Wind Pattern Recognition and Reference Wind Mast Data Correlations With NWP for Improved Wind-Electric Power Forecasts

Buhan, Serkan; Ozkazanc, Yakup; Çadırcı, I.

doi:10.1109/tii.2016.2543004

Cited by 122 publications

(38 citation statements)

References 31 publications

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“…One of the important and most challenging aspects of a prediction system is to identify a suitable model. As far as wind speed prediction is considered, there are several models exist based on numerical, statistical, and artificial intelligence techniques (Buhan et al, 2016;Bianchi et al, 2017). For station-based prediction, numerical prediction methods are not suitable.…”

Section: Selection Of Modelmentioning

confidence: 99%

Short-term forecasting of wind speed using time division ensemble of hierarchical deep neural networks

Marndi

Patra

Gouda

2020

Bull. of Atmos. Sci.& Technol.

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Wind power in tropical countries like India has great potential as a major source of green energy. However, in order to do proper energy provisioning, there is a need to forecast and estimate the wind speed at windmill locations with an actionable leadtime. Forecasting wind speed at station level is a big challenge using dynamical models as it gives only macro level information. Therefore, use of statistical models is preferably adopted for this purpose. With the recent phenomenal growth of applications in artificial intelligence (AI), it is also possible to use data-driven models based on AI, especially deep learning for short-term forecasting of wind speed. In this paper, we have proposed a novel ensemble forecasting methodology using the long short-term memory (LSTM) model, which is a deep learning approach for time series data analysis. The capability of this approach has been demonstrated using wind speed data obtained from two meteorological stations located at New Delhi in North India and at Bengaluru in South India. We have used the ensemble methodology in two different modes; one is the averaging pooling and other is by using a hierarchical LSTM. The simulations using these models have been validated against the true observations at station scale. The ensemble forecasting method has shown promising results for 3-h early wind speed prediction at both the locations. The results are also compared with two classical statistical methods namely autoregressive and persistence models and two state-of-the-art data-driven models namely support vector machine (SVM) and extreme learning machine (ELM). The capability of the proposed method is demonstrated through various error matrices and found to have better performance. We believe that the proposed method has the potential to improve the short-term wind speed prediction capability at station level.

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Section: Selection Of Modelmentioning

confidence: 99%

Short-term forecasting of wind speed using time division ensemble of hierarchical deep neural networks

Marndi

Patra

Gouda

2020

Bull. of Atmos. Sci.& Technol.

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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%

“…To downscale NWP data to smaller areas, the method proposed in [58] first uses the WRF and ALADIN mesoscale models and then analyzes the correlation between the NWP data and the power generated by each turbine. Next, this method clusters the obtained forecasts and historical generated power data to highlight similar patterns.…”

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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“…Physical models combine the meteorological attributes to highlight the forecasting performance. Numerical weather prediction (NWP) is a well-known physical approach [7]. A novel wind speed model was applied by utilizing the Kalman filter method to minimize the forecasting errors of a NWP model [8].…”

Section: Introductionmentioning

confidence: 99%

Multiperiod‐Ahead Wind Speed Forecasting Using Deep Neural Architecture and Ensemble Learning

Chen

Zhang

2019

Mathematical Problems in Engineering

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Accurate forecasting of wind speed plays a fundamental role in enabling reliable operation and planning for large-scale integration of wind turbines. It is difficult to obtain the accurate wind speed forecasting (WSF) due to the intermittent and random nature of wind energy. In this paper, a multiperiod-ahead WSF model based on the analysis of variance, stacked denoising autoencoder (SDAE), and ensemble learning is proposed. The analysis of variance classifies the training samples into different categories. The stacked denoising autoencoder as a deep learning architecture is later built for unsupervised feature learning in each category. The ensemble of extreme learning machine (ELM) is applied to fine-tune the SDAE for multiperiod-ahead wind speed forecasting. Experimental results are made to demonstrate that the proposed model has the best performance compared with the classic WSF methods including the single SDAE-ELM, ELMAN, and adaptive neuron-fuzzy inference system (ANFIS).

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Wind Pattern Recognition and Reference Wind Mast Data Correlations With NWP for Improved Wind-Electric Power Forecasts

Cited by 122 publications

References 31 publications

Short-term forecasting of wind speed using time division ensemble of hierarchical deep neural networks

Short-term forecasting of wind speed using time division ensemble of hierarchical deep neural networks

A Decision Support System for Wind Power Production

Multiperiod‐Ahead Wind Speed Forecasting Using Deep Neural Architecture and Ensemble Learning

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