scholarly journals High Accuracy Modeling for Solar PV Power Generation Using Noble BD-LSTM-Based Neural Networks with EMA

2020 ◽  
Vol 10 (20) ◽  
pp. 7339 ◽  
Author(s):  
Youngil Kim ◽  
Keunjoo Seo ◽  
Robert J. Harrington ◽  
Yongju Lee ◽  
Hyeok Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Power Generation ◽  
Prediction Model ◽  
Time Series Data ◽  
Weather Condition ◽  
Series Data ◽  
Accurate Estimation ◽  
Solar Pv ◽  
Ann Model ◽  
Pv Power Generation

More accurate self-forecasting not only provides a better-integrated solution for electricity grids but also reduces the cost of operation of the entire power system. To predict solar photovoltaic (PV) power generation (SPVG) for a specific hour, this paper proposes the combination of a two-step neural network bi directional long short-term memory (BD-LSTM) model with an artificial neural network (ANN) model using exponential moving average (EMA) preprocessing. In this study, four types of historical input data are used: hourly PV generation for one week (168 h) ahead, hourly horizontal radiation, hourly ambient temperature, and hourly device (surface) temperature, downloaded from the Korea Open Data Portal. The first strategy is employed using the LSTM prediction model, which forecasts the SPVG of the desired time through the data from the previous week, which is preprocessed to smooth the dynamic SPVG using the EMA approach. The SPVG was predicted using the LSTM model according to the trend of the previous time-series data. However, slight errors still occur because the weather condition of the time is not reflected at the desired time. Therefore, we proposed a second strategy of an ANN model for more accurate estimation to compensate for this slight error using the four inputs predicted by the LSTM model. As a result, the LSTM prediction model with the ANN estimation model using EMA preprocessing exhibited higher accuracy in performance than other options for SPVG.

scholarly journals Ultra-Short-Term Forecasting of Photo-Voltaic Power via RBF Neural Network

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1717
Author(s):  
Wanxing Ma ◽  
Zhimin Chen ◽  
Qing Zhu
Keyword(s):  
Neural Network ◽  
Power Generation ◽  
Smart Grids ◽  
Percentage Error ◽  
Solar Pv ◽  
Short Term ◽  
Forecasting Method ◽  
Photo Voltaic ◽  
Short Term Forecasting ◽  
Pv Power Generation

With the fast expansion of renewable energy systems during recent years, the stability and quality of smart grids using solar energy have been challenged because of the intermittency and fluctuations. Hence, forecasting photo-voltaic (PV) power generation is essential in facilitating planning and managing electricity generation and distribution. In this paper, the ultra-short-term forecasting method for solar PV power generation is investigated. Subsequently, we proposed a radial basis function (RBF)-based neural network. Additionally, to improve the network generalization ability and reduce the training time, the numbers of hidden layer neurons are limited. The input of neural network is selected as the one with higher Spearman correlation among the predicted power features. The data are normalized and the expansion parameter of RBF neurons are adjusted continuously in order to reduce the calculation errors and improve the forecasting accuracy. Numerous simulations are carried out to evaluate the performance of the proposed forecasting method. The mean absolute percentage error (MAPE) of the testing set is within 10%, which show that the power values of the following 15 min. can be predicted accurately. The simulation results verify that our method shows better performance than other existing works.

The Comparison of GARCH and ANN Model for Forecasting Volatility: Evidence based on Indian Stock Markets

2020 ◽  
Vol 14 (2) ◽  
pp. 103-121
Author(s):  
MUNEER SHAIK ◽  
Aditya Sejpal
Keyword(s):  
Neural Network ◽  
Time Series Data ◽  
Absolute Error ◽  
Series Data ◽  
Ann Model ◽  
Squared Error ◽  
Out Of Sample ◽  
Indian Stock Market ◽  
Forecasting Volatility ◽  
Better Than

