scholarly journals Recurrent Neural Networks Based Photovoltaic Power Forecasting Approach

Energies ◽  
2019 ◽  
Vol 12 (13) ◽  
pp. 2538 ◽  
Author(s):  
Li ◽  
Wang ◽  
Zhang ◽  
Xin ◽  
Liu
Keyword(s):  
Neural Network ◽  
Solar Power ◽  
Time Series Data ◽  
Short Term Memory ◽  
Rbf Neural Network ◽  
Back Propagation ◽  
Series Data ◽  
Support Vector ◽  
Short Term ◽  
Forecasting Method

The intermittency of solar energy resources has brought a big challenge for the optimization and planning of a future smart grid. To reduce the intermittency, an accurate prediction of photovoltaic (PV) power generation is very important. Therefore, this paper proposes a new forecasting method based on the recurrent neural network (RNN). At first, the entire solar power time series data is divided into inter-day data and intra-day data. Then, we apply RNN to discover the nonlinear features and invariant structures exhibited in the adjacent days and intra-day data. After that, a new point prediction model is proposed, only by taking the previous PV power data as input without weather information. The forecasting horizons are set from 15 to 90 minutes. The proposed forecasting method is tested by using real solar power in Flanders, Belgium. The classical persistence method (Persistence), back propagation neural network (BPNN), radial basis function (RBF) neural network and support vector machine (SVM), and long short-term memory (LSTM) networks are adopted as benchmarks. Extensive results show that the proposed forecasting method exhibits a good forecasting quality on very short-term forecasting, which demonstrates the feasibility and effectiveness of the proposed forecasting model.

scholarly journals Long Short Term Memory Recurrent Network for Standard and Poor’s 500 Index Modelling

2018 ◽  
Vol 7 (4.15) ◽  
pp. 25 ◽  
Author(s):  
Said Jadid Abdulkadir ◽  
Hitham Alhussian ◽  
Muhammad Nazmi ◽  
Asim A Elsheikh
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Recurrent Neural Network ◽  
Time Series Data ◽  
Short Term Memory ◽  
Series Data ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Forecasting time-series data are imperative especially when planning is required through modelling using uncertain knowledge of future events. Recurrent neural network models have been applied in the industry and outperform standard artificial neural networks in forecasting, but fail in long term time-series forecasting due to the vanishing gradient problem. This study offers a robust solution that can be implemented for long-term forecasting using a special architecture of recurrent neural network known as Long Short Term Memory (LSTM) model to overcome the vanishing gradient problem. LSTM is specially designed to avoid the long-term dependency problem as their default behavior. Empirical analysis is performed using quantitative forecasting metrics and comparative model performance on the forecasted outputs. An evaluation analysis is performed to validate that the LSTM model provides better forecasted outputs on Standard & Poor’s 500 Index (S&P 500) in terms of error metrics as compared to other forecasting models.  

scholarly journals Prediction of Rainfall Using Intensified LSTM Based Recurrent Neural Network with Weighted Linear Units

Atmosphere ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 668 ◽  
Author(s):  
S. Poornima ◽  
M. Pushpalatha
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Time Series Data ◽  
Short Term Memory ◽  
Moving Average ◽  
Series Data ◽  
Short Term ◽  
Term Memory ◽  
Rainfall Prediction ◽  
Long Short Term Memory

Prediction of rainfall is one of the major concerns in the domain of meteorology. Several techniques have been formerly proposed to predict rainfall based on statistical analysis, machine learning and deep learning techniques. Prediction of time series data in meteorology can assist in decision-making processes carried out by organizations responsible for the prevention of disasters. This paper presents Intensified Long Short-Term Memory (Intensified LSTM) based Recurrent Neural Network (RNN) to predict rainfall. The neural network is trained and tested using a standard dataset of rainfall. The trained network will produce predicted attribute of rainfall. The parameters considered for the evaluation of the performance and the efficiency of the proposed rainfall prediction model are Root Mean Square Error (RMSE), accuracy, number of epochs, loss, and learning rate of the network. The results obtained are compared with Holt–Winters, Extreme Learning Machine (ELM), Autoregressive Integrated Moving Average (ARIMA), Recurrent Neural Network and Long Short-Term Memory models in order to exemplify the improvement in the ability to predict rainfall.

scholarly journals Short-term power prediction of photovoltaic power station based on long short-term memory-back-propagation

2019 ◽  
Vol 15 (10) ◽  
pp. 155014771988313 ◽  
Author(s):  
Chi Hua ◽  
Erxi Zhu ◽  
Liang Kuang ◽  
Dechang Pi
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Short Term ◽  
Data Set ◽  
Term Memory ◽  
Photovoltaic Power ◽  
Forecasting Method ◽  
Long Short Term Memory

