Accurate short-term wind speed prediction by exploiting diversity in input data using banks of artificial neural networks

Salcedo‐Sanz, Sancho; Pérez-Bellido, Ángel M.; Ortiz-García, Emilio G.; Portilla-Figueras, Antonio; Prieto, Luís; Correoso, Juan Francisco

doi:10.1016/j.neucom.2008.09.010

Cited by 96 publications

(48 citation statements)

References 17 publications

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“…Therefore, some hybrid models have been proposed to remedy some of the weaknesses [36][37][38][39]. A hybridization of the fifth generation mesoscale model with neural networks was employed to address the short-term wind speed forecasting issue [40].…”

Section: Review and Discussion For Previous Workmentioning

confidence: 99%

A Hybrid Wind Speed Forecasting System Based on a ‘Decomposition and Ensemble’ Strategy and Fuzzy Time Series

Yang

Jiang

2017

Energies

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Accurate and stable wind speed forecasting is of critical importance in the wind power industry and has measurable influence on power-system management and the stability of market economics. However, most traditional wind speed forecasting models require a large amount of historical data and face restrictions due to assumptions, such as normality postulates. Additionally, any data volatility leads to increased forecasting instability. Therefore, in this paper, a hybrid forecasting system, which combines the 'decomposition and ensemble' strategy and fuzzy time series forecasting algorithm, is proposed that comprises two modules-data pre-processing and forecasting. Moreover, the statistical model, artificial neural network, and Support Vector Regression model are employed to compare with the proposed hybrid system, which is proven to be very effective in forecasting wind speed data affected by noise and instability. The results of these comparisons demonstrate that the hybrid forecasting system can improve the forecasting accuracy and stability significantly, and supervised discretization methods outperform the unsupervised methods for fuzzy time series in most cases.

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Section: Review and Discussion For Previous Workmentioning

confidence: 99%

A Hybrid Wind Speed Forecasting System Based on a ‘Decomposition and Ensemble’ Strategy and Fuzzy Time Series

Yang

Jiang

2017

Energies

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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%

“…The methods described in [28], [61], [62] use neural networks and the MM5 model to predict the wind power generated by each turbine of the power plant two days ahead. However, these methods require real-time measurements and require information on the local orography and terrain.…”

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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“…Wind energy has been part of the fastest growing renewable energy sources that is clean and pollution-free, which has been successfully adopted in many countries, and wind energy represents about 10% of energy consumption in Europe, over 15% in America and Germany [1]. In China, the installed wind power capacity was 75324 MW in 2012, with year on year growth rate of 24.1%, and in 2013, the installed wind power capacity was 91413 MW, with year on year growth rate of 21.4%, ranking first in the world [2].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Forecasting Model Based Data Mining and Cuckoo Search: A Case Study of Wind Speed Time Series

Xu¹,

Song²,

Qian³

et al. 2016

TOEFJ

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Abstract:Wind energy has been part of the fastest growing renewable energy sources that is clean and pollution-free, which has been increasingly gaining global attention, and wind speed forecasting plays a vital role in the wind energy field, however, it has been proven to be a challenging task owing to the effect of various meteorological factors. This paper proposes a hybrid forecasting model, which can effectively make a preprocess for the original data and improve forecasting accuracy, the developed model applies cuckoo search(CS) algorithm to optimize the parameters of the wavelet neural network (WNN) model. The proposed hybrid method is subsequently examined on the wind farms of eastern China and the forecasting performance shows that the developed model is better than some traditional models.

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Accurate short-term wind speed prediction by exploiting diversity in input data using banks of artificial neural networks

Cited by 96 publications

References 17 publications

A Hybrid Wind Speed Forecasting System Based on a ‘Decomposition and Ensemble’ Strategy and Fuzzy Time Series

A Hybrid Wind Speed Forecasting System Based on a ‘Decomposition and Ensemble’ Strategy and Fuzzy Time Series

A Decision Support System for Wind Power Production

Hybrid Forecasting Model Based Data Mining and Cuckoo Search: A Case Study of Wind Speed Time Series

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