Wind power forecasting : state-of-the-art 2009.

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

doi:10.2172/968212

Cited by 300 publications

(279 citation statements)

References 190 publications

(255 reference statements)

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“…We compare SHREA against a Persistence baseline algorithm. Despite its simplicity, the predictions of this model are the same as the last observation, this model is known to be hard to beat in short-time ahead predictions [10].…”

Section: Experimental Configurationmentioning

confidence: 99%

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Predicting Ramp Events with a Stream-Based HMM Framework

Ferreira

Gama

Costa

et al. 2012

Lecture Notes in Computer Science

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Abstract. The motivation for this work is the study and prediction of wind ramp events occurring in a large-scale wind farm located in the US Midwest. In this paper we introduce the SHREA framework, a stream-based model that continuously learns a discrete HMM model from wind power and wind speed measurements. We use a supervised learning algorithm to learn HMM parameters from discretized data, where ramp events are HMM states and discretized wind speed data are HMM observations. The discretization of the historical data is obtained by running the SAX algorithm over the first order variations in the original signal. SHREA updates the HMM using the most recent historical data and includes a forgetting mechanism to model natural time dependence in wind patterns. To forecast ramp events we use recent wind speed forecasts and the Viterbi algorithm, that incrementally finds the most probable ramp event to occur.We compare SHREA framework against Persistence baseline in predicting ramp events occurring in very short-time horizons.

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Section: Experimental Configurationmentioning

confidence: 99%

“…We present a comparison against the Persistence model that is known to be hard to beat in short-time forecasts [10].…”

Section: Introductionmentioning

confidence: 99%

Predicting Ramp Events with a Stream-Based HMM Framework

Ferreira

Gama

Costa

et al. 2012

Lecture Notes in Computer Science

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“…Because NWP run at high resolution over a sizable domain requires on the order of hours to run on supercomputers and often requires spin-up time, it is not available for real-time use in the shortest ranges. Modern methods of forecasting renewable energy output employing postprocessing methods to blend disparate models or ensem-bles are shown to greatly improve the forecast skill (Giebel and Kariniotakis 2007;Monteiro et al 2009;Mahoney et al 2012;Ahlstrom et al 2013;Orwig et al 2014;Tuohy et al 2015). Figure 7.1 indicates that this blending can provide value beyond that of the input models.…”

Section: Overview Of Scalesmentioning

confidence: 99%

Short-Range Forecasting for Energy

Haupt

2018

Weather &Amp; Climate Services for the Energy Industry

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Short-range forecasts for periods on the order of hours to days and up to two weeks ahead are necessary to smoothly run transmission and distribution systems, plan maintenance, protect infrastructure and allocate units. In particular, forecasting the renewable energy resources on a day-to-day basis enables integration of increasing capacities of these variable resources. This chapter describes the basics of this short-range forecasting, beginning with the observation-based "nowcasting" of the first 15 minutes and ranging up to two weeks using numerical weather prediction. We discuss how blending multiple forecasts can increase accuracy and how probabilistic forecasts are being used to quantify the forecast uncertainty.

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“…These global models predict some meteorological variables for points in a low resolution grid. NWP predicted variables have been used as input for machine learning techniques mainly for wind power prediction [2,12] and recently for solar energy forecasting [18,8].…”

Section: Introductionmentioning

confidence: 99%

A Study of Machine Learning Techniques for Daily Solar Energy Forecasting Using Numerical Weather Models

Aler

Martín

Valls

et al. 2015

Studies in Computational Intelligence

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Abstract. Forecasting solar energy is becoming an important issue in the context of renewable energy sources and Machine Learning Algorithms play an important rule in this field. The prediction of solar energy can be addressed as a time series prediction problem using historical data. Also, solar energy forecasting can be derived from numerical weather prediction models (NWP). Our interest is focused on the latter approach. We focus on the problem of predicting solar energy from NWP computed from GEFS, the Global Ensemble Forecast System, which predicts meteorological variables for points in a grid. In this context, it can be useful to know how prediction accuracy improves depending on the number of grid nodes used as input for the machine learning techniques. However, using the variables from a large number of grid nodes can result in many attributes which might degrade the generalization performance of the learning algorithms. In this paper both issues are studied using data supplied by Kaggle for the State of Oklahoma comparing Support Vector Machines and Gradient Boosted Regression. Also, three different feature selection methods have been tested: Linear Correlation, the ReliefF algorithm and, a new method based on local information analysis.

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Wind power forecasting : state-of-the-art 2009.

Cited by 300 publications

References 190 publications

Predicting Ramp Events with a Stream-Based HMM Framework

Predicting Ramp Events with a Stream-Based HMM Framework

Short-Range Forecasting for Energy

A Study of Machine Learning Techniques for Daily Solar Energy Forecasting Using Numerical Weather Models

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