Kernel methods for short-term spatio-temporal wind prediction

Dowell, Jethro; Weiss, Stephan; Infield, David

doi:10.1109/pesgm.2015.7285965

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…This work addresses a regression problem, where kernel machines consider the fact that observational data can be represented by a linear combination of kernel functions [33]. Kernel methods have been successfully applied in time series prediction [34], wind speed forecasting [35], [36], wind power forecasting [37], electric load forecasting [38], [39], and many other applications.…”

Section: Introductionmentioning

confidence: 99%

A Self-Adaptive Multikernel Machine Based on Recursive Least-Squares Applied to Very Short-Term Wind Power Forecasting

2021

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Wind power has contributed significantly to the increase in electricity generation, but a decision-making tool capable of dealing with its variability and limited predictability is necessary. For this purpose, a novel self-adaptive approach for kernel recursive least-squares machines named multiple challengers is introduced in this work, which is successfully used to produce very short-term wind power forecasts at eight wind farms in Australia. The proposed method is based on a competitive tracking method, and the algorithm deals with some common difficulties of kernel methods, e.g., the increasing kernel matrix size associated with time and memory complexities and the overfitting problem. The proposed method always considers the new information received by the model, thus identifying changes in the time series, avoiding abrupt loss of information and maintaining a controlled number of examples since there is an adaptive selection of the active kernel. It works with the smallest dictionary possible, reducing the probability of overfitting. Five minute-ahead wind power forecasts are produced and evaluated in terms of point forecast skill scores and calibration. The results of the proposed method are compared with those provided by other kernel-based versions of the recursive least-squares algorithm, an online version of the extreme learning machine method, and the persistence time series model. An increase in the number of kernels used in the ensemble system can lead to better results when compared with those of single-kernel models.INDEX TERMS Multiple kernel learning, online training, renewable energy, wind power forecasting.

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Section: Introductionmentioning

confidence: 99%

A Self-Adaptive Multikernel Machine Based on Recursive Least-Squares Applied to Very Short-Term Wind Power Forecasting

2021

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“…The Wiener cyclo-stationary filter and the LMS provide speed and direction predictions. Two non-linear methods for the production of short-term spatial-temporal wind speed forecasts are presented in [25]. A kernel least mean square algorithm and a kernel least-square recursive algorithm are introduced and used to generate wind speed forecasts at different locations.…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Kriging Based Economic Dispatch Problem Including Wind Uncertainty

Cuenca¹,

Flórez²,

Patiño³

et al. 2020

Preprint

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The incorporation of wind generation introduces challenges to the operation of the power system due to its uncertain characteristics. Therefore, the development of methods to accurately model the uncertainty is necessary. In this paper, the spatio-temporal Kriging and analog approaches are used to forecast wind power generation and used as input to solve an economic dispatch problem, considering the uncertainties of wind generation. Spatio-temporal Kriging takes into account the spatial and temporal information given by the database to enhance wind forecasts. We evaluate the performance of using the spatio-temporal Kriging, and comparisons are carried out versus other approaches in the framework of the economic power dispatch problem, for which simulations are developed on the modified IEEE 3-bus and IEEE 24-bus test systems. The results show that the use of Kriging-based spatio-temporal models in the context of economic power dispatch can provide an opportunity for lower operating costs in the presence of uncertainty when compared to other approaches.

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“…KRLS has been widely applied to nonlinear signal processing [17][18][19], and a few explorations have also been performed for wind power forecasting. Some work discussed how to improve the accuracy of very short-term wind speed forecasting using KRLS [20,21].…”

Section: Introductionmentioning

confidence: 99%

Modelling of wind power forecasting errors based on kernel recursive least-squares method

Qiao

et al. 2017

J. Mod. Power Syst. Clean Energy

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Forecasting error amending is a universal solution to improve short-term wind power forecasting accuracy no matter what specific forecasting algorithms are applied. The error correction model should be presented considering not only the nonlinear and non-stationary characteristics of forecasting errors but also the field application adaptability problems. The kernel recursive least-squares (KRLS) model is introduced to meet the requirements of online error correction. An iterative error modification approach is designed in this paper to yield the potential benefits of statistical models, including a set of error forecasting models. The teleconnection in forecasting errors from aggregated wind farms serves as the physical background to choose the hybrid regression variables. A case study based on field data is found to validate the properties of the proposed approach. The results show that our approach could effectively extend the modifying horizon of statistical models and has a better performance than the traditional linear method for amending short-term forecasts.

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Kernel methods for short-term spatio-temporal wind prediction

Cited by 5 publications

References 15 publications

A Self-Adaptive Multikernel Machine Based on Recursive Least-Squares Applied to Very Short-Term Wind Power Forecasting

A Self-Adaptive Multikernel Machine Based on Recursive Least-Squares Applied to Very Short-Term Wind Power Forecasting

Spatio-Temporal Kriging Based Economic Dispatch Problem Including Wind Uncertainty

Modelling of wind power forecasting errors based on kernel recursive least-squares method

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