Wind speed forecast correction models using polynomial neural networks

Zjavka, Ladislav

doi:10.1016/j.renene.2015.04.054

Cited by 92 publications

(33 citation statements)

References 28 publications

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“…This correction module can provide alternative forecasts to NWP systems, which might be especially useful in predictions of local weather events (e.g. storms), pollution concentrations or renewable energy sources (Zjavka, ). An analogous on‐line forecast system monitors local wind speed NWP errors to calculate intra‐day forecast corrections several hours ahead using the statistical data relationships (Hirataa et al, ).…”

Section: Discussionmentioning

confidence: 99%

Local improvements in numerical forecasts of relative humidity using polynomial solutions of general differential equations

Zjavka

Sokol

2018

Quart J Royal Meteoro Soc

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Large‐scale forecast models are based on the numerical integration of differential equation systems, which can describe atmospheric processes in light of global meteorological observations. Mesoscale forecast systems need to define the initial and lateral boundary conditions, which may be carried out with robust global numerical models. Their overall solutions are able to describe the dynamic weather system on the Earth scale using a large number of complete globe 3D matrix variables in several atmospheric layers. Post‐processing methods using local measurements were developed in order to clarify surface weather details and adapt numerical weather prediction model outputs for local conditions. Differential polynomial network is a new type of neural network that can model local weather using spatial data observations to process forecasts of the input variables and revise the target 24 h prognosis. It defines and solves general partial differential equations, being able to describe unknown dynamic systems. The proposed forecast correction method uses a differential network to estimate the optimal numbers of training days and form derivative prediction models. It can improve final numerical forecasts, processed with additional data analysis and statistical techniques, in the majority of cases. The two‐stage procedure presented is analogous to the perfect‐prognosis method using real observations to derive a model, which is applied to the forecasts of the predictors to calculate output predictions.

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Section: Discussionmentioning

confidence: 99%

Local improvements in numerical forecasts of relative humidity using polynomial solutions of general differential equations

Zjavka

Sokol

2018

Quart J Royal Meteoro Soc

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“…To compensate for the NWP data inaccuracies, some methods in the literature have estimated the quality of the weather forecasts [56], [57], evaluated the forecasting error [21], [38], [58], [59], performed preliminary feature selection [49], [60], or enhanced the NWP data by considering mesoscale models as the source of weather forecasts [28], [61], [62]. Other approaches have integrated the NWP data with local observations [63], terrain data, and orography information to downscale the NWP forecasts to a smaller areas (e.g., an area of 1 km × 1 km).…”

Section: Related Workmentioning

confidence: 99%

“…Similarly, the method described in [21] improves the NWP by performing a WRF simulation and cluster analysis to search for correspondences between forecasting errors and NWP values, while [38] uses an error-correcting model for NWP that analyzes the differences between the weather forecasts and actual wind speed measurements. The method proposed in [59] adopts the ALADIN mesoscale models and uses polynomial neural networks to improve the NWP for a specific site. However, these methods require real-time input data collected by sensors placed in the wind farm.…”

Section: Related Workmentioning

confidence: 99%

A Decision Support System for Wind Power Production

Labati

Genovese

Piuri

et al. 2020

IEEE Trans. Syst. Man Cybern, Syst.

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Abstract-Renewable energy production is constantly growing worldwide, and some countries produce a relevant percentage of their daily electricity consumption through wind energy. Therefore, decision support systems that can make accurate predictions of wind-based power production are of paramount importance for traders operating in the energy market and for managers in charge of planning the non-renewable energy production. In this paper, we present a decision support system that can predict electric power production, estimate a variability index for the prediction, and analyze wind farm production characteristics. The main contribution of this paper is a novel system for long-term electric power prediction based solely on weather forecasts; thus, it is suitable for wind farms that cannot collect or manage real-time data acquired by sensors. Our system is based on neural networks and on novel techniques for calibrating and thresholding the weather forecasts based on the distinctive characteristics of wind farm orography. We tuned and evaluated the proposed system using data collected from two wind farms over a two-year period and achieved satisfactory results. We studied different feature sets, training strategies, and system configurations before implementing this system for a player in the energy market. This company evaluated the power production prediction performance and impact of our system at ten different wind farms under real-world conditions and achieved a significant improvement with respect to their previous approach.

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“…The commonly used methods in terms of wind speed prediction are mainly divided into two categories: statistical analysis models, such as autoregressive moving average (ARMA) models [5] and autoregressive integrated moving average (ARIMA) models [6][7][8]; and machine learning methods, including artificial neural networks (ANNs) [9][10][11][12] and the support vector machine (SVM) [13,14]. prediction accuracy than the single ENN model.…”

Section: Introductionmentioning

confidence: 99%

Wind Speed Forecasting Based on EMD and GRNN Optimized by FOA

2017

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As a kind of clean and renewable energy, wind power is winning more and more attention across the world. Regarding wind power utilization, safety is a core concern and such concern has led to many studies on predicting wind speed. To obtain a more accurate prediction of the wind speed, this paper adopts a new hybrid forecasting model, combing empirical mode decomposition (EMD) and the general regression neural network (GRNN) optimized by the fruit fly optimization algorithm (FOA). In this new model, the original wind speed series are first decomposed into a collection of intrinsic mode functions (IMFs) and a residue. Next, the inherent relationship (partial correlation) of the datasets is analyzed, and the results are then used to select the input for the forecasting model. Finally, the GRNN with the FOA to optimize the smoothing factor is used to predict each sub-series. The mean absolute percentage error of the forecasting results in two cases are respectively 8.95% and 9.87%, suggesting that the hybrid approach outperforms the compared models, which provides guidance for future wind speed forecasting.

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Wind speed forecast correction models using polynomial neural networks

Cited by 92 publications

References 28 publications

Local improvements in numerical forecasts of relative humidity using polynomial solutions of general differential equations

Local improvements in numerical forecasts of relative humidity using polynomial solutions of general differential equations

A Decision Support System for Wind Power Production

Wind Speed Forecasting Based on EMD and GRNN Optimized by FOA

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