Accurate prediction of the generation capacity of photovoltaic systems is fundamental to ensuring the stability of the grid and to performing scheduling arrangements correctly. In view of the temporal defect and the local minimum problem of back-propagation neural network, a forecasting method of power generation based on long short-term memory-back-propagation is proposed. On this basis, the traditional prediction data set is improved. According to the three traditional methods listed in this article, we propose a fourth method to improve the traditional photovoltaic power station short-term power generation prediction. Compared with the traditional method, the long short-term memory-back-propagation neural network based on the improved data set has a lower prediction error. At the same time, a horizontal comparison with the multiple linear regression and the support vector machine shows that the long short-term memory-back-propagation method has several advantages. Based on the long short-term memory-back-propagation neural network, the short-term forecasting method proposed in this article for generating capacity of photovoltaic power stations will provide a basis for dispatching plan and optimizing operation of power grid.

scholarly journals Early-Stage Gas Identification Using Convolutional Long Short-Term Neural Network with Sensor Array Time Series Data

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4826
Author(s):  
Kai Zhou ◽  
Yixin Liu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Sensor Array ◽  
Time Series Data ◽  
Practical Significance ◽  
Series Data ◽  
Support Vector ◽  
Identification Performance ◽  
Short Term ◽  
Gas Identification

Gas identification/classification through pattern recognition techniques based on gas sensor arrays often requires the equilibrium responses or the full traces of time-series data of the sensor array. Leveraging upon the diverse gas sensing kinetics behaviors measured via the sensor array, a computational intelligence- based meta-model is proposed to automatically conduct the feature extraction and subsequent gas identification using time-series data during the transitional phase before reaching equilibrium. The time-series data contains implicit temporal dependency/correlation that is worth being characterized to enhance the gas identification performance and reliability. In this context, a tailored approach so-called convolutional long short-term memory (CLSTM) neural network is developed to perform the identification task incorporating temporal characteristics within time-series data. This novel approach shows the enhanced accuracy and robustness as compared to the baseline models, i.e., multilayer perceptron (MLP) and support vector machine (SVM) through the comprehensive statistical examination. Specifically, the classification accuracy of CLSTM reaches as high as 96%, regardless of the operating condition specified. More importantly, the excellent gas identification performance of CLSTM at early stages of gas exposure indicates its practical significance in future real-time applications. The promise of the proposed method has been clearly illustrated through both the internal and external validations in the systematic case investigation.

scholarly journals Airport Arrival Flow Prediction considering Meteorological Factors Based on Deep-Learning Methods

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Zhao Yang ◽  
Yifan Wang ◽  
Jie Li ◽  
Liming Liu ◽  
Jiyang Ma ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series Data ◽  
Short Term Memory ◽  
Series Data ◽  
Gradient Boosting ◽  
Support Vector ◽  
Model Parameters ◽  
Neural Network Models ◽  
Flow Prediction ◽  
Extreme Gradient Boosting

This study presents a combined Long Short-Term Memory and Extreme Gradient Boosting (LSTM-XGBoost) method for flight arrival flow prediction at the airport. Correlation analysis is conducted between the historic arrival flow and input features. The XGBoost method is applied to identify the relative importance of various variables. The historic time-series data of airport arrival flow and selected features are taken as input variables, and the subsequent flight arrival flow is the output variable. The model parameters are sequentially updated based on the recently collected data and the new predicting results. It is found that the prediction accuracy is greatly improved by incorporating the meteorological features. The data analysis results indicate that the developed method can characterize well the dynamics of the airport arrival flow, thereby providing satisfactory prediction results. The prediction performance is compared with benchmark methods including backpropagation neural network, LSTM neural network, support vector machine, gradient boosting regression tree, and XGBoost. The results show that the proposed LSTM-XGBoost model outperforms baseline and state-of-the-art neural network models.

scholarly journals LSTM-Based Forecasting for Urban Construction Waste Generation

2020 ◽  
Vol 12 (20) ◽  
pp. 8555
Author(s):  
Li Huang ◽  
Ting Cai ◽  
Ya Zhu ◽  
Yuliang Zhu ◽  
Wei Wang ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Short Term Memory ◽  
Structure Design ◽  
Time Series Forecasting ◽  
Series Data ◽  
Support Vector ◽  
Waste Generation ◽  
Short Term ◽  
Construction Waste

