A hybrid model based on data preprocessing for electrical power forecasting

Xiao, Liye; Wang, Jianzhou; Yang, Xue‐Song; Xiao, Liyang

doi:10.1016/j.ijepes.2014.07.029

Cited by 63 publications

(23 citation statements)

References 47 publications

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“…It is proved that these three indexes could measure the overall forecasting accuracy of the model well [35]. MAE and MSE are mainly applied to evaluate the deviation between the forecasting value and actual value and the former one is more sensitive to the error than the latter one.…”

Section: Evaluation Metricsmentioning

confidence: 99%

A New Hybrid Model Based on Data Preprocessing and an Intelligent Optimization Algorithm for Electrical Power System Forecasting

Jiang

Liu

2015

Mathematical Problems in Engineering

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The establishment of electrical power system cannot only benefit the reasonable distribution and management in energy resources, but also satisfy the increasing demand for electricity. The electrical power system construction is often a pivotal part in the national and regional economic development plan. This paper constructs a hybrid model, known as the E-MFA-BP model, that can forecast indices in the electrical power system, including wind speed, electrical load, and electricity price. Firstly, the ensemble empirical mode decomposition can be applied to eliminate the noise of original time series data. After data preprocessing, the back propagation neural network model is applied to carry out the forecasting. Owing to the instability of its structure, the modified firefly algorithm is employed to optimize the weight and threshold values of back propagation to obtain a hybrid model with higher forecasting quality. Three experiments are carried out to verify the effectiveness of the model. Through comparison with other traditional well-known forecasting models, and models optimized by other optimization algorithms, the experimental results demonstrate that the hybrid model has the best forecasting performance.

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Section: Evaluation Metricsmentioning

confidence: 99%

A New Hybrid Model Based on Data Preprocessing and an Intelligent Optimization Algorithm for Electrical Power System Forecasting

Jiang

Liu

2015

Mathematical Problems in Engineering

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“…To gain good forecasting accuracy and low deviation, many studies [25][26][27][28][29][30][31][32] have been conducted to determine the optimal weights of neural networks. However, an original hybrid model system-a traditional hybrid method based on the rapid searching theory developed by Xiao et al [33]-has been put into use.…”

Section: Introductionmentioning

confidence: 99%

“…Wang et al [30] modified the CS method to optimize the parameters of multi-step-ahead wind speed forecasting models. Xiao et al [31] applied the genetic algorithm (GA) to optimize the parameters of the proposed model. In the present paper, a highly valid optimization method, the Broyden-Fletcher-Goldfarb-Shanno-Firefly Algorithm (BFGS-FA), is used to determine the parameters of the proposed hybrid model.…”

Section: Introductionmentioning

confidence: 99%

An Innovative Hybrid Model Based on Data Pre-Processing and Modified Optimization Algorithm and Its Application in Wind Speed Forecasting

et al. 2017

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Abstract:Wind speed forecasting has an unsuperseded function in the high-efficiency operation of wind farms, and is significant in wind-related engineering studies. Back-propagation (BP) algorithms have been comprehensively employed to forecast time series that are nonlinear, irregular, and unstable. However, the single model usually overlooks the importance of data pre-processing and parameter optimization of the model, which results in weak forecasting performance. In this paper, a more precise and robust model that combines data pre-processing, BP neural network, and a modified artificial intelligence optimization algorithm was proposed, which succeeded in avoiding the limitations of the individual algorithm. The novel model not only improves the forecasting accuracy but also retains the advantages of the firefly algorithm (FA) and overcomes the disadvantage of the FA while optimizing in the later stage. To verify the forecasting performance of the presented hybrid model, 10-min wind speed data from Penglai city, Shandong province, China, were analyzed in this study. The simulations revealed that the proposed hybrid model significantly outperforms other single metaheuristics.

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“…Extended refers to the production activities will be extended to the product sales and service phase [4]. Therefore, the supply chain is a complex collaborative network composed of suppliers, manufacturers, distributors and retailers to achieve from raw materials to finished products and ultimately meet the needs of internal and external markets, the effective management of the supply chain is the enterprise response to modern Fierce competition in the market one of the main ways [5][6].…”

Section: Introductionmentioning

confidence: 99%

Design and Risk Management of Flexible Supply Chain Network Influenced on Vulnerability Modeling

He¹,

Zhidong²,

Xinquan³

et al. 2017

IJAIASD

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A hybrid model based on data preprocessing for electrical power forecasting

Cited by 63 publications

References 47 publications

A New Hybrid Model Based on Data Preprocessing and an Intelligent Optimization Algorithm for Electrical Power System Forecasting

A New Hybrid Model Based on Data Preprocessing and an Intelligent Optimization Algorithm for Electrical Power System Forecasting

An Innovative Hybrid Model Based on Data Pre-Processing and Modified Optimization Algorithm and Its Application in Wind Speed Forecasting

Design and Risk Management of Flexible Supply Chain Network Influenced on Vulnerability Modeling

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