scholarly journals Assessment of LSTM, Conv2D and ConvLSTM2D Prediction Models for Long-Term Wind Speed and Direction Regression Analysis

2021 ◽  
Author(s):  
Zaccheus Olaofe
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Wind Speed ◽  
Prediction Models ◽  
Short Term Memory ◽  
Model Performance ◽  
Input Sequence ◽  
Forecast Models ◽  
Sequential Models ◽  
High Level

Abstract This paper assessed the model performance accuracies of 3 forecast-based architectures (Long Short-Term Memory, LSTM; Convolutional Neural Network, Conv2D and hybrid ConvLSTM2D) for multivariate inputs to multi-steps wind speed and direction forecasts. These high-level neural network-based architectures were setup with the Keras sequential models trained to learn the historical patterns from the processed weather input datasets. To build these forecast models, the sampled time series weather observations at different station heights were obtained and reshaped for network layer compatibility, while the Adamax algorithm for the network optimization was considered. The trained and evaluated model performances with different input data sequences (normalized/un-normalized) were assessed while the forecast results were also compared with the Actual and Conv1D models. Upon optimal network training, the Conv2D model returned MSE, MAE and RMSE estimated values of 0.82, 4.48 and 0.91 %, respectively; the LSTM model returned 1.03, 4.75 and 1.01 %; while the ConvLSTM2D model returned 2.11, 10.13 and 1.45 %, respectively. Also, Conv2D validated model values of 3.16, 14.73 and 1.77 % were obtained %, respectively; 3.21, 14.98 and 1.82, for the LSTM-based; while ConvLSTM2D model returned 3.27, 15.92 and 1.91 %, respectively. Studied finding results show that better prediction and evaluation could be achieved for all the trained model architectures as compared to the untrained models. Also, from the predicted model results, the Keras sequential models were found to be useful for replicating the time-series historical wind speed and direction based on the well-tuned model hyperparameters as well as the input sequence structure

scholarly journals Research on Safety Prediction of Sector Traffic Operation Based on a Long Short Term Memory Model

2021 ◽  
Vol 11 (11) ◽  
pp. 5141
Author(s):  
Wenying Lyu ◽  
Honghai Zhang ◽  
Junqiang Wan ◽  
Lei Yang
Keyword(s):  
Time Series ◽  
Traffic Safety ◽  
Prediction Models ◽  
Short Term Memory ◽  
Model Performance ◽  
Risk Identification ◽  
Civil Aviation ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Traffic safety has been thought of as a basic feature of transportation, recent developments in civil aviation have emphasized the need for risk identification and safety prediction. This study aims to increase en-route flight safety through the development of prediction models for flight conflicts. Firstly, flight conflicts time series and traffic parameters are extracted from historical ADS-B data. In the second step, a Long Short-Term Memory (LSTM) model is trained to make a one-step-ahead prediction on the flight conflict time series. The results show that the LSTM model has the greatest prediction effect (MAE 0.3901) with comparison to other models. Based on that, we add traffic parameters (volume, density, velocity) into the LSTM model as new input variables and issue a comprehensive analysis of the relative predictive power of traffic parameters. The accuracy of prediction model is validated with a mean error of less than 3%. Based on the improvements of model performance brought by traffic parameters, LSTM models with a single traffic parameter are proposed for further discussion. The results illustrate that volume is the most important factor in promoting prediction accuracy and density has an advantage of improvement in the aspect of model stability.

scholarly journals Ultra-Short-Term Photovoltaic Power Prediction Model Based on the Localized Emotion Reconstruction Emotional Neural Network

Energies ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 2857 ◽  
Author(s):  
Yufei Wang ◽  
Li Zhu ◽  
Hua Xue
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Prediction Model ◽  
Prediction Models ◽  
Short Term Memory ◽  
Single Step ◽  
Power Prediction ◽  
Short Term ◽  
Model Based ◽  
Emotional Neural Network

