A new approach to very short term wind speed prediction using k-nearest neighbor classification

Yeşilbudak, Mehmet; Sağıroğlu, Şeref; Çolak, İlhami

doi:10.1016/j.enconman.2013.01.033

Cited by 100 publications

(35 citation statements)

References 35 publications

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“…It is proven that the wind power generation forecasting errors largely depend on the wind speed [2,8,16,18]. Theoretically, wind speed series containing sensitivity and wind forecasting results could be improved by introducing chaos theory [19,20].…”

Section: Lorenz System and Wind Power Forecastingmentioning

confidence: 99%

Wind Power Prediction Considering Nonlinear Atmospheric Disturbances

et al. 2015

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This paper considers the effect of nonlinear atmospheric disturbances on wind power prediction. A Lorenz system is introduced as an atmospheric disturbance model. Three new improved wind forecasting models combined with a Lorenz comprehensive disturbance are put forward in this study. Firstly, we define the form of the Lorenz disturbance variable and the wind speed perturbation formula. Then, different artificial neural network models are used to verify the new idea and obtain better wind speed predictions. Finally we separately use the original and improved wind speed series to predict the related wind power. This proves that the corrected wind speed provides higher precision wind power predictions. This research presents a totally new direction in the wind prediction field and has profound theoretical research value and practical guiding significance.

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Section: Lorenz System and Wind Power Forecastingmentioning

confidence: 99%

Wind Power Prediction Considering Nonlinear Atmospheric Disturbances

et al. 2015

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“…Spam filtering, investment risk and energy consumption forecasting are some examples of predictive modeling. Predictive modeling approaches include: Artificial Neural Networks for energy consumption [2], Support Vector Machines for energy consumption [2] and KNearest Neighbors for wind power [3].…”

Section: Related Work a Machine Learningmentioning

confidence: 99%

MLaaS: Machine Learning as a Service

Ribeiro

Grolinger

Capretz

2015

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)

312

120

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Abstract-The demand for knowledge extraction has been increasing. With the growing amount of data being generated by global data sources (e.g., social media and mobile apps) and the popularization of context-specific data (e.g., the Internet of Things), companies and researchers need to connect all these data and extract valuable information. Machine learning has been gaining much attention in data mining, leveraging the birth of new solutions. This paper proposes an architecture to create a flexible and scalable machine learning as a service. An open source solution was implemented and presented. As a case study, a forecast of electricity demand was generated using real-world sensor and weather data by running different algorithms at the same time.

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“…With the continuous increase in energy demand, the consumption of non-renewable energy sources, such as coal and oil, has become alarmingly serious, resulting in an ever-growing energy crisis. This is due to the fact that fossil fuels, such as coal and oil, are slowly drying up, and non-renewable energy will become history in the near future [3]. In view of this present situation, people have gradually turned their attention to the development and utilization of new energy sources and have tried to change the trend in energy consumption to relieve, to some extent, the double pressure caused by the dry up of conventional energy and worsening of the global ecological environment [4].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Wind Speed Forecasting System Based on a ‘Decomposition and Ensemble’ Strategy and Fuzzy Time Series

Yang

Jiang

2017

Energies

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Accurate and stable wind speed forecasting is of critical importance in the wind power industry and has measurable influence on power-system management and the stability of market economics. However, most traditional wind speed forecasting models require a large amount of historical data and face restrictions due to assumptions, such as normality postulates. Additionally, any data volatility leads to increased forecasting instability. Therefore, in this paper, a hybrid forecasting system, which combines the 'decomposition and ensemble' strategy and fuzzy time series forecasting algorithm, is proposed that comprises two modules-data pre-processing and forecasting. Moreover, the statistical model, artificial neural network, and Support Vector Regression model are employed to compare with the proposed hybrid system, which is proven to be very effective in forecasting wind speed data affected by noise and instability. The results of these comparisons demonstrate that the hybrid forecasting system can improve the forecasting accuracy and stability significantly, and supervised discretization methods outperform the unsupervised methods for fuzzy time series in most cases.

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A new approach to very short term wind speed prediction using k-nearest neighbor classification

Cited by 100 publications

References 35 publications

Wind Power Prediction Considering Nonlinear Atmospheric Disturbances

Wind Power Prediction Considering Nonlinear Atmospheric Disturbances

MLaaS: Machine Learning as a Service

A Hybrid Wind Speed Forecasting System Based on a ‘Decomposition and Ensemble’ Strategy and Fuzzy Time Series

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