Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method

Wang, Shouxiang; Zhang, Na; Wu, Lei; Wang, Yamin

doi:10.1016/j.renene.2016.03.103

Cited by 552 publications

(211 citation statements)

References 16 publications

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“…The forecasting performances and the forecasting errors of all the above models are provided in Figure 5 and 200 400 600 800 1000 1200 1400 -200 0 200 Mode5 200 400 600 800 1000 1200 1400 -200 0 200 Mode6 200 400 600 800 1000 1200 1400 -100 0 100 Mode7 200 400 600 800 1000 1200 1400 -50 0 50 Mode8 200 400 600 800 1000 1200 1400 -50 0 50 Mode9 Time/ Half-hour Then, the forecasting issue turns to the forecasting of each mode using the ELM model improved by the DE algorithm. In this study, the inputs of DE-ELM model are determined using the rolling technique proposed in the reference [36], that is, every eight continuous electric load data points are employed to predict the ninth one, the detailed procedure is provided in Figure 3. The parameter settings of DE algorithm including iteration number (Gen), population size (Pop), mutation control parameter (F) and crossover probability (Cr) are all listed as follows: Gen = 100, Pop = 30, F = 0.9 and Cr = 0.5.…”

Section: Forecasting Results Comparative Analysis and Discussionmentioning

confidence: 99%

“…Then, the forecasting issue turns to the forecasting of each mode using the ELM model improved by the DE algorithm. In this study, the inputs of DE-ELM model are determined using the rolling technique proposed in the reference [36], that is, every eight continuous electric load data points are employed to predict the ninth one, the detailed procedure is provided in Figure 3. The parameter settings of DE algorithm including iteration number ( Gen ), population size ( Pop ), mutation control parameter ( F ) and crossover probability ( Cr ) are all listed as follows:…”

Section: Data Preprocessing Of the Original Electric Load Seriesmentioning

confidence: 99%

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

et al. 2017

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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.

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Section: Forecasting Results Comparative Analysis and Discussionmentioning

confidence: 99%

Section: Data Preprocessing Of the Original Electric Load Seriesmentioning

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

show abstract

“…The selection of the type of neural network for the best performance depends on the data sources [45]; therefore, we need to compare the proposed models with other well-known techniques by using the same data sets to prove their performance effectively and efficiently. The hybrid models can handle non-stationary data well [46]. (3) High forecasting accuracy.…”

Section: Contributionmentioning

confidence: 99%

Comparative Study of Hybrid Models Based on a Series of Optimization Algorithms and Their Application in Energy System Forecasting

Liu

2016

Energies

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Abstract:Big data mining, analysis, and forecasting play vital roles in modern economic and industrial fields, especially in the energy system. Inaccurate forecasting may cause wastes of scarce energy or electricity shortages. However, forecasting in the energy system has proven to be a challenging task due to various unstable factors, such as high fluctuations, autocorrelation and stochastic volatility. To forecast time series data by using hybrid models is a feasible alternative of conventional single forecasting modelling approaches. This paper develops a group of hybrid models to solve the problems above by eliminating the noise in the original data sequence and optimizing the parameters in a back propagation neural network. One of contributions of this paper is to integrate the existing algorithms and models, which jointly show advances over the present state of the art. The results of comparative studies demonstrate that the hybrid models proposed not only satisfactorily approximate the actual value but also can be an effective tool in the planning and dispatching of smart grids.

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“…Yamin Wang et al, 2013 [7] proposes a novel wind speed forecasting method based on ensemble empirical mode decomposition (EEMD) and Genetic algorithmbackpropagation Neural network.…”

Section: Literature Surveymentioning

confidence: 99%

Daily Weather Forecasting using Artificial Neural Network

Narvekar¹,

Fargose²

2015

IJCA

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Daily Weather forecasting is used for multiple reasons in multiple areas like agriculture, energy supply, transportations, etc. Accuracy of weather conditions shown in forecast reports is very necessary. In this paper, the review is conducted to investigate a better approach for forecasting which compares many techniques such as Artificial Neural Network, Ensemble Neural Network, Backpropagation Network, Radial Basis Function Network, General Regression Neural Network, Genetic Algorithm, Multilayer Perceptron, Fuzzy clustering, etc. which are used for different types of forecasting. Among which neural network with the backpropagation algorithm performs prediction with minimal error. Neural network is a complex network which is self-adaptive in nature. It learns by itself using the training data and generates some intelligent patterns which are useful for forecasting the weather. This paper reviews various techniques and focuses mainly on neural network with back propagation technique for daily weather forecasting. The technique uses 28 input parameters to forecast the daily weather in terms of temperature, rainfall, humidity, cloud condition, and weather of the day.

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Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method

Cited by 552 publications

References 16 publications

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

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

Comparative Study of Hybrid Models Based on a Series of Optimization Algorithms and Their Application in Energy System Forecasting

Daily Weather Forecasting using Artificial Neural Network

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