Due to the intermittency and randomness of photovoltaic (PV) power, the PV power prediction accuracy of the traditional data-driven prediction models is difficult to improve. A prediction model based on the localized emotion reconstruction emotional neural network (LERENN) is proposed, which is motivated by chaos theory and the neuropsychological theory of emotion. Firstly, the chaotic nonlinear dynamics approach is used to draw the hidden characteristics of PV power time series, and the single-step cyclic rolling localized prediction mechanism is derived. Secondly, in order to establish the correlation between the prediction model and the specific characteristics of PV power time series, the extended signal and emotional parameters are reconstructed with a relatively certain local basis. Finally, the proposed prediction model is trained and tested for single-step and three-step prediction using the actual measured data. Compared with the prediction model based on the long short-term memory (LSTM) neural network, limbic-based artificial emotional neural network (LiAENN), the back propagation neural network (BPNN), and the persistence model (PM), numerical results show that the proposed prediction model achieves better accuracy and better detection of ramp events for different weather conditions when only using PV power data.

scholarly journals Multi-Step Short-Term Wind Speed Prediction Using a Residual Dilated Causal Convolutional Network with Nonlinear Attention

Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1772 ◽  
Author(s):  
Kumar Shivam ◽  
Jong-Chyuan Tzou ◽  
Shang-Chen Wu
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Convolutional Neural Network ◽  
Real World ◽  
Prediction Models ◽  
Short Term Memory ◽  
Short Term ◽  
Wind Speed Prediction ◽  
Proposed Model ◽  
Speed Prediction

Wind energy is the most used renewable energy worldwide second only to hydropower. However, the stochastic nature of wind speed makes it harder for wind farms to manage the future power production and maintenance schedules efficiently. Many wind speed prediction models exist that focus on advance neural networks and/or preprocessing techniques to improve the accuracy. Since most of these models require a large amount of historic wind data and are validated using the data split method, the application to real-world scenarios cannot be determined. In this paper, we present a multi-step univariate prediction model for wind speed data inspired by the residual U-net architecture of the convolutional neural network (CNN). We propose a residual dilated causal convolutional neural network (Res-DCCNN) with nonlinear attention for multi-step-ahead wind speed forecasting. Our model can outperform long-term short-term memory networks (LSTM), gated recurrent units (GRU), and Res-DCCNN using sliding window validation techniques for 50-step-ahead wind speed prediction. We tested the performance of the proposed model on six real-world wind speed datasets with different probability distributions to confirm its effectiveness, and using several error metrics, we demonstrated that our proposed model was robust, precise, and applicable to real-world cases.

scholarly journals An Attention-Based Multilayer GRU Model for Multistep-Ahead Short-Term Load Forecasting

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1639
Author(s):  
Seungmin Jung ◽  
Jihoon Moon ◽  
Sungwoo Park ◽  
Eenjun Hwang
Keyword(s):  
Power Consumption ◽  
Prediction Models ◽  
Short Term Memory ◽  
Load Forecasting ◽  
Input Sequence ◽  
Short Term ◽  
Performance Improvements ◽  
Short Term Load Forecasting ◽  
Significant Performance ◽  
Input Variables

Recently, multistep-ahead prediction has attracted much attention in electric load forecasting because it can deal with sudden changes in power consumption caused by various events such as fire and heat wave for a day from the present time. On the other hand, recurrent neural networks (RNNs), including long short-term memory and gated recurrent unit (GRU) networks, can reflect the previous point well to predict the current point. Due to this property, they have been widely used for multistep-ahead prediction. The GRU model is simple and easy to implement; however, its prediction performance is limited because it considers all input variables equally. In this paper, we propose a short-term load forecasting model using an attention based GRU to focus more on the crucial variables and demonstrate that this can achieve significant performance improvements, especially when the input sequence of RNN is long. Through extensive experiments, we show that the proposed model outperforms other recent multistep-ahead prediction models in the building-level power consumption forecasting.