In this paper, we study the performance of the Artificial Neural Networks (ANNs) and GARCH modelsto predict the volatility of the Indian stock market indices namely, NIFTY 50, NIFTY Bank and NIFTYFMCG. We have used the GARCH (1,1) and Recurrent Neural Network, a type of neural network whichis widely used for predicting time series data. The purpose of the study is to investigate if the ArtificialNeural Networks perform better than the traditional GARCH (1,1) model. An out of sample testingmethodology is applied to the most recent 20 percent of the observations for all the three indices. Wehave used Root Means Squared Error (RMSE) and Mean Absolute Error (MAE) as metrics to evaluatethe volatility predicting performances of the models. The results show no clear evidence of ANN modelperforming better than GARCH model for any of the three indices. ANNs may prove to be betterindicators in periods with low volatility while its performance deteriorated in periods with highvolatility.

scholarly journals Forecasting Rice Production of Bangladesh Using ARIMA and Artificial Neural Network Models

2020 ◽  
Vol 68 (2) ◽  
pp. 143-147
Author(s):  
Abira Sultana ◽  
Murshida Khanam
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Time Series Data ◽  
Arima Model ◽  
Rice Production ◽  
Series Data ◽  
Ann Model ◽  
Neural Network Models ◽  
Artificial Neural ◽  
Estimated Error

Forecasting behavior of Econometric and Machine Learning models has recently attracted much attention in the research sector. In this study an attempt has been made to compare the forecasting behavior of Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) using univariate time series data of annual rice production (1972 to 2013) of Bangladesh. Here, suitable ARIMA has been chosen from several selected ARIMA models with the help of AIC and BIC values. A simple ANN model using backpropagation algorithm with appropriate number of nodes or neurons in a single hidden layer, adjustable threshold value and learning rate, has been constructed. Based on the RMSE, MAE and MAPE values, the results showed that the estimated error of ANN is much higher than the estimated error of chosen ARIMA. So, according to this study, it can be said that the ARIMA model is better than ANN model for forecasting the rice production in Bangladesh. Dhaka Univ. J. Sci. 68(2): 143-147, 2020 (July)

scholarly journals A Double-Channel Hybrid Deep Neural Network Based on CNN and BiLSTM for Remaining Useful Life Prediction

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7109
Author(s):  
Chengying Zhao ◽  
Xianzhen Huang ◽  
Yuxiong Li ◽  
Muhammad Yousaf Iqbal
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Spatial Data ◽  
Deep Neural Network ◽  
Time Series Data ◽  
Remaining Useful Life ◽  
Series Data ◽  
Useful Life ◽  
Lstm Network ◽  
Double Channel

In recent years, prognostic and health management (PHM) has played an important role in industrial engineering. Efficient remaining useful life (RUL) prediction can ensure the development of maintenance strategies and reduce industrial losses. Recently, data-driven based deep learning RUL prediction methods have attracted more attention. The convolution neural network (CNN) is a kind of deep neural network widely used in RUL prediction. It shows great potential for application in RUL prediction. A CNN is used to extract the features of time-series data according to the spatial feature method. This way of processing features without considering the time dimension will affect the prediction accuracy of the model. On the contrary, the commonly used long short-term memory (LSTM) network considers the timing of the data. However, compared with CNN, it lacks spatial data extraction capabilities. This paper proposes a double-channel hybrid prediction model based on the CNN and a bidirectional LSTM network to avoid those drawbacks. The sliding time window is used for data preprocessing, and an improved piece-wise linear function is used for model validating. The prediction model is evaluated using the C-MAPSS dataset provided by NASA. The predicted results show the proposed prediction model to have a better prediction performance compared with other state-of-the-art models.

scholarly journals Estimation and Mapping of Solar Irradiance for Korea by Using COMS MI Satellite Images and an Artificial Neural Network Model

