Wind Power Forecasting Using Support Vector Machine Model in RStudio

Pawar, Archana; Jape, V. S.; Mathew, Seema

doi:10.1007/978-981-13-0617-4_28

Cited by 6 publications

(3 citation statements)

References 4 publications

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“…Based on the reviews proposed in the listed literature, it is evident that deterministic forecasting models have been widely used over the last few decades for wind power predictions. Among these, the most popular are conventional time series based models, such as autoregressive moving average, 13 autoregressive integrated moving average, 14 and Grey method, 15 as well as artificial intelligence‐based models, 16 like artificial neural networks 17 and support vector machine 18 . Continuous technological development has also led to new methodologies being considered for wind power forecasting, like deep learning for example 19 .…”

Section: Introductionmentioning

confidence: 99%

“…Among these, the most popular are conventional time series based models, such as autoregressive moving average, 13 autoregressive integrated moving average, 14 and Grey method, 15 as well as artificial intelligence-based models, 16 like artificial neural networks 17 and support vector machine. 18 Continuous technological development has also led to new methodologies being considered for wind power forecasting, like deep learning for example. 19 The challenges related to the uncertainty of predictions in wind power forecasting have gained great interest in the recent literature.…”

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confidence: 99%

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Predictive analytics beyond time series: Predicting series of events extracted from time series data

et al. 2022

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Realizing carbon neutral energy generation creates the challenge of accurately predicting time-series generation data for long-term capacity planning and for shortterm operational decisions. The key challenges for adopting data-driven decisionmaking, specifically predictive analytics, can be attributed to data volume and velocity. Data volume poses challenges for data storage and retrieval. Data velocity poses challenges for processing the data near real time for operational decisions or for capacity building. This manuscript proposes a novel prediction method to tackle the above two challenges by using an event-based prediction in place of traditional time series prediction methods. The central concept is to extract meaningful information, denoted by events, from time-series data and use these events for predictive analysis. These extracted events retain the information required for predictive analytics while significantly reducing the volume of the velocity of data; consequently, a series of events present the information at a glance, effectively enabling data-driven decision-making. This method is applied to a data set consisting of six years of historical wind power capacity factor and temperature measurements. Deploying five deep learning models, a comparison is drawn between classical time-series predictions and series of events predictions based on computational time and several error metrics.The computational analysis results are presented in graphical format and a comparative discussion is drawn on the prediction results. The results indicate that the proposed method obtains the same or better prediction accuracy while significantly reducing computational time and data volume.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

Predictive analytics beyond time series: Predicting series of events extracted from time series data

et al. 2022

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“…NWP input is optional for these models. They are faster during the development period and processing results than physical models, and many have been studied for this purpose, such as autoregressive moving average with exogenous input (ARMAX) [13], autoregressive integrated moving average (ARIMA) [14], neural networks (NNs) [15], support vector machines (SVMs) [16], fuzzy logic [17], [18], and extreme learning machines (ELM) [19], [20]. (iii) Hybrid models, in order to improve forecasting performance, combine different methodologies to take advantage of each method [21], such as weighting-based models, hybrid models with data preprocessing techniques, hybrid models with parameter selection, and optimization techniques and hybrid models with error processing techniques [22]- [26].…”

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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Short Term Wind Power Prediction Based on Wavelet Transform and BP Neural Network

Zheng

Jia

Zhang

et al. 2020

Green Energy and Networking

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Wind Power Forecasting Using Support Vector Machine Model in RStudio

Cited by 6 publications

References 4 publications

Predictive analytics beyond time series: Predicting series of events extracted from time series data

Predictive analytics beyond time series: Predicting series of events extracted from time series data

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

Short Term Wind Power Prediction Based on Wavelet Transform and BP Neural Network

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