Combined forecasting models for wind energy forecasting: A case study in China

Xiao, Litian; Wang, Jianzhou; Dong, Yao; Wu, Jie

doi:10.1016/j.rser.2014.12.012

Cited by 178 publications

(73 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Various attempts have been made to use hybrid methods for short-term wind forecasting. The combined approaches most commonly seen in the literature are data pre-processing-based approaches, parameter-optimization-based approaches and weighting-based approaches [34]. Combination forecasts can be used to enhance the eventual prediction results because they can integrate signal forecasting models and make use of component forecasts.…”

Section: Combined Modelmentioning

confidence: 99%

A Novel Combined Model Based on an Artificial Intelligence Algorithm—A Case Study on Wind Speed Forecasting in Penglai, China

2016

View full text Add to dashboard Cite

Abstract:Wind speed forecasting plays a key role in wind-related engineering studies and is important in the management of wind farms. Current forecasting models based on different optimization algorithms can be adapted to various wind speed time series data. However, these methodologies cannot aggregate different hybrid forecasting methods and take advantage of the component models. To avoid these limitations, we propose a novel combined forecasting model called SSA-PSO-DWCM, i.e., particle swarm optimization (PSO) determined weight coefficients model. This model consisted of three main steps: one is the decomposition of the original wind speed signals to discard the noise, the second is the parameter optimization of the forecasting method, and the last is the combination of different models in a nonlinear way. The proposed combined model is examined by forecasting the wind speed (10-min intervals) of wind turbine 5 located in the Penglai region of China. The simulations reveal that the proposed combined model demonstrates a more reliable forecast than the component forecasting engines and the traditional combined method, which is based on a linear method.

show abstract

Section: Combined Modelmentioning

confidence: 99%

A Novel Combined Model Based on an Artificial Intelligence Algorithm—A Case Study on Wind Speed Forecasting in Penglai, China

2016

View full text Add to dashboard Cite

show abstract

“…The combined forecasting models were initially proposed by Bates and Granger who proved that the linear combination of two forecasting models could obtain better forecasting results than the single models alone. Xiao et al [26] and Wang et al [27] also proved that the forecasting accuracy of the combined model were higher than that of a single model. The basic principles of the combined forecasting methods are to integrate the forecasting output results of different single models based on certain weights, narrowing the value range of the forecasting down to a smaller scale.…”

Section: Introductionmentioning

confidence: 97%

Research and Application of a Hybrid Forecasting Model Based on Data Decomposition for Electrical Load Forecasting

Dong

et al. 2016

Energies

Self Cite

View full text Add to dashboard Cite

Accurate short-term electrical load forecasting plays a pivotal role in the national economy and people's livelihood through providing effective future plans and ensuring a reliable supply of sustainable electricity. Although considerable work has been done to select suitable models and optimize the model parameters to forecast the short-term electrical load, few models are built based on the characteristics of time series, which will have a great impact on the forecasting accuracy. For that reason, this paper proposes a hybrid model based on data decomposition considering periodicity, trend and randomness of the original electrical load time series data. Through preprocessing and analyzing the original time series, the generalized regression neural network optimized by genetic algorithm is used to forecast the short-term electrical load. The experimental results demonstrate that the proposed hybrid model can not only achieve a good fitting ability, but it can also approximate the actual values when dealing with non-linear time series data with periodicity, trend and randomness.

show abstract

“…Along with the rapid development of technology in the last few decades, energy demands continue to increase rapidly [1]. In accordance with the IEA World Energy Outlook 2010, China and India will be responsible for approximately 50% of the growth in global energy demand by 2050.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Multi-Step Rolling Forecasting Model Based on SSA and Simulated Annealing—Adaptive Particle Swarm Optimization for Wind Speed

Jin

Zhang

2016

Sustainability

View full text Add to dashboard Cite

Abstract:With the limitations of conventional energy becoming increasing distinct, wind energy is emerging as a promising renewable energy source that plays a critical role in the modern electric and economic fields. However, how to select optimization algorithms to forecast wind speed series and improve prediction performance is still a highly challenging problem. Traditional single algorithms are widely utilized to select and optimize parameters of neural network algorithms, but these algorithms usually ignore the significance of parameter optimization, precise searching, and the application of accurate data, which results in poor forecasting performance. With the aim of overcoming the weaknesses of individual algorithms, a novel hybrid algorithm was created, which can not only easily obtain the real and effective wind speed series by using singular spectrum analysis, but also possesses stronger adaptive search and optimization capabilities than the other algorithms: it is faster, has fewer parameters, and is less expensive. For the purpose of estimating the forecasting ability of the proposed combined model, 10-min wind speed series from three wind farms in Shandong Province, eastern China, are employed as a case study. The experimental results were considerably more accurately predicted by the presented algorithm than the comparison algorithms.

show abstract

Combined forecasting models for wind energy forecasting: A case study in China

Cited by 178 publications

References 34 publications

A Novel Combined Model Based on an Artificial Intelligence Algorithm—A Case Study on Wind Speed Forecasting in Penglai, China

A Novel Combined Model Based on an Artificial Intelligence Algorithm—A Case Study on Wind Speed Forecasting in Penglai, China

Research and Application of a Hybrid Forecasting Model Based on Data Decomposition for Electrical Load Forecasting

A Hybrid Multi-Step Rolling Forecasting Model Based on SSA and Simulated Annealing—Adaptive Particle Swarm Optimization for Wind Speed

Contact Info

Product

Resources

About