Hybrid forecasting system based on data area division and deep learning neural network for short-term wind speed forecasting

Liu, Zhuoyi; Hara, Ryoichi; Kita, Hidetoshi

doi:10.1016/j.enconman.2021.114136

Cited by 71 publications

(22 citation statements)

References 32 publications

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“…For example, the divide-and-conquer approach has been used in several fields: oil prices ( Rădulescu et al, 2020 ; Wang et al, 2018 ) foreign currency exchange rate ( Jin et al, 2021 ; Lin, Chiu & Lin, 2012 ; Wang & Luo, 2021 ), stock market trend ( Cheng & Wei, 2014 ; Na & Kim, 2021 ; Stasiak, 2020 ; Wang & Luo, 2021 ), wind speed ( Hu et al, 2021 ; Wang et al, 2014 ; Xie et al, 2021 ), electronics sales ( Chen & Lu, 2021 ; Lu & Shao, 2012 ), healthcare ( Aileni, Rodica & Valderrama, 2016 ; Dwivedi et al, 2019 ; Singh, Dwivedi & Srivastava, 2020 ), and tourism market ( Chen, Lai & Yeh, 2012 ; Guerra-Montenegro et al, 2021 ; Tang et al, 2021 ). The hybrid EMD combined with the artificial neural network(ANN) method was applied to predict the first, second, and third steps moving forward wind speed time series ( Chen et al, 2021 ; Hu et al, 2021 ; Liu et al, 2012 ; Liu, Hara & Kita, 2021 ). Several predicting powers from low to high frequency and short-term to long-term trend elements were observed for analysis of the accuracy of EMD forecasting combined with ANN in the Baltic Exchange Dry Index ( Gavriilidis et al, 2021 ; Zeng & Qu, 2014 ).…”

Section: Literature Reviewmentioning

confidence: 99%

Empirical mode decomposition using deep learning model for financial market forecasting

Jin

Jin²,

Chen

2022

PeerJ Computer Science

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Financial market forecasting is an essential component of financial systems; however, predicting financial market trends is a challenging job due to noisy and non-stationary information. Deep learning is renowned for bringing out excellent abstract features from the huge volume of raw data without depending on prior knowledge, which is potentially fascinating in forecasting financial transactions. This article aims to propose a deep learning model that autonomously mines the statistical rules of data and guides the financial market transactions based on empirical mode decomposition (EMD) with back-propagation neural networks (BPNN). Through the characteristic time scale of data, the intrinsic wave pattern was obtained and then decomposed. Financial market transaction data were analyzed, optimized using PSO, and predicted. Combining the nonlinear and non-stationary financial time series can improve prediction accuracy. The predictive model of deep learning, based on the analysis of the massive financial trading data, can forecast the future trend of financial market price, forming a trading signal when particular confidence is satisfied. The empirical results show that the EMD-based deep learning model has an excellent predicting performance.

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Section: Literature Reviewmentioning

confidence: 99%

Empirical mode decomposition using deep learning model for financial market forecasting

Jin

Jin²,

Chen

2022

PeerJ Computer Science

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“…This is confirmed from the findings of various studies that have been done previously. As an example: Monica, Melin and Sachez who exposed that optimization is an imperative process on the ensemble of neural network architectures they developed for prediction of COVID-19 confirmed and death cases [31]; Liu, Hara and Kita who in their study have found that optimized deep learning networks is giving higher accuracy when predicting wind speed [32]; and study by Vadicharla and Sharma that explores various optimization approach which also found that ensemble of models needs to be finely tuned in order to give the best result [33].…”

Section: -4-genetic Algorithm Optimized Ensemblementioning

confidence: 99%

GA-Optimized Multivariate CNN-LSTM Model for Predicting Multi-channel Mobility in the COVID-19 Pandemic

Widiputra

2021

Emerg Sci J

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The primary factor that contributes to the transmission of COVID-19 infection is human mobility. Positive instances added on a daily basis have a substantial positive association with the pace of human mobility, and the reverse is true. Thus, having the ability to predict human mobility trend during a pandemic is critical for policymakers to help in decreasing the rate of transmission in the future. In this regard, one approach that is commonly used for time-series data prediction is to build an ensemble with the aim of getting the best performance. However, building an ensemble often causes the performance of the model to decrease, due to the increasing number of parameters that are not being optimized properly. Consequently, the purpose of this study is to develop and evaluate a deep learning ensemble model, which is optimized using a genetic algorithm (GA) that incorporates a convolutional neural network (CNN) and a long short-term memory (LSTM). A CNN is used to conduct feature extraction from mobility time-series data, while an LSTM is used to do mobility prediction. The parameters of both layers are adjusted using GA. As a result of the experiments conducted with data from the Google Community Mobility Reports in Indonesia that ranges from the beginning of February 2020 to the end of December 2020, the GA-Optimized Multivariate CNN-LSTM ensemble outperforms stand-alone CNN and LSTM models, as well as the non-optimized CNN-LSTM model, in terms of predicting human movement in the future. This may be useful in assisting policymakers in anticipating future human mobility trends. Doi: 10.28991/esj-2021-01300 Full Text: PDF

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“…On the other hand, considering the benefits and drawbacks of ML and DL algorithms, various studies were employed either a hybrid method or an ensemble method to execute more reliable and accurate forecasting outcomes [20][21][22][23]. The incorporation of an artificial neural network (ANN), a BPNN, a generalized regression neural network (GRNN), an Elman neural network, and a genetic algorithm optimized backpropagation neural network (GABPNN) was proposed for half-hourly electrical power prediction by Xiao et al [24].…”

Section: Related Workmentioning

confidence: 99%

Hybrid Ensemble Deep Learning-Based Approach for Time Series Energy Prediction

Phyo

Byun

2021

Symmetry

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The energy manufacturers are required to produce an accurate amount of energy by meeting the energy requirements at the end-user side. Consequently, energy prediction becomes an essential role in the electric industrial zone. In this paper, we propose the hybrid ensemble deep learning model, which combines multilayer perceptron (MLP), convolutional neural network (CNN), long short-term memory (LSTM), and hybrid CNN-LSTM to improve the forecasting performance. These DL architectures are more popular and better than other machine learning (ML) models for time series electrical load prediction. Therefore, hourly-based energy data are collected from Jeju Island, South Korea, and applied for forecasting. We considered external features associated with meteorological conditions affecting energy. Two-year training and one-year testing data are preprocessed and arranged to reform the times series, which are then trained in each DL model. The forecasting results of the proposed ensemble model are evaluated by using mean square error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Error metrics are compared with DL stand-alone models such as MLP, CNN, LSTM, and CNN-LSTM. Our ensemble model provides better performance than other forecasting models, providing minimum MAPE at 0.75%, and was proven to be inherently symmetric for forecasting time-series energy and demand data, which is of utmost concern to the power system sector.

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Hybrid forecasting system based on data area division and deep learning neural network for short-term wind speed forecasting

Cited by 71 publications

References 32 publications

Empirical mode decomposition using deep learning model for financial market forecasting

Empirical mode decomposition using deep learning model for financial market forecasting

GA-Optimized Multivariate CNN-LSTM Model for Predicting Multi-channel Mobility in the COVID-19 Pandemic

Hybrid Ensemble Deep Learning-Based Approach for Time Series Energy Prediction

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