Journal ArticleDOI
A case study on a hybrid wind speed forecasting method using BP neural network
TLDR
This paper proposes a new hybrid wind speed forecasting method based on a back-propagation (BP) neural network and the idea of eliminating seasonal effects from actual wind speed datasets using seasonal exponential adjustment that can forecast the daily average wind speed one year ahead with lower mean absolute errors.Abstract:
Wind energy, which is intermittent by nature, can have a significant impact on power grid security, power system operation, and market economics, especially in areas with a high level of wind power penetration. Wind speed forecasting has been a vital part of wind farm planning and the operational planning of power grids with the aim of reducing greenhouse gas emissions. Improving the accuracy of wind speed forecasting algorithms has significant technological and economic impacts on these activities, and significant research efforts have addressed this aim recently. However, there is no single best forecasting algorithm that can be applied to any wind farm due to the fact that wind speed patterns can be very different between wind farms and are usually influenced by many factors that are location-specific and difficult to control. In this paper, we propose a new hybrid wind speed forecasting method based on a back-propagation (BP) neural network and the idea of eliminating seasonal effects from actual wind speed datasets using seasonal exponential adjustment. This method can forecast the daily average wind speed one year ahead with lower mean absolute errors compared to figures obtained without adjustment, as demonstrated by a case study conducted using a wind speed dataset collected from the Minqin area in China from 2001 to 2006.read more
Citations
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Journal ArticleDOI
A review of combined approaches for prediction of short-term wind speed and power
TL;DR: In this article, a comprehensive research about the combined models is called on for how these models are constructed and affect the forecasting performance, and an up-to-date annotated bibliography of the wind forecasting literature is presented.
Journal ArticleDOI
A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly optimization algorithm
TL;DR: A hybrid annual power load forecasting model combining fruit fly optimization algorithm (FOA) and generalized regression neural network was proposed to solve this problem, where the FOA was used to automatically select the appropriate spread parameter value for the GRNN power load forecasts model.
Journal ArticleDOI
Optimal parameters selection for BP neural network based on particle swarm optimization: A case study of wind speed forecasting
TL;DR: A Back Propagation neural network based on Particle Swam Optimization that combines PSO-BP with comprehensive parameter selection is introduced that achieves much better forecast performance than the basic back propagation neural network and ARIMA model.
Journal ArticleDOI
A new hybrid model for wind speed forecasting combining long short-term memory neural network, decomposition methods and grey wolf optimizer
TL;DR: Experimental results indicate that the proposed combined model can capture non-linear characteristics of WSTS, achieving better forecasting performance than single forecasting models, in terms of accuracy.
Journal ArticleDOI
Forecasting models for wind speed using wavelet, wavelet packet, time series and Artificial Neural Networks
TL;DR: The results of three experimental cases show that the proposed three hybrid models have satisfactory performance in the wind speed predictions, and the Wavelet Packet-ANN model is the best among them.
References
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Journal ArticleDOI
Neural network forecasting for seasonal and trend time series
G.Peter Zhang,Min Qi +1 more
TL;DR: In this paper, the authors investigated the effect of data preprocessing, including deseasonalization and detrending, on neural network modeling and forecasting performance for seasonal time series forecasting.
Journal ArticleDOI
Handbook of Methods of Applied Statistics
Computing, Artificial Intelligence and Information Technology Neural network forecasting for seasonal and trend time series
G. Peter Zhang,Min Qi +1 more
TL;DR: It is found that neural networks are not able to capture seasonal or trend variations effectively with the unpreprocessed raw data and either detrending or deseasonalization can dramatically reduce forecasting errors.
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.
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