A novel neural network method for wind speed forecasting using exogenous measurements from agriculture stations

Palomares-Salas, José Carlos; Agüera-Pérez, Agustín; Rosa, Juan José González de la; Moreno-Muñoz, Antonio

doi:10.1016/j.measurement.2014.05.020

Cited by 26 publications

(16 citation statements)

References 24 publications

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“…Neural networks are defined by important parameters that cannot be estimated from the data in a direct fashion (Palomares‐Salas et al ., ). These parameters are usually referred to as tuning parameters as there is no analytical formula to determine appropriate values for them (Kuhn & Johnson, ).…”

Section: Methodsmentioning

confidence: 99%

Predicting performance in ASEAN banks: an integrated fuzzy MCDM–neural network approach

Wänke

Azad

Barros³

et al. 2015

Expert Systems

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This paper presents a performance assessment of 88 Association of Southeast Asian Nations banks from 2010 to 2013, using an integrated three‐stage approach on financial criteria that emulates the CAMELS rating system. More precisely, fuzzy analytic hierarchy process is used first to assess the relative weights of a number of criteria related to capital adequacy (C), asset quality (A), management quality (M), earnings (E), liquidity (L), and sensitivity to market risk (S) based on the opinion of 88 Association of Southeast Asian Nations experts. Then, these weights are used as technique for order of preference by similarity to ideal solution inputs to assess their relative efficiency. Lastly, neural networks are combined with technique for order of preference by similarity to ideal solution results to produce a model for banking performance with effective predictive ability. The results reveal that contextual variables have a prominent impact on efficiency. Specifically, parsimony in equity leveraging derived from Islamic finance principles may be the underlying cause in explaining higher efficiency levels.

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Section: Methodsmentioning

confidence: 99%

Predicting performance in ASEAN banks: an integrated fuzzy MCDM–neural network approach

Wänke

Azad

Barros³

et al. 2015

Expert Systems

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“…Besides, the forecasting errors of PM are used to evaluate the existing results. PM is especially suitable for short-term wind prediction [34]. In this paper, PM behaves better than the BP network on short-term wind prediction.…”

Section: Wind Disturbance Models When Rayleigh Numbers Are Equal To 0mentioning

confidence: 86%

“…In [32], the new proposed model IS-PSO-BP obtained good wind prediction performance based on different training numbers and data sources, whose MAE is 0.16 m/s and RMSE with 0.4123 m/s. In [34] its authors adopted All the above statistics and analysis suggest that we have proposed a valid and feasible wind disturbance model based on a BP neural network. This model fully considers the basic features of nonlinear atmospheric systems.…”

Section: Discussionmentioning

confidence: 99%

Lorenz Wind Disturbance Model Based on Grey Generated Components

et al. 2014

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Abstract:In order to meet the needs of wind speed prediction in wind farms, we consider the influence of random atmospheric disturbances on wind variations. Considering a simplified fluid convection mode, a Lorenz system can be employed as an atmospheric disturbance model. Here Lorenz disturbance is defined as the European norm of the solutions of the Lorenz equation. Grey generating and accumulated generating models are employed to explore the relationship between wind speed and its related disturbance series. We conclude that a linear or quadric polynomial generating model are optimal through the verification of short-term wind speed prediction in the Sotavento wind farm. The new proposed model not only greatly improves the precision of short-term wind speed prediction, but also has great significance for the maintenance and stability of wind power system operation.

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“…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). Examples of mesoscale models include the Fifth-Generation Mesoscale Model (MM5) [64], the Weather Research & Forecasting Model (WRF) [65], and the Aire Limitée Adaptation dynamique Développement InterNational (ALADIN) [66].…”

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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A novel neural network method for wind speed forecasting using exogenous measurements from agriculture stations

Cited by 26 publications

References 24 publications

Predicting performance in ASEAN banks: an integrated fuzzy MCDM–neural network approach

Predicting performance in ASEAN banks: an integrated fuzzy MCDM–neural network approach

Lorenz Wind Disturbance Model Based on Grey Generated Components

A Decision Support System for Wind Power Production

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