Energies ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 301 ◽  
Author(s):  
YoungHyun Koo ◽  
Myeongchan Oh ◽  
Sung-Min Kim ◽  
Hyeong-Dong Park
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Solar Irradiance ◽  
Satellite Images ◽  
Accurate Estimation ◽  
Ensemble Model ◽  
Solar Pv ◽  
Ann Model ◽  
Pv Systems ◽  
Artificial Neural

The power capacity of solar photovoltaics (PVs) in Korea has grown dramatically in recent years, and an accurate estimation of solar resources is crucial for the efficient management of these solar PV systems. Since the number of solar irradiance measurement sites is insufficient for Korea, satellite images can be useful sources for estimating solar irradiance over a wide area of Korea. In this study, an artificial neural network (ANN) model was constructed to calculate hourly global horizontal solar irradiance (GHI) from Korea Communication, Ocean and Meteorological Satellite (COMS) Meteorological Imager (MI) images. Solar position variables and five COMS MI channels were used as inputs for the ANN model. The basic ANN model was determined to have a window size of five for the input satellite images and two hidden layers, with 30 nodes on each hidden layer. After these ANN parameters were determined, the temporal and spatial applicability of the ANN model for solar irradiance mapping was validated. The final ANN ensemble model, which calculated the hourly GHI from 10 independent ANN models, exhibited a correlation coefficient (R) of 0.975 and root mean square error (RMSE) of 54.44 W/m² (12.93%), which were better results than for other remote-sensing based works for Korea. Finally, GHI maps for Korea were generated using the final ANN ensemble model. This COMS-based ANN model can contribute to the efficient estimation of solar resources and the improvement of the operational efficiency of solar PV systems for Korea.

MWGANN Prediction Model for Electromechanical Equipment Running State

2012 ◽  
Vol 490-495 ◽  
pp. 437-441
Author(s):  
Xiao Li Xu ◽  
Zhang Lei Jiang ◽  
Peng Chen
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Prediction Model ◽  
Prediction Accuracy ◽  
Time Series Data ◽  
Series Data ◽  
Stable Operation ◽  
Pump Unit ◽  
Injection Pump ◽  
Electromechanical Equipment

To ensure the rotary electromechanical equipment injection pump unit safe and stable operation, status assessment parameters should be predicted used by appropriate prediction model. This paper presents a genetic algorithm optimization neural network prediction model based on mean function new information-weighted theory (MWGANN prediction model). MWGANN prediction model can optimize the neural network structure parameters and improve the prediction accuracy and prediction timeliness by using the recency difference of time series data. Collecting large rotating injection pump unit vibration intensity time series in the industrial site, MWGANN prediction model and GANN prediction model are applied to predict trend. The results show that MWGANN model achieved good results in prediction accuracy and prediction timeliness.

scholarly journals A Modular Tide Level Prediction Method Based On NARX Neural Network

2021 ◽  
Author(s):  
Wenhao Wu ◽  
Lianbo LI ◽  
Jianchuan YIN ◽  
Wenyu LYU ◽  
Wenjun ZHANG
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Harmonic Analysis ◽  
Time Series Data ◽  
Back Propagation ◽  
Prediction Method ◽  
Series Data ◽  
Astronomical Tide ◽  
Tide Level ◽  
Narx Neural Network

Abstract This paper proposed a modular tide level prediction model based on nonlinear autoregressive exogenous model (NARX) neural network in order to improve the accuracy of tide prediction. The model divides tide data into two parts: the astronomical tide data affected by celestial tide generating force, and non-astronomical tide data affected by various environmental factors. NARX neural network and harmonic analysis are used to simulate and predict the non-astronomical and astronomical part of tide respectively, and then the final result is obtained by combining the two parts. In this paper, the tide data from Yorktown, USA, are used to simulate the prediction of tide level, and the results are compared with the traditional harmonic analysis (HA) method and Genetic Algorithm-Back Propagation (GA-BP) neural network. The results show that as a dynamic neural network, NARX neural network modular prediction model is more suitable for the analysis and prediction of time series data and has better stability and accuracy.

