scholarly journals PREDICTION MODEL OF AMMONIA CONCENTRATION IN YELLOW-FEATHER BROILERS HOUSE DURING WINTER BASED ON EEMD-GRU

2020 ◽  
Vol 61 (2) ◽  
pp. 59-70
Author(s):  
Zeying Xu ◽  
Xiuguo Zou ◽  
Zhengling Yin ◽  
Shikai Zhang ◽  
Yuanyuan Song ◽  
...  
Keyword(s):  
Prediction Model ◽  
Time Series Data ◽  
Ventilation System ◽  
Back Propagation ◽  
Ammonia Concentration ◽  
Ensemble Empirical Mode Decomposition ◽  
Series Data ◽  
Central System ◽  
Mean Square Errors ◽  
Broiler House

In winter