Short-term wind power forecasting using nonnegative sparse coding

Kim, Seung Jun; Giannakis, Georgios B.

doi:10.1109/ciss.2015.7086873

Cited by 7 publications

(3 citation statements)

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“…Although not explicitly recovering deleted outliers, [20] mentions that these recordings can be corrected by temporal and spatial interpolation methods [86][87][88], and that the fitted power curve based on normal measurements could also be utilised to infer the P out values for given wind speeds as inputs. Furthermore, [40] states that some imputation approaches (e.g., [89]) can be used to replace (or predict) missing data due to outlier filtering, but it argues that these efforts may not be necessary for two reasons: (1) the outliers are often consistent and lasting for longer periods, and there are not sufficient data to make a smooth imputation; (2) there will be enough remaining data to obtain interesting patterns, as usually a relatively small portion of outliers is removed.…”

Section: Approaches For Testing Success Of Outlier Detection and Removal/treatment Proceduresmentioning

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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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: Approaches For Testing Success Of Outlier Detection and Removal/treatment Proceduresmentioning

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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“…Moreover, besides load forecasting, several power grid-related problems have been addressed using sparse coding approach. In [22], real-time wind-power forecasting is achieved by online nonnegative sparse coding with elastic net regularization. The efficient and wellknown algorithm K-SVD [23] has also recently been used in the smart grid framework [18], either for disaggregating a building's energy into the energy consumed by individual appliances [24], or compressing data from individual smart meters and extracting partial usage -sparse -patterns [25].…”

Section: Introductionmentioning

confidence: 99%

Short-Term Electric Load Forecasting With Sparse Coding Methods

et al. 2021

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Short-term load forecasting is a key task for planning and stability of the current and future distribution grid, as it can significantly contribute to the management of energy market for ancillary services. In this paper we introduce the beneficial properties of applications of sparse representation and corresponding dictionary learning to the net load forecasting problem on a substation level. In this context, sparse representation theory can provide parsimonial predictive models, which become attractive mainly due to their ability to successfully model the input space in a self-learning manner, by interacting between theory, algorithms, and applications. Several techniques are implemented, incorporating numerous dictionary learning and sparse decomposition algorithms, and a hierarchical structured model is proposed. The concept of sparsity in each case is embedded throughout the utilization of different regularization forms which include the 0 , 1 , 2 and tree 0 norms. The observed superiority of the proposed theory, especially the one which embeds the atoms and corresponding coefficients in a tree structure, stems from the construction of the dictionary so as to represent efficiently the ambient electricity signal space and the consequent extraction of sparse basis-vectors. The performance of each model is evaluated using real hourly load measurements from a high voltage/medium voltage (HV/MV) substation and compared with that of widely used machine learning methods. The provided analytical results, verify the effectiveness of hierarchical sparse representation in short-term load forecasting applications, in terms of common accuracy indices.INDEX TERMS Generative models, hierarchical dictionaries, load forecasting, power grid, sparse representation.

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“…Alternative proposals can be found for high-dimensional spatio-temporal WPF problems. Among others, the compressive sensing and structured-sparse recovery algorithms [26], [28], dictionary learning method [29] and two-stage sparse vector autoregressive (VAR) model [30] have achieved results with reasonably sparse structures. However, these methods can only provide overall and fully data-driven sparse structures, while forecasters and forecast users may be interested in controlling sparsity in a finer way, e.g., by using knowledge on space time wind dynamics and layout of wind farms as natural constraints on sparsity.…”

Section: Introductionmentioning

confidence: 99%

Correlation-Constrained and Sparsity-Controlled Vector Autoregressive Model for Spatio-Temporal Wind Power Forecasting

Zhao

Yang

Pinson

et al. 2018

IEEE Trans. Power Syst.

116

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Short-term wind power forecasting using nonnegative sparse coding

Cited by 7 publications

References 20 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

Short-Term Electric Load Forecasting With Sparse Coding Methods

Correlation-Constrained and Sparsity-Controlled Vector Autoregressive Model for Spatio-Temporal Wind Power Forecasting

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