Wind power forecasting using the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si14.gif" display="inline" overflow="scroll"><mml:mi>k</mml:mi></mml:math>-nearest neighbors algorithm

Мангалова, Е С; Агафонов, Е. Д.

doi:10.1016/j.ijforecast.2013.07.008

Cited by 57 publications

(16 citation statements)

References 2 publications

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“…For example, the presence of outliers is one of the biggest difficulties in applying appropriate PC-based monitoring methods, as the outliers usually result in unsatisfactory power curve limits and it is important that input data for the limit-setting algorithms do not include outliers [31]. Undetected or incorrectly processed outliers also have strong negative impact in applications related to wind power forecasting, power flow studies and economic dispatch analysis, since the models with outliers are likely to have lower accuracy, higher forecasting errors and suboptimal unit commitment results [36][37][38][39].…”

Section: Introductionmentioning

confidence: 99%

“…The importance of outlier analysis is reflected in a recent increase of interest of both scientific and engineering communities in approaches and methods for detection and processing of outliers in measurement-based WT and WF models. Most of the proposed approaches are statistical [14,15,18,20,[23][24][25][26]29,31,[33][34][35]37,[39][40][41][42][43][44][45][46][47][48][49]. More specifically, Refs.…”

Section: Introductionmentioning

confidence: 99%

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A Review of Approaches for the Detection and Treatment of Outliers in Processing Wind Turbine and Wind Farm Measurements

Zou

Djokić

2020

Energies

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Due to the significant increase of the number of wind-based electricity generation systems, it is important to have accurate information on their operational characteristics, which are typically obtained by processing large amounts of measurements from the individual wind turbines (WTs) and from the whole wind farms (WFs). For further processing of these measurements, it is important to identify and remove bad quality or abnormal data, as otherwise obtained WT and WF models may be biased, or even inaccurate. There are wide ranges of both causes and manifestations of these bad/abnormal data, which are often denoted with the common general term “outlier”. This paper reviews approaches for the detection and treatment of outliers in processing WT and WF measurements, starting from the discussion of the commonly measured parameters, variables and resolutions, as well as the corresponding requirements and recommendations in related standards. Afterwards, characteristics and causes of outliers reported in existing literature are discussed and illustrated, as well as the requirements for the data rejection in related standard. Next, outlier identification methods are reviewed, followed by a review of approaches for testing the success of outlier removal procedures, with a discussion of their potential negative effects and impact on the WT and WF models. Finally, the paper indicates some issues and concerns that could be of interests for the further research on the detection and treatment of outliers in processing WT and WF measurements.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Review of Approaches for the Detection and Treatment of Outliers in Processing Wind Turbine and Wind Farm Measurements

Zou

Djokić

2020

Energies

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“…Relevant literature on load and generation forecasting is quite heterogeneous; this is highlighted by the comparative dissertations in reviews and surveys [8,9], clearly showing that no method outperforms the others in every aspect. Major efforts have been devoted to point prediction, for which researchers and practitioners often individuate Artificial Neural Networks (ANN) [10,11], K-Nearest Neighbors (KNN) [12], support vector regression [13], Random Forests (RF) [14], and multiple linear regression models [15] as the best solutions.…”

Section: Introductionmentioning

confidence: 99%

“…Pinball Score values averaged over the tasks[11][12][13][14][15]. Bold values highlight the best results for each zone.…”

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confidence: 99%

Developing and Comparing Different Strategies for Combining Probabilistic Photovoltaic Power Forecasts in an Ensemble Method

2019

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Accurate probabilistic forecasts of renewable generation are drivers for operational and management excellence in modern power systems and for the sustainable integration of green energy. The combination of forecasts provided by different individual models may allow increasing the accuracy of predictions; however, in contrast to point forecast combination, for which the simple weighted averaging is often a plausible solution, combining probabilistic forecasts is a much more challenging task. This paper aims at developing a new ensemble method for photovoltaic (PV) power forecasting, which combines the outcomes of three underlying probabilistic models (quantile k-nearest neighbors, quantile regression forests, and quantile regression) through a weighted quantile combination. Due to the challenges in combining probabilistic forecasts, the paper presents different combination strategies; the competing strategies are based on unconstrained, constrained, and regularized optimization problems for estimating the weights. The competing strategies are compared to individual forecasts and to several benchmarks, using the data published during the Global Energy Forecasting Competition 2014. Numerical experiments were run in MATLAB and R environments; the results suggest that unconstrained and Least Absolute Shrinkage and Selection Operator (LASSO)-regularized strategies exhibit the best performances for the datasets under study, outperforming the best competitors by 2.5 to 9% of the Pinball Score.

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“…These methods can be divided into two main groups of parametric and nonparametric models, in which K-Nearest Neighbour (K-NN) method is one of the most acknowledged nonparametric ones [1]. In the past decades, K-NN has been applied in a variety of areas such as density estimation [1], rainfall-runo forecasting [2], resampling hydrologic time series [3], generating regional climate scenarios [4], short-term tra c ow prediction [5], wind power forecasting [6], short-term rainfall predictions [7], probabilistic stream ow fore-casts [8], modelling hydrological time series [9], shortterm foreign exchange forecast [10], and long-term rainfall probabilistic predictions [11].…”

Section: Introductionmentioning

confidence: 99%

Improving the Accuracy of K-Nearest Neighbour Method in Long Lead Hydrological Forecasting

Azmi¹,

Sarmadi²

2016

Scientia Iranica

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The nonparametric regression method of K-Nearest Neighbour (K-NN) has been used in a variety of eco-hydrological issues. In this study, some techniques were presented to improve the accuracy of the K-NN method in forecasting accumulated 9month in ow, from 1971 to 2001, of Zayandeh-rud dam in Iran, from winter to the end of the following summer. The considered improving techniques consisted of: 1) selecting the best data preprocessing functions, 2) selecting the best number of neighbours, 3) selecting the best distance functions, 4) specifying the best weights of predictors at distance functions, and 5) adding the ability of extrapolation to K-NN using a proposed method. Final results showed that the use of the mentioned techniques had promoted the accuracy of K-NN's forecast, meaningfully. The results of goodness-of-t criteria for the optimized K-NN in comparison with a regular K-NN presented an increase by 31% in correlation coe cient

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Wind power forecasting using the k-nearest neighbors algorithm

Cited by 57 publications

References 2 publications

A Review of Approaches for the Detection and Treatment of Outliers in Processing Wind Turbine and Wind Farm Measurements

A Review of Approaches for the Detection and Treatment of Outliers in Processing Wind Turbine and Wind Farm Measurements

Developing and Comparing Different Strategies for Combining Probabilistic Photovoltaic Power Forecasts in an Ensemble Method

Improving the Accuracy of K-Nearest Neighbour Method in Long Lead Hydrological Forecasting

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