Prediction of InSAR deformation time-series using a long short-term memory neural network

2021 ◽  
Vol 42 (18) ◽  
pp. 6921-6944
Author(s):  
Yi Chen ◽  
Yi He ◽  
Lifeng Zhang ◽  
Youdong Chen ◽  
Hongyu Pu ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Forecasting the risk at infractions: an ensemble comparison of machine learning approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lei Li ◽  
Desheng Wu
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Short Term Memory ◽  
Model Performance ◽  
Large Data ◽  
Support Vector ◽  
Learning Approaches ◽  
Content Type ◽  
Day To Day Operations ◽  
Prediction Approach

PurposeThe infraction of securities regulations (ISRs) of listed firms in their day-to-day operations and management has become one of common problems. This paper proposed several machine learning approaches to forecast the risk at infractions of listed corporates to solve financial problems that are not effective and precise in supervision.Design/methodology/approachThe overall proposed research framework designed for forecasting the infractions (ISRs) include data collection and cleaning, feature engineering, data split, prediction approach application and model performance evaluation. We select Logistic Regression, Naïve Bayes, Random Forest, Support Vector Machines, Artificial Neural Network and Long Short-Term Memory Networks (LSTMs) as ISRs prediction models.FindingsThe research results show that prediction performance of proposed models with the prior infractions provides a significant improvement of the ISRs than those without prior, especially for large sample set. The results also indicate when judging whether a company has infractions, we should pay attention to novel artificial intelligence methods, previous infractions of the company, and large data sets.Originality/valueThe findings could be utilized to address the problems of identifying listed corporates' ISRs at hand to a certain degree. Overall, results elucidate the value of the prior infraction of securities regulations (ISRs). This shows the importance of including more data sources when constructing distress models and not only focus on building increasingly more complex models on the same data. This is also beneficial to the regulatory authorities.

Multi-step wind speed forecasting based on hybrid multi-stage decomposition model and long short-term memory neural network

2020 ◽  
Vol 213 ◽  
pp. 112869 ◽  
Author(s):  
Sinvaldo Rodrigues Moreno ◽  
Ramon Gomes da Silva ◽  
Viviana Cocco Mariani ◽  
Leandro dos Santos Coelho
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Wind Speed Forecasting ◽  
Decomposition Model ◽  
Multi Stage ◽  
Long Short Term Memory

scholarly journals Learners Demographics Classification on MOOCs During the COVID-19: Author Profiling via Deep Learning Based on Semantic and Syntactic Representations

2021 ◽  
Vol 6 ◽  
Author(s):  
Tahani Aljohani ◽  
Alexandra I. Cristea
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Prediction Models ◽  
Short Term Memory ◽  
Methodological Approach ◽  
High Accuracy ◽  
Directional Model ◽  
Textual Representations ◽  
The One

Massive Open Online Courses (MOOCs) have become universal learning resources, and the COVID-19 pandemic is rendering these platforms even more necessary. In this paper, we seek to improve Learner Profiling (LP), i.e. estimating the demographic characteristics of learners in MOOC platforms. We have focused on examining models which show promise elsewhere, but were never examined in the LP area (deep learning models) based on effective textual representations. As LP characteristics, we predict here the employment status of learners. We compare sequential and parallel ensemble deep learning architectures based on Convolutional Neural Networks and Recurrent Neural Networks, obtaining an average high accuracy of 96.3% for our best method. Next, we predict the gender of learners based on syntactic knowledge from the text. We compare different tree-structured Long-Short-Term Memory models (as state-of-the-art candidates) and provide our novel version of a Bi-directional composition function for existing architectures. In addition, we evaluate 18 different combinations of word-level encoding and sentence-level encoding functions. Based on these results, we show that our Bi-directional model outperforms all other models and the highest accuracy result among our models is the one based on the combination of FeedForward Neural Network and the Stack-augmented Parser-Interpreter Neural Network (82.60% prediction accuracy). We argue that our prediction models recommended for both demographics characteristics examined in this study can achieve high accuracy. This is additionally also the first time a sound methodological approach toward improving accuracy for learner demographics classification on MOOCs was proposed.

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