Accurate forecasts of construction waste are important for recycling the waste and formulating relevant governmental policies. Deficiencies in reliable forecasting methods and historical data hinder the prediction of this waste in long- or short-term planning. To effectively forecast construction waste, a time-series forecasting method is proposed in this study, based on a three-layer long short-term memory (LSTM) network and univariate time-series data with limited sample points. This method involves network structure design and implementation algorithms for network training and the forecasting process. Numerical experiments were performed with statistical construction waste data for Shanghai and Hong Kong. Compared with other time-series forecasting models such as ridge regression (RR), support vector regression (SVR), and back-propagation neural networks (BPNN), this paper demonstrates that the proposed LSTM-based forecasting model is effective and accurate in predicting construction waste generation.

scholarly journals A LSTM-RNN-Assisted Vector Tracking Loop for Signal Outage Bridging

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Di Liu ◽  
Qingyuan Xia ◽  
Changhui Jiang ◽  
Chaochen Wang ◽  
Yuming Bo
Keyword(s):  
Neural Network ◽  
Urban Areas ◽  
Time Series Data ◽  
Short Term Memory ◽  
Series Data ◽  
Support Vector ◽  
Tracking Loop ◽  
Navigation Solution ◽  
Positioning Errors ◽  
Vector Tracking

Global Navigation Satellite System (GNSS) has been the most popular tool for providing positioning, navigation, and timing (PNT) information. Some methods have been developed for enhancing the GNSS performance in signal challenging environments (urban canyon, dense foliage, signal blockage, multipath, and none-line-of-sight signals). Vector Tracking Loop (VTL) was recognized as the most promising and prospective one among these technologies, since VTL realized mutual aiding between channels. However, momentary signal blockage from part of the tracking channels affected the VTL operation and the navigation solution estimation. Moreover, insufficient available satellites employed would lead to the navigation solution errors diverging quickly over time. Short-time or temporary signal blockage was common in urban areas. Aiming to improve the VTL performance during the signal outage, in this paper, the deep learning method was employed for assisting the VTL navigation solution estimation; more specifically, a Long Short-Term Memory-Recurrent Neural Network (LSTM-RNN) was employed to aid the VTL navigation filter (navigation filter was usually a Kalman filter). LSTM-RNN obtained excellent performance in time-series data processing; therefore, in this paper, the LSTM-RNN was employed to predict the navigation filter innovative sequence values during the signal outage, and then, the predicted innovative values were employed to aid the navigation filter for navigation solution estimation. The LSTM-RNN was well trained while the signal was normal, and the past innovative sequence was employed as the input of the LSTM-RNN. A simulation was designed and conducted based on an open-source Matlab GNSS software receiver; a dynamic trajectory with several temporary signal outages was designed for testing the proposed method. Compared with the conventional VTL, the LSTM-RNN-assisted VTL could keep the horizontal positioning errors within 50 meters during a signal outage. Also, conventional Support Vector Machine (SVM) and radial basis function neural network (RBF-NN) were compared with the LSTM-RNN method; LSTM-RNN-assisted VTL could maintain the positioning errors less than 20 meters during the outages, which demonstrated LSTM-RNN was superior to the SVM and RBF-NN in these applications.

scholarly journals A Robust Terrain Aided Navigation Using the Rao-Blackwellized Particle Filter Trained by Long Short-Term Memory Networks

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2886 ◽  
Author(s):  
Jungshin Lee ◽  
Hyochoong Bang
Keyword(s):  
Neural Network ◽  
Particle Filter ◽  
Time Series Data ◽  
Short Term Memory ◽  
Measurement Model ◽  
Series Data ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Elevation Model

Terrain-aided navigation (TAN) is a technology that estimates the position of the vehicle by comparing the altitude measured by an altimeter and height from the digital elevation model (DEM). The particle filter (PF)-based TAN has been commonly used to obtain stable real-time navigation solutions in cases where the unmanned aerial vehicle (UAV) operates at a high altitude. Even though TAN performs well on rough and unique terrains, its performance degrades in flat and repetitive terrains. In particular, in the case of PF-based TAN, there has been no verified technique for deciding its terrain validity. Therefore, this study designed a Rao-Blackwellized PF (RBPF)-based TAN, used long short-term memory (LSTM) networks to endure flat and repetitive terrains, and trained the noise covariances and measurement model of RBPF. LSTM is a modified recurrent neural network (RNN), which is an artificial neural network that recognizes patterns from time series data. Using this, this study tuned the noise covariances and measurement model of RBPF to minimize the navigation errors in various flight trajectories. This paper designed a TAN algorithm based on combining RBPF and LSTM and confirmed that it can enable a more precise navigation performance than conventional RBPF based TAN through simulations.

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