direct Quantile Regression for Nonparametric Probabilistic Forecasting of Wind Power Generation

Cited by 238 publications

References 44 publications

A New Hybrid Approach to Forecast Wind Power for Large Scale Wind Turbine Data Using Deep Learning with TensorFlow Framework and Principal Component Analysis

A New Hybrid Approach to Forecast Wind Power for Large Scale Wind Turbine Data Using Deep Learning with TensorFlow Framework and Principal Component Analysis

Quantile Regression and Clustering Models of Prediction Intervals for Weather Forecasts: A Comparative Study

An Optimized Prediction Intervals Approach for Short Term PV Power Forecasting

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