A quick guide to wind power forecating : state-of-the-art 2009.

Monteiro, Cláudio; Keko, Hrvoje; Bessa, Ricardo; Miranda, Vladimiro; Botterud, Audun; Wang, J.; Conzelmann, Guenter; Decision,; Porto, Inesc

doi:10.2172/968213

Cited by 18 publications

(9 citation statements)

References 30 publications

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“…In fact, as shown in Refs. [33], this approach allows obtaining better results in WPF applications than a two-step methodology based on wind speed correction and subsequent power curve implementation. Hence, the NN is configured with the u and v wind components as input, along with the forecast lead time, imposing 4 neurons in the hidden layer.…”

Section: Deterministic Prediction Systemmentioning

confidence: 99%

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

et al. 2015

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a b s t r a c tThe efficient integration of wind in the energy market is limited by its natural variability and predictability. This limitation can be tackled by using the probabilistic predictions that provide accurate deterministic forecasts along with a quantification of their uncertainty. We propose as a novelty the application of an analog ensemble (AnEn) method to generate probabilistic wind power forecasts (WPF). The AnEn prediction of a given variable is constituted by a set of measurements of the past, concurrent to the past forecasts most similar to the current one. The AnEn performance for WPF is compared with three state-of-the-science methods for probabilistic predictions over a wind farm and a 505-day long period: a wind power prediction based on the ensemble wind forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System (ECMWF-EPS), the Limitedarea Ensemble Prediction System (LEPS) developed within the COnsortium for Small-scale MOdelling (COSMO-LEPS) and a quantile regression (QR) technique. The AnEn performs as well as ECMWF-EPS, COSMO-LEPS and QR for common events while it exhibits more skill for rare events. A comparison with the performances obtained with a deterministic forecasting method based on a Neural Network is also carried out showing the benefits of using AnEn.

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Section: Deterministic Prediction Systemmentioning

confidence: 99%

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

et al. 2015

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“…Much research on wind power prediction methods has been conducted and some of the mainstream wind power forecasting methods and systems along with their practical applications are summarized in Reference [1]. Broadly speaking, the approaches for wind speed and power forecasting can be divided into three categories: physical forecasting approaches, statistical forecasting approaches, and combination approaches [2].…”

Section: Introductionmentioning

confidence: 99%

A Novel Multi-Objective Optimal Approach for Wind Power Interval Prediction

Yue

et al. 2017

Energies

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Numerous studies on wind power forecasting show that random errors found in the prediction results cause uncertainty in wind power prediction and cannot be solved effectively using conventional point prediction methods. In contrast, interval prediction is gaining increasing attention as an effective approach as it can describe the uncertainty of wind power. A wind power interval forecasting approach is proposed in this article. First, the original wind power series is decomposed into a series of subseries using variational mode decomposition (VMD); second, the prediction model is established through kernel extreme learning machine (KELM). Three indices are taken into account in a novel objective function, and the improved artificial bee colony algorithm (IABC) is used to search for the best wind power intervals. Finally, when compared with other competitive methods, the simulation results show that the proposed approach has much better performance.

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“…A vast number of forecast models have been developed in research. The state-of-the-art in wind power forecasting is summarized in [1] and [4]. In the review of [5], five basic types of forecasting models are identified.…”

Section: Introductionmentioning

confidence: 99%

EEM 2017 Forecast Competition: Wind power generation prediction using autoregressive models

Dimoulkas

Mazidi

Herre

2017

2017 14th International Conference on the European Energy Market (EEM)

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Abstract-Energy forecasting provides essential contribution to integrate renewable energy sources into power systems. Today, renewable energy from wind power is one of the fastest growing means of power generation. As wind power forecast accuracy gains growing significance, the number of models used for forecasting is increasing as well. In this paper, we propose an autoregressive (AR) model that can be used as a benchmark model to validate and rank different forecasting models and their accuracy. The presented paper and research was developed within the scope of the European energy market (EEM) 2017 wind power forecasting competition. I. INTRODUCTIONWind power is one of the fastest growing means of power generation. In a time of paradigm change in energy policy and intensified competition of renewable energy sources, wind power offers several environmental benefits. However, due to its intermittent nature, the integration of wind power poses challenges on power system operation [1], [2], [3]. In order to minimize power imbalance, appropriate means of forecasting power production are essential. Since forecasting is inherently erroneous, the target is to minimize the forecast error. A vast number of forecast models have been developed in research. The state-of-the-art in wind power forecasting is summarized in [1] and [4]. In the review of [5], five basic types of forecasting models are identified.Autoregressive (AR) and autoregressive moving average models (ARMA) have been widely used to predict the wind speed or the wind power generation directly. Reference [6] for example, provides a methodology to generate statistically dependent wind speed scenarios based on ARMA modeling. It is concluded that this methodology is accurate in reproducing wind speed historical series. In [7], an ARMA model is developed to predict wind speeds up to a forecast horizon of 10 hours. A different model is developed for each calendar month. Forecasts are proven to perform significantly better in short terms.A multi-variate ARMA model is presented in [8] for wind power generation forecasting and for simulation of realistic wind speed predictions with adjustable accuracy. In [9], the performance of an autoregressive model and a neural network (NN) model are compared based on the root mean square error. In their analysis, neural networks with a varying number of

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A quick guide to wind power forecating : state-of-the-art 2009.

Cited by 18 publications

References 30 publications

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

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

A Novel Multi-Objective Optimal Approach for Wind Power Interval Prediction

EEM 2017 Forecast Competition: Wind power generation prediction using autoregressive models

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