scholarly journals Forecasting Error Modelling for Improving PV Generation Prediction

2019 ◽  
Vol 2 (1) ◽  
pp. 27-36
Author(s):  
Happy Aprillia ◽  
Hong-Tzer Yang
Keyword(s):  
Neural Network ◽  
Power Systems ◽  
Network Model ◽  
Neural Network Model ◽  
Weather Condition ◽  
Absolute Error ◽  
Density Estimator ◽  
Forecasting Error ◽  
Uncertainty Model ◽  
Pv Generation

Accurate forecasting of Photovoltaic (PV) generation output is important in operation of high PV-penetrated power systems. In this paper, an adaptive uncertainty modelling method for forecasting error is proposed to improve the prediction accuracy of PV generation. The proposed method models the uncertainty in forecast data using Kernel Density Estimator and guarantee the provision of accurate expected value. Neural Network model is then constructed by the developed uncertainty model to forecast the PV output. The actual confidence level is traced within the day and injected as an input to the Neural Network model by observing the Mean Absolute Prediction Error (MAPE) and Unscaled Mean Bounded Relative Absolute Error (UMBRAE). The proposed method is tested with various significant changes of weather condition and proved to have promising performance on PV generation forecasting. Thus, the developed adaptive uncertainty model can be further used in power system planning that have high-penetration energy sources with stochastic behavior.

Prediction Model of Micum Strength for Iron Ore Sinter

2011 ◽  
Vol 402 ◽  
pp. 476-479
Author(s):  
Wei Wang ◽  
Zhi Hui Xu ◽  
Long Long Yang ◽  
Zheng Liang Xue ◽  
Dong Nan Zhao ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Absolute Error ◽  
Maximum Absolute Error ◽  
Important Indicator ◽  
The Neural Network ◽  
Iron Ore Sinter ◽  
Real Value ◽  
The Law

Micum strength is an important indicator of quality of sinter; BP artificial neural network model is built to predict the strength of sinter drum. The neural network use the main factors that influence the sinter drum as input data, and output is Micum strength. Experiment results shows that the maximum absolute error between the Micum strength predicted by neural network and real value from the sinter plant is 0.3346, and the average absolute error is 0.1154. These prove that the prediction is accuracy. In addition, because of the "black box" characteristic of the neural network model, the neural network model can not give the law of how the various factors affect the micum strength of sinter ore, this paper also uses the model to analysis the law of how TFe, SiO2 content affect the micum strength. The results not only consist with the sintering theory, but also verify the validity of the model.

Optimization and Analysis for Coke Consumption Based on Clustering

2011 ◽  
Vol 287-290 ◽  
pp. 1112-1115
Author(s):  
Jun Hong Zhang
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Clustering Analysis ◽  
Coke Consumption ◽  
Prediction Method ◽  
Absolute Error ◽  
Coke Rate ◽  
The Neural Network ◽  
Fuel Rate

In order to reduce the coke consumption of Blast Furnace（BF），a relevance analysis is carried out for operation parameters and fuel rate of BF，and a prediction method that is combining clustering analysis and artificial neural network for coke rate is proposed. The data cluster is divided into several classes by clustering analysis，the data similarity is high，and the neural network model is used to realize the prediction of coke rate. By combining the neural network with clustering analysis，the data in one BF is simulated，and the results are compared with the traditional neural network model. The result shows that the improved neural network has a higher accuracy, the average absolute error can be decreased by 3.13kg/t, and the average relative error can be decreased by 5.19%, it will have a good using foreground.

scholarly journals Evaluation of Neural Network Model for Estimating Pile Load Capacity

2021 ◽  
Vol 21 (5) ◽  
pp. 221-228
Author(s):  
Byungsik Lee
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Load Capacity ◽  
Absolute Error ◽  
Descent Method ◽  
Training Data ◽  
Gradient Descent Method ◽  
Neural Network Models ◽  
Data Set

