A Comparative Analysis of the ARIMA and LSTM Predictive Models and Their Effectiveness for Predicting Wind Speed

Elsaraiti, Meftah; Merabet, Adel

doi:10.3390/en14206782

Cited by 86 publications

(42 citation statements)

References 23 publications

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“…We estimated the hourly demand for five communities and 1164 square grids. Existing studies have demonstrated that the LSTM used in this study has excellent predictive power when forecasting time series data [53][54][55][56]. LSTM and historical analysis (HA) were used to predict hourly demand.…”

Section: Prediction Of Demandmentioning

confidence: 99%

Predicting Demand for Shared E-Scooter Using Community Structure and Deep Learning Method

et al. 2022

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The shared e-scooter is a popular and user-convenient mode of transportation, owing to the free-floating manner of its service. The free-floating service has the advantage of offering pick-up and drop-off anywhere, but has the disadvantage of being unavailable at the desired time and place because it is spread across the service area. To improve the level of service, relocation strategies for shared e-scooters are needed, and it is important to predict the demand for their use within a given area. Therefore, this study aimed to develop a demand prediction model for the use of shared e-scooters. The temporal scope was selected as October 2020, when the demand for e-scooter use was the highest in 2020, and the spatial scope was selected as Seocho and Gangnam, where shared e-scooter services were first introduced and most frequently used in Seoul, Korea. The spatial unit for the analysis was set as a 200 m square grid, and the hourly demand for each grid was aggregated based on e-scooter trip data. Prior to predicting the demand, the spatial area was clustered into five communities using the community structure method. The demand prediction model was developed based on long short-term memory (LSTM) and the prediction results according to the activation function were compared. As a result, the model employing the exponential linear unit (ELU) and the hyperbolic tangent (tanh) as the activation function produced good predictions regarding peak time demands and off-peak demands, respectively. This study presents a methodology for the efficient analysis of the wider spatial area of e-scooters.

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Section: Prediction Of Demandmentioning

confidence: 99%

Predicting Demand for Shared E-Scooter Using Community Structure and Deep Learning Method

et al. 2022

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“…[6] experimental results show that the ANN model does a better job than the ARIMA model. Meanwhile, [7] research shows that the LSTM method is more accurate than ARIMA, but without showing the results of ANN and RNN.…”

Section: Introductionmentioning

confidence: 96%

“…Today wind speed forecasting has become one of the most interesting topics in the field of renewable energy, because it can produce clean energy, and its capacity can be integrated into the grid. [6] and [7] research compared the autoregressive integrated moving average (ARIMA) model, with artificial neural networks (ANN), RNN and LSTM, to estimate wind speed in the future. The model is applied to wind speed data for each month.…”

Section: Introductionmentioning

confidence: 99%

“…Based on these studies, in this study, the effect of using the number of factors and data on the weather prediction process using the Artificial Neural Network classification method will be studied. This method was chosen because based on [6], [7] research, ANN is a recommended method for studies related to wind speed and rainfall and produces a high level of accuracy [7], [8]. In this study, there is an additional weather factor, namely rainfall in the previous month.…”

Section: Introductionmentioning

confidence: 99%

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The Effect of Number of Factors and Data on Monthly Weather Classification Performance Using Artificial Neural Networks

Shofura

Suryani

Salma

et al. 2021

ijoict

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Current weather-related research only focuses on weather prediction based on raw data and the factors used are generally 4 factors: average temperature, solar radiation, air pressure, and wind. In this research, monthly weather prediction is done using 5 factors where the additional factor used is rainfall in the previous time. In contrast to previous prediction research, the prediction process carried out in this study emphasizes the modeling of training data according to the desired prediction model.. These two things distinguish this research from previous studies. The prediction model used in this study is a classification-based prediction model that is the Artificial Neural Network (ANN) method combined with the backpropagation algorithm for calculating the weight of the ANN network. The data used are meteorological data from 2010 to 2018 in the Bogor area, where data from 2010 to 2016 are used as training data, and data from 2017 to 2018 are used as test data. The results of this study indicate that the design of the model with the use of data for 6 years with feature data of 5 factors has an accuracy rate of 83.33%.

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“…Statistical models have better prediction results for the linear part of the wind speed series. However, the actual wind speed series usually exhibit prominent nonlinear and non-smooth properties, when statistical models may obtain undesirable prediction results [10].…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Predictive Model for Ultra-Short-Term Wind Speed Prediction

et al. 2022

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A novel hybrid model is proposed to improve the accuracy of ultra-short-term wind speed prediction by combining the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), the sample entropy (SE), optimized recurrent broad learning system (ORBLS), and broadened temporal convolutional network (BTCN). First, ICEEMDAN is introduced to smooth the nonlinear part of the wind speed data by decomposing the raw wind speed data into a series of sequences. Second, SE is applied to quantitatively assess the complexity of each sequence. All sequences are divided into simple sequence set and complex sequence set based on the values of SE. Third, based on the typical broad learning system (BLS), we propose ORBLS with cyclically connected enhancement nodes, which can better capture the dynamic characteristics of the wind. The improved particle swarm optimization (PSO) is used to optimize the hyper-parameters of ORBLS. Fourth, we propose BTCN by adding a dilated causal convolution layer in parallel to each residual block, which can effectively alleviate the local information loss of the temporal convolutional network (TCN) in case of insufficient time series data. Note that ORBLS and BTCN can effectively predict the simple and complex sequences, respectively. To validate the performance of the proposed model, we conducted three predictive experiments on four data sets. The experimental results show that our model obtains the best predictive results on all evaluation metrics, which fully demonstrates the accuracy and robustness of the proposed model.

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A Comparative Analysis of the ARIMA and LSTM Predictive Models and Their Effectiveness for Predicting Wind Speed

Cited by 86 publications

References 23 publications

Predicting Demand for Shared E-Scooter Using Community Structure and Deep Learning Method

Predicting Demand for Shared E-Scooter Using Community Structure and Deep Learning Method

The Effect of Number of Factors and Data on Monthly Weather Classification Performance Using Artificial Neural Networks

A Novel Hybrid Predictive Model for Ultra-Short-Term Wind Speed Prediction

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