Neural Network Techniques for Time Series Prediction: A Review

2019 ◽  
Vol 3 (3) ◽  
Author(s):  
Muhammad Faheem Mushtaq ◽  
Urooj Akram ◽  
Muhammad Aamir ◽  
Haseeb Ali ◽  
Muhammad Zulqarnain
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Time Series Data ◽  
Weather Prediction ◽  
Time Series Prediction ◽  
Series Data ◽  
Prediction Problem ◽  
Neural Network Models ◽  
Physical Time

It is important to predict a time series because many problems that are related to prediction such as health prediction problem, climate change prediction problem and weather prediction problem include a time component. To solve the time series prediction problem various techniques have been developed over many years to enhance the accuracy of forecasting. This paper presents a review of the prediction of physical time series applications using the neural network models. Neural Networks (NN) have appeared as an effective tool for forecasting of time series.  Moreover, to resolve the problems related to time series data, there is a need of network with single layer trainable weights that is Higher Order Neural Network (HONN) which can perform nonlinearity mapping of input-output. So, the developers are focusing on HONN that has been recently considered to develop the input representation spaces broadly. The HONN model has the ability of functional mapping which determined through some time series problems and it shows the more benefits as compared to conventional Artificial Neural Networks (ANN). The goal of this research is to present the reader awareness about HONN for physical time series prediction, to highlight some benefits and challenges using HONN.

Neural networks approached for modelling river suspended sediment concentration due to tropical storms

2013 ◽  
Vol 11 (4) ◽  
pp. 457-466
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Suspended Sediment ◽  
Suspended Sediment Concentration ◽  
Time Series Data ◽  
Water Discharge ◽  
Sediment Concentration ◽  
Series Data ◽  
Generalized Regression Neural Network ◽  
Event Based

Artificial neural networks are one of the advanced technologies employed in hydrology modelling. This paper investigates the potential of two algorithm networks, the feed forward backpropagation (BP) and generalized regression neural network (GRNN) in comparison with the classical regression for modelling the event-based suspended sediment concentration at Jiasian diversion weir in Southern Taiwan. For this study, the hourly time series data comprised of water discharge, turbidity and suspended sediment concentration during the storm events in the year of 2002 are taken into account in the models. The statistical performances comparison showed that both BP and GRNN are superior to the classical regression in the weir sediment modelling. Additionally, the turbidity was found to be a dominant input variable over the water discharge for suspended sediment concentration estimation. Statistically, both neural network models can be successfully applied for the event-based suspended sediment concentration modelling in the weir studied herein when few data are available.

scholarly journals Remaining Useful Life Prediction Using Temporal Convolution with Attention

AI ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 48-70
Author(s):  
Wei Ming Tan ◽  
T. Hui Teo
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Series Data ◽  
Remaining Useful Life ◽  
Sensor Data ◽  
Series Data ◽  
Multiple Time ◽  
Data Set ◽  
Form Complex ◽  
Useful Life

Prognostic techniques attempt to predict the Remaining Useful Life (RUL) of a subsystem or a component. Such techniques often use sensor data which are periodically measured and recorded into a time series data set. Such multivariate data sets form complex and non-linear inter-dependencies through recorded time steps and between sensors. Many current existing algorithms for prognostic purposes starts to explore Deep Neural Network (DNN) and its effectiveness in the field. Although Deep Learning (DL) techniques outperform the traditional prognostic algorithms, the networks are generally complex to deploy or train. This paper proposes a Multi-variable Time Series (MTS) focused approach to prognostics that implements a lightweight Convolutional Neural Network (CNN) with attention mechanism. The convolution filters work to extract the abstract temporal patterns from the multiple time series, while the attention mechanisms review the information across the time axis and select the relevant information. The results suggest that the proposed method not only produces a superior accuracy of RUL estimation but it also trains many folds faster than the reported works. The superiority of deploying the network is also demonstrated on a lightweight hardware platform by not just being much compact, but also more efficient for the resource restricted environment.

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