The study and application of a novel hybrid forecasting model – A case study of wind speed forecasting in China

Wang, Jianzhou; Wang, Yun; Jiang, Ping

doi:10.1016/j.apenergy.2015.01.038

Cited by 145 publications

(44 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Numerous studies have described that many insects and animals exhibit the behavior of Lévy flights [25]. A moving objective takes a stochastic step to alter the behavior of a system; this situation can be described as a Lévy flights; a sketch is shown in Figure 1, part c.…”

Section: Cuckoo Search (Cs) Algorithmmentioning

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: Cuckoo Search (Cs) Algorithmmentioning

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

“…Its basic principle can be described as below: firstly, making some assumptions, then statistical reasoning, and lastly determining whether to reject or accept the hypothesis under a level of significance that is defined beforehand [4]. There are many commonly used methods of hypothesis testing such as T-test, F-test, rank and inspection, etc.…”

Section: Statistical Hypothesis Testingmentioning

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

“…Liu [20] predicted short-term wind speed by a hybrid model combining wavelet transform (WT) and SVM optimized by genetic algorithm, and the simulation results demonstrated that the proposed hybrid model is more efficient than the other comparison models adopted in their paper. Wang et al [21] developed a hybrid forecasting model based on WPT, particle swarm optimization (PSO), simulated annealing (SA), phase space reconstruction (PSR) and LSSVM for multi-step ahead wind speed forecasting, and the case studies showed that the proposed model outperformed all the other comparison models. Zhang et al [22] investigated the usage of WT-LSSVM model in the time series forecasting of fair-weather atmospheric electric field, and the experimental results showed that the proposed WT-LSSVM model is a superior method compared to the single LSSVM and ANN model.…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Model Based on Machine Learning Methods and Data Preprocessing for Short-Term Electric Load Forecasting

et al. 2017

View full text Add to dashboard Cite

Abstract:The experience with deregulated electricity market has shown the increasingly important role of short-term electric load forecasting in the energy producing and scheduling. However, because of nonlinear, stochastic and nonstable characteristics associated with the electric load series, it is extremely difficult to precisely forecast the electric load. This paper aims to establish a novel ensemble model based on variational mode decomposition (VMD) and extreme learning machine (ELM) optimized by differential evolution (DE) algorithm for multi-step ahead electric load forecasting. The proposed model is novel in the sense that VMD is firstly applied to decompose the original electric load series into a set of components with different frequencies in order to effectively eliminate the stochastic fluctuation characteristic so as to improve the overall prediction accuracy. The proposed ensemble model is tested using two electric load series collected from New South Wales (NSW) and Queensland (QLD) in the Australian electricity market. The experimental results show that: (1) the data preprocessing by VMD can effectively decrease the stochastic fluctuation characteristics that existed in the electric load series, consequently improving the whole forecasting accuracy, and; (2) the proposed forecasting model performs better than all other benchmark models for both one-step and multi-step ahead electric load forecasting.

show abstract

The study and application of a novel hybrid forecasting model – A case study of wind speed forecasting in China

Cited by 145 publications

References 38 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

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

An Ensemble Model Based on Machine Learning Methods and Data Preprocessing for Short-Term Electric Load Forecasting

Contact Info

Product

Resources

About