Journal ArticleDOI
A review on the forecasting of wind speed and generated power
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TLDR
A bibliographical survey on the general background of research and developments in the fields of wind speed and wind power forecasting and further direction for additional research and application is proposed.Abstract:
In the world, wind power is rapidly becoming a generation technology of significance. Unpredictability and variability of wind power generation is one of the fundamental difficulties faced by power system operators. Good forecasting tools are urgent needed under the relevant issues associated with the integration of wind energy into the power system. This paper gives a bibliographical survey on the general background of research and developments in the fields of wind speed and wind power forecasting. Based on the assessment of wind power forecasting models, further direction for additional research and application is proposed.read more
Citations
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Proceedings ArticleDOI
A review of wind power and wind speed forecasting methods with different time horizons
TL;DR: In this article, the main challenges and problems associated with wind power prediction are discussed, and an overview of comparative analysis of various available forecasting techniques is discussed as well as a major challenges and major challenges.
Journal ArticleDOI
ARMA based approaches for forecasting the tuple of wind speed and direction
Ergin Erdem,Jing Shi +1 more
TL;DR: In this paper, four approaches based on autoregressive moving average (ARMA) method are employed for short-term forecasting of wind speed and direction are employed to forecast wind turbine operation and efficient energy harvesting.
Journal ArticleDOI
On comparing three artificial neural networks for wind speed forecasting
TL;DR: A comprehensive comparison study on the application of different artificial neural networks in 1-h-ahead wind speed forecasting shows that even for the same wind dataset, no single neural network model outperforms others universally in terms of all evaluation metrics.
Journal ArticleDOI
Probabilistic Forecasting of Wind Power Generation Using Extreme Learning Machine
TL;DR: In this paper, an extreme learning machine (ELM)-based probabilistic forecasting method for wind power generation is proposed to account for the uncertainties in the forecasting results, several bootstrap methods have been compared for modeling the regression uncertainty, based on which the pairs bootstrap method is identified with the best performance.
Journal ArticleDOI
Smart home energy management systems: Concept, configurations, and scheduling strategies
TL;DR: In this paper, a brief overview on the architecture and functional modules of smart HEMS is presented, and various home appliance scheduling strategies to reduce the residential electricity cost and improve the energy efficiency from power generation utilities are also investigated.
References
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Journal ArticleDOI
Support vector machines for wind speed prediction
TL;DR: This paper introduces support vector machines (SVM), the latest neural network algorithm, to wind speed prediction and compares their performance with the multilayer perceptron (MLP) neural networks.
Journal ArticleDOI
Forecast of hourly average wind speed with ARMA models in Navarre (Spain)
TL;DR: It has been proven that the transformation and standardization of the original series allow the use of ARMA models and these behave significantly better in the forecast than the persistence model, especially in the longer-term forecasts.
Journal ArticleDOI
An Advanced Statistical Method for Wind Power Forecasting
G. Sideratos,Nikos Hatziargyriou +1 more
TL;DR: In this paper, an advanced statistical method for wind power forecasting based on artificial intelligence techniques is presented, which requires as input past power measurements and meteorological forecasts of wind speed and direction interpolated at the site of the wind farm.
Journal ArticleDOI
A fuzzy model for wind speed prediction and power generation in wind parks using spatial correlation
TL;DR: A fuzzy model is suggested for the prediction of wind speed and the produced electrical power at a wind park using a genetic algorithm-based learning scheme, and achieves an adequate understanding of the problem.
Journal ArticleDOI
A comparison of various forecasting techniques applied to mean hourly wind speed time series
TL;DR: A comparison of various forecasting approaches, using time series analysis, on mean hourly wind speed data, including the traditional linear (ARMA) models and the commonly used feed forward and recurrent neural networks is presented.