Short term wind power forecasting using k-nearest neighbour (KNN)

Mahaseth, Rahul; Kumar, Neeraj; Aggarwal, Aayush; Tayal, Anshul; Kumar, Amit; Gupta, R.

doi:10.1080/02522667.2022.2042093

Cited by 6 publications

(3 citation statements)

References 11 publications

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“…These models have shown satisfactory performance in some cases, but they do not perform well when the dataset is more complex and has non-linear features [144,199]. Machine Learning techniques, such as SVM, KNN, and ANN, have emerged as promising alternatives to traditional models, offering better performance in capturing complex relationships and non-linearity in time-series data [185,200,201]. However, these models may require more computational resources and can be less interpretable than traditional models [33,54].…”

Section: Discussion Of Time-series Forecasting Models For Industrial ...mentioning

confidence: 99%

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A Review of Time-Series Forecasting Algorithms for Industrial Manufacturing Systems

Fatima,

Rahimi

2024

Machines

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Time-series forecasting is crucial in the efficient operation and decision-making processes of various industrial systems. Accurately predicting future trends is essential for optimizing resources, production scheduling, and overall system performance. This comprehensive review examines time-series forecasting models and their applications across diverse industries. We discuss the fundamental principles, strengths, and weaknesses of traditional statistical methods such as Autoregressive Integrated Moving Average (ARIMA) and Exponential Smoothing (ES), which are widely used due to their simplicity and interpretability. However, these models often struggle with the complex, non-linear, and high-dimensional data commonly found in industrial systems. To address these challenges, we explore Machine Learning techniques, including Support Vector Machine (SVM) and Artificial Neural Network (ANN). These models offer more flexibility and adaptability, often outperforming traditional statistical methods. Furthermore, we investigate the potential of hybrid models, which combine the strengths of different methods to achieve improved prediction performance. These hybrid models result in more accurate and robust forecasts. Finally, we discuss the potential of newly developed generative models such as Generative Adversarial Network (GAN) for time-series forecasting. This review emphasizes the importance of carefully selecting the appropriate model based on specific industry requirements, data characteristics, and forecasting objectives.

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Section: Discussion Of Time-series Forecasting Models For Industrial ...mentioning

confidence: 99%

“…In the energy sector, Mahaset et al [185] utilized a KNN-based approach for shortterm wind speed forecasting, demonstrating improved prediction accuracy compared with traditional time-series models. This accurate wind speed forecasting is essential for efficiently integrating wind energy into power grids.…”

Section: K-nearest Neighbors Modelsmentioning

confidence: 99%

A Review of Time-Series Forecasting Algorithms for Industrial Manufacturing Systems

Fatima,

Rahimi

2024

Machines

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“…The KNN algorithm is a non-parametric method used for regression (and classification). It predicts the value of a new point based on the 'K' nearest points in the training dataset [28,29]. The output is typically the average of the values of its nearest neighbors.…”

Section: Methodsmentioning

confidence: 99%

Comparison of machine learning models in forecasting different ENSO types

Ibebuchi,

Rainey,

Obarein

et al. 2024

Phys. Scr.

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Accurate forecasting of the El Niño Southern Oscillation (ENSO) plays a critical role in mitigating the impacts of extreme weather conditions linked to ENSO variability on ecosystems. This study evaluates the performance of six machine learning models in forecasting two ENSO types: the Central Pacific El Niño (Niño 4 index) and the East Central Pacific El Niño (Niño 3.4 index). The models analyzed include the Feed Forward Neural Network (FFNN), Long Short-term Memory (LSTM) neural network, eXtreme Gradient Boosting Regressor, K-Nearest Neighbors Regressor, Gradient Boosting Regressor, and Support Vector Regressor, using the ENSO index lagged by six months as the predictor. The models were trained on the monthly ENSO indices from 1870 to 1992 and tested from 1993 to 2023. We also assess the relative predictability of the two ENSO types. Events were defined as when the ENSO index exceeded ±0.4. Our evaluation during the testing period reveals that for the analyzed models, the deep neural network models (LSTM and FFNN) demonstrated superior performance in forecasting ENSO at a 6-month lead time. Furthermore, all models achieved impressive all-season correlations ranging from 0.93 to 0.97 and threat score for the ENSO phases between 0.71 to 0.88 for Niño 3.4 events, and 0.72 to 0.93 for Niño 4 events. The predictability of the two ENSO types depended on the model and strength of the ENSO event. Considering both ENSO phases, La Niña events were forecasted with a higher accuracy relative to El Niño events, and all models, besides the deep learning models, notably fell short in capturing the extreme 2015/2016 Central Pacific El Niño event. These results highlight the potential of machine learning models, particularly the deep learning approaches, for skillful ENSO forecasting, by leveraging its historical data.

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