A novel application of an analog ensemble for short-term wind power forecasting

Alessandrini, Stefano; Monache, Luca Delle; Sperati, Simone; Nissen, Jesper Nielsen

doi:10.1016/j.renene.2014.11.061

Cited by 124 publications

(80 citation statements)

References 39 publications

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“…Probabilistic forecasting based on statistical post-processing techniques applied on a single deterministic run (i.e., quantile regression) requires similar computational time as that of a deterministic forecast. Some evidences of what mentioned are reported in [30].…”

Section: Discussionmentioning

confidence: 73%

“…Therefore, higher resolutions in both boundary conditions and the limited-area model were used. A post-processing system based on an ANN [29,30] was applied to both RAMS and HIRLAM output. As a consequence, the post-processing model is similar to the one used by the winner in the exercise, even if the GLM part was not performed.…”

Section: Klim Case-comparison With Previous Benchmark and High Resolumentioning

confidence: 99%

See 1 more Smart Citation

The “Weather Intelligence for Renewable Energies” Benchmarking Exercise on Short-Term Forecasting of Wind and Solar Power Generation

et al. 2015

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A benchmarking exercise was organized within the framework of the European Action Weather Intelligence for Renewable Energies ("WIRE") with the purpose of evaluating the performance of state of the art models for short-term renewable energy forecasting. The exercise consisted in forecasting the power output of two wind farms and two photovoltaic power plants, in order to compare the merits of forecasts based on different modeling approaches and input data. It was thus possible to obtain a better knowledge of the state of the art in both wind and solar power forecasting, with an overview and comparison of the principal and the novel approaches that are used today in the field, and to assess the evolution of forecast performance with respect to previous benchmarking exercises. The outcome of this exercise consisted then in proposing new challenges in the renewable power forecasting field and identifying the main areas for improving accuracy in the future.

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Section: Discussionmentioning

confidence: 73%

Section: Klim Case-comparison With Previous Benchmark and High Resolumentioning

confidence: 99%

The “Weather Intelligence for Renewable Energies” Benchmarking Exercise on Short-Term Forecasting of Wind and Solar Power Generation

et al. 2015

View full text Add to dashboard Cite

show abstract

“…is known as the Heaviside function [56,57]. Thus, the F o i (x) shows a Boolean value of whether the observed value x a i is smaller than x.…”

Section: Continuous Ranked Probability Skill (Crps)mentioning

confidence: 99%

“…Here, the CRPS of the original and corrected ensemble forecasts are given in Table 6. The CRPS score (CRPSS) is further used here to compare impacts of two correction methods, which is defined as follows [57]:…”

Section: Continuous Ranked Probability Skill (Crps)mentioning

confidence: 99%

A Long-Term Wind Speed Ensemble Forecasting System with Weather Adapted Correction

Chu

Wang

et al. 2016

Energies

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Abstract:Wind forecasting is critical in the wind power industry, yet forecasting errors often exist. In order to effectively correct the forecasting error, this study develops a weather adapted bias correction scheme on the basis of an average bias-correction method, which considers the deviation of estimated biases associated with the difference in weather type within each unit of the statistical sample. This method is tested by an ensemble forecasting system based on the Weather Research and Forecasting (WRF) model. This system provides high resolution wind speed deterministic forecasts using 40 members generated by initial perturbations and multi-physical schemes. The forecasting system outputs 28-52 h predictions with a temporal resolution of 15 min, and is evaluated against collocated anemometer towers observations at six wind fields located on the east coast of China. Results show that the information contained in weather types produces an improvement in the forecast bias correction.

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“…makes it very difficult to develop a single robust and reliable model for wind forecasting. Therefore, it has been many attempts to develop hybrid or ensemble methods for wind speed forecasting [7][8][9][10][11]. The main objective of this study was to develop a robust forecast model for the wind speed so to be able to use it in future ensemble methods.…”

Section: Introductionmentioning

confidence: 99%

Short-term wind speed forecasting by an adaptive network-based fuzzy inference system (ANFIS): an attempt towards an ensemble forecasting method

Yousefi

Hooshyar

Remezani

et al. 2015

Int. J. Adv. Intell. Informatics

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Accurate Wind speed forecasting has a vital role in efficient utilization of wind farms. Wind forecasting could be performed for long or short time horizons. Given the volatile nature of wind and its dependent on many geographical parameters, it is difficult for traditional methods to provide a reliable forecast of wind speed time series. In this study, an attempt is made to establish an efficient adaptive network-based fuzzy interference (ANFIS) for short-term wind speed forecasting. Using the available data sets in the literature, the ANFIS network is constructed, tested and the results are compared with that of a regular neural network, which has been forecasted the same set of dataset in previous studies. To avoid trial-and-error process for selection of the ANFIS input data, the results of autocorrelation factor (ACF) and partial auto correlation factor (PACF) on the historical wind speed data are employed. The available data set is divided into two parts. 50% for training and 50% for testing and validation. The testing part of data set will be merely used for assessing the performance of the neural network which guarantees that only unseen data is used to evaluate the forecasting performance of the network. On the other hand, validation data could be used for parameter-setting of the network if required. The results indicate that ANFIS could not outperform ANN in short-term wind speed forecasting though its results are competitive. The two methods are hybridized, though simply by weightage, and the hybrid methods shows slight improvement comparing to both ANN and ANFIS results. Therefore, the goal of future studies could be implementing ANFIS and ANNs in a more comprehensive ensemble method which could be ultimately more robust and accurate

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A novel application of an analog ensemble for short-term wind power forecasting

Cited by 124 publications

References 39 publications

The “Weather Intelligence for Renewable Energies” Benchmarking Exercise on Short-Term Forecasting of Wind and Solar Power Generation

The “Weather Intelligence for Renewable Energies” Benchmarking Exercise on Short-Term Forecasting of Wind and Solar Power Generation

A Long-Term Wind Speed Ensemble Forecasting System with Weather Adapted Correction

Short-term wind speed forecasting by an adaptive network-based fuzzy inference system (ANFIS): an attempt towards an ensemble forecasting method

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