A case study on a hybrid wind speed forecasting method using BP neural network

Guo, Zhengfu; Wu, Jie; Lu, Hanqing; Wang, Jianzhou

doi:10.1016/j.knosys.2011.04.019

Cited by 316 publications

(142 citation statements)

References 18 publications

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“…In order to obtain better performance, researchers have been constantly developing new technologies and methods for the hydrological prediction. In recent years, many hybrid approaches take advantage of more than one forecasting method to carry out the research work and engineering practice related to the reservoir inflow [34][35][36][37][38][39]. Application results indicate that the hybrid methods have higher forecasting precision than a single forecasting method.…”

Section: Introductionmentioning

confidence: 99%

Heuristic Methods for Reservoir Monthly Inflow Forecasting: A Case Study of Xinfengjiang Reservoir in Pearl River, China

Cheng

Feng

Niu

et al. 2015

Water

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Abstract:Reservoir monthly inflow is rather important for the security of long-term reservoir operation and water resource management. The main goal of the present research is to develop forecasting models for the reservoir monthly inflow. In this paper, artificial neural networks (ANN) and support vector machine (SVM) are two basic heuristic forecasting methods, and genetic algorithm (GA) is employed to choose the parameters of the SVM. When forecasting the monthly inflow data series, both approaches are inclined to acquire relatively poor performances. Thus, based on the thought of refined prediction by model combination, a hybrid forecasting method involving a two-stage process is proposed to improve the forecast accuracy. In the hybrid method, the ANN and SVM are, first, respectively implemented to forecast the reservoir monthly inflow data. Then, the processed predictive values of both ANN and SVM are selected as the input variables of a newly-built ANN model for refined forecasting. Three models, ANN, SVM, and the hybrid method, are developed for the monthly inflow forecasting in Xinfengjiang reservoir with 71-year discharges from 1944 to 2014. The comparison of results reveal that three models have satisfactory performances in the Xinfengjiang reservoir monthly inflow prediction, and the hybrid method performs better than ANN and SVM in terms of five statistical indicators. Thus, the hybrid method is an efficient tool for the long-term operation and dispatching of Xinfengjiang reservoir. OPEN ACCESSWater 2015, 7 4478

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Section: Introductionmentioning

confidence: 99%

Heuristic Methods for Reservoir Monthly Inflow Forecasting: A Case Study of Xinfengjiang Reservoir in Pearl River, China

Cheng

Feng

Niu

et al. 2015

Water

View full text Add to dashboard Cite

show abstract

“…Compared with the other methods, these hybrid models have great improvements in the performance of forecasting accuracy. In other filed, researchers [27][28][29][30] consistently reached a conclusion that hybrid model predicts more accurate than the single models.…”

Section: Introductionmentioning

confidence: 88%

Hybrid Forecasting Approach Based on GRNN Neural Network and SVR Machine for Electricity Demand Forecasting

Yang

et al. 2017

Energies

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Accurate electric power demand forecasting plays a key role in electricity markets and power systems. The electric power demand is usually a non-linear problem due to various unknown reasons, which make it difficult to get accurate prediction by traditional methods. The purpose of this paper is to propose a novel hybrid forecasting method for managing and scheduling the electricity power. EEMD-SCGRNN-PSVR, the proposed new method, combines ensemble empirical mode decomposition (EEMD), seasonal adjustment (S), cross validation (C), general regression neural network (GRNN) and support vector regression machine optimized by the particle swarm optimization algorithm (PSVR). The main idea of EEMD-SCGRNN-PSVR is respectively to forecast waveform and trend component that hidden in demand series to substitute directly forecasting original electric demand. EEMD-SCGRNN-PSVR is used to predict the one week ahead half-hour's electricity demand in two data sets (New South Wales (NSW) and Victorian State (VIC) in Australia). Experimental results show that the new hybrid model outperforms the other three models in terms of forecasting accuracy and model robustness.

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“…However, as for the stock price, the data sets are generally incomplete for the reason that there are always existing noisy outliers. To eliminate the influences caused by the noisy outliers and restore the reality for trend prediction, we need to adopt fuzzy rough theory to serve as the data pre-processing step [6][7].…”

Section: The Proposed Methodologymentioning

confidence: 99%

A Novel Data Mining Algorithm based on BP Neural Network and Its Applications on Stock Price Prediction

Shu¹,

Li²,

Zhuang³

2016

Proceedings of the 2016 3rd International Conference on Materials Engineering, Manufacturing Technology and Control

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Abstract.As an indispensable part in financial industry, stock investment has gradually become more and more important in people's life. The major fluctuation in stock market directly affects the stability of financial markets and the healthy development of the economy. The successful stock price and trend prediction will help investors to yield profit and provide timely and reasonable guidance to market regulators. Therefore, how to forecast stock market is a crucial and worthy topic in financial area. Neural network simulated how brain functions, can automatically extract features from the historical data and related information to predicate future trend, with the primary characteristics of self-organization and adaption. Therefore, neural network is suitable for processing prediction in the field of nonlinear time series. This paper presents a novel data mining algorithm based on the BP neural network and its applications on stock price prediction. Taking improving the efficiency of the traditional back propagation network, we revise the perspective error, and modify incentive function and the adaptive learning rate in order to avoid the local minimum and decrease training time. Moreover, to eliminate the influences caused by noisy data, we combine the fuzzy rough set theory to pre-process the original data. The experimental result verifies feasibility of our model, without the local minimum and slow convergence speed, compared to the other state-of-the-art approaches. Therefore, our algorithm on the predications of the exact stock price value and future trend are applicable and satisfactory.

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A case study on a hybrid wind speed forecasting method using BP neural network

Cited by 316 publications

References 18 publications

Heuristic Methods for Reservoir Monthly Inflow Forecasting: A Case Study of Xinfengjiang Reservoir in Pearl River, China

Heuristic Methods for Reservoir Monthly Inflow Forecasting: A Case Study of Xinfengjiang Reservoir in Pearl River, China

Hybrid Forecasting Approach Based on GRNN Neural Network and SVR Machine for Electricity Demand Forecasting

A Novel Data Mining Algorithm based on BP Neural Network and Its Applications on Stock Price Prediction

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