Neural network models based on deep learning algorithms are increasingly used for estimating pile load capacities as supplements of bearing capacity equations and field load tests. A series of hyperparameter tuning is required to improve the performance and reliability of developing a neural network model. In this study, the number of hidden layers and neurons, the activation functions, the optimizing algorithms of the gradient descent method, and the learning rates were tuned. The grid search method was applied for the tuning, which is a hyperpameter optimizer supplied by the developing platform. The cross-validation method was applied to enhance reliability for model validation. An appropriate number of epochs was determined using the early stopping method to prevent the overfitting of the model to the training data. The performance of the tuned optimum model evaluated for the test data set revealed that the model could estimate pile load capacities approximately with an average absolute error of 3,000 kN and a coefficient of determinant of 0.5.

scholarly journals Novel Cooperative Multi-Input Multilayer Perceptron Neural Network Performance Analysis with Application of Solar Irradiance Forecasting

2021 ◽  
Vol 2021 ◽  
pp. 1-24
Author(s):  
M. Madhiarasan ◽  
Mohamed Louzazni ◽  
Partha Pratim Roy
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Multilayer Perceptron ◽  
Solar Irradiance ◽  
Meteorological Parameters ◽  
Weather Condition ◽  
Forecasting Model ◽  
Generalization Ability ◽  
Forecasting Models

To forecast solar irradiance with higher accuracy and generalization capability is challenging in the photovoltaic (PV) energy system. Meteorological parameters are highly influential in solar irradiance, leading to intermittent and randomicity. Forecasting using a single neural network model does not have sufficient generalization ability to achieve the optimal forecasting of solar irradiance. This paper proposes a novel cooperative multi-input multilayer perceptron neural network (CMMLPNN) to mitigate the issues related to generalization and meteorological effects. Authors develop a proposed forecasting neural network model based on the amalgamation of two inputs, three inputs, four inputs, five inputs, and six inputs associated multilayer perceptron neural network. In the proposed forecasting model (CMMLPNN), the authors overcome the variance based on the meteorological parameters. The amalgamation of five multi-input multilayer perceptron neural networks leads to better generalization ability. Some individual multilayer perceptron neural network-based forecasting models outperform in some situations, but cannot assure generalization ability and suffer from the meteorological weather condition. The proposed CMMLPNN (cooperative multi-input multilayer perceptron neural network) achieves better forecasting accuracy with the generalization ability. Therefore, the proposed forecasting model is superior to other neural network-based forecasting models and existing models.

Inflation Forecast Based on BP Neural Network Model

2014 ◽  
Vol 989-994 ◽  
pp. 5536-5539
Author(s):  
Yue Chun Wen ◽  
Li Na Tan ◽  
Hai Long Wu
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Absolute Error ◽  
Maximum Absolute Error ◽  
Inflation Expectation ◽  
Wide Range ◽  
Excess Liquidity ◽  
Bp Neural Network Model

This paper analyzes inflation forecast based on BP neural network model. Firstly, it reviews some references about BP neural network and finds that it is a nonlinear adaptive data-driven model with induction ability and a wide range of function approximation ability so that BP neural network could be applied into forecast research. Secondly, it builds up the BP neural network model to predict CPI, selecting the four indicators, which are excess liquidity, exchange rates, inflation expectation and macro-economic leading index. Then it carries out empirical experiment and takes advantage of the monthly data of the above four indicators from March 2005 to December 2012 to forecast CPI. The results show that when prediction period is 3 months, the maximum absolute error between forecast value and real value is 0.0139, and the minimum absolute error is 0.0005. When prediction period is 6 months, the maximum absolute error is not more than 0.02. It proves that BP neural network model can predict coming CPI trend at least 6 months according to the existing data and it means it is suitable for the study of inflation forecast.

scholarly journals Generalized Deep Neural Network Model for Cuffless Blood Pressure Estimation with Photoplethysmogram Signal Only

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5668
Author(s):  
Yan-Cheng Hsu ◽  
Yung-Hui Li ◽  
Ching-Chun Chang ◽  
Latifa Nabila Harfiya
Keyword(s):  
Neural Network ◽  
Blood Pressure ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
Public Awareness ◽  
Absolute Error ◽  
Estimation Model ◽  
British Hypertension Society ◽  
Proposed Model

Due to the growing public awareness of cardiovascular disease (CVD), blood pressure (BP) estimation models have been developed based on physiological parameters extracted from both electrocardiograms (ECGs) and photoplethysmograms (PPGs). Still, in order to enhance the usability as well as reduce the sensor cost, researchers endeavor to establish a generalized BP estimation model using only PPG signals. In this paper, we propose a deep neural network model capable of extracting 32 features exclusively from PPG signals for BP estimation. The effectiveness and accuracy of our proposed model was evaluated by the root mean square error (RMSE), mean absolute error (MAE), the Association for the Advancement of Medical Instrumentation (AAMI) standard and the British Hypertension Society (BHS) standard. Experimental results showed that the RMSEs in systolic blood pressure (SBP) and diastolic blood pressure (DBP) are 4.643 mmHg and 3.307 mmHg, respectively, across 9000 subjects, with 80.63% of absolute errors among estimated SBP records lower than 5 mmHg and 90.19% of absolute errors among estimated DBP records lower than 5 mmHg. We demonstrated that our proposed model has remarkably high accuracy on the largest BP database found in the literature, which shows its effectiveness compared to some prior works.

Study on Model of Organic Bentonite Adsorption for Phenol Based on Artificial Neural Network

2014 ◽  
Vol 675-677 ◽  
pp. 460-465
Author(s):  
Shang Chao Liu ◽  
Jin Xuan Zhou ◽  
Gai Feng Xue
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Absolute Error ◽  
Solution Concentration ◽  
Solution Temperature ◽  
Phenol Solution ◽  
Adsorption Time ◽  
Organic Bentonite

This paper studied the adsorption of organic bentonite for phenol solution by changing the condition of organic bentonite dosage, phenol concentration temperature, adsorption time. According to the experimental results The system of BP neural network was evaluated by Matlab software to forecasting the complex nonlinear relationship between the amount of phenol solution concentration, adsorption time, solution temperature and remove rate of phenol, Experimental data used for the neural network model is more than 534 times of training. After training, the model achieves an accuracy of 0.0001. Finally, a group of test is forecasted by the data model. The results showed that predictive value and measured value of absolute error is only 0.0032 and0.0016. The predicted results show that in the system, a BP neural network model is evaluated successful..This template explains and demonstrates how to prepare your camera-ready paper forTrans Tech Publications. The best is to read these instructions and follow the outline of this text.

scholarly journals Parameter Identification of Multistage Fracturing Horizontal Well Based on PSO-RBF Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Rongwang Yin ◽  
Qingyu Li ◽  
Peichao Li ◽  
Detang Lu
Keyword(s):  
Neural Network ◽  
Parameter Identification ◽  
Network Model ◽  
Neural Network Model ◽  
Horizontal Well ◽  
Rbf Neural Network ◽  
Optimal Solution ◽  
Absolute Error ◽  
Physical Parameters ◽  
Proposed Model

In order to more accurately identify multistage fracturing horizontal well (MFHW) parameters and address the heterogeneity of reservoirs and the randomness of well-production data, a new method based on the PSO-RBF neural network model is proposed. First, the GPU parallel program is used to calculate the bottomhole pressure of a multistage fracturing horizontal well. Second, most of the above pressure data are imported into the RBF neural network model for training. In the training process, the optimization function of the global optimal solution of the PSO algorithm is employed to optimize the parameters of the RBF neural network, and eventually, the required PSO-RBF neural network model is established. Third, the resulting neural network is tested using the remaining data. Finally, a field case of a multistage fracturing horizontal well is studied by using the presented PSO-RBF neural network model. The results show that in most cases, the proposed model performs better than other models, with the highest correlation coefficient, the lowest mean, and absolute error. This proves that the PSO-RBF neural network model can be applied effectively to horizontal well parameter identification. The proposed model has great potential to improve the prediction accuracy of reservoir physical parameters.

Sign in / Sign up

Export Citation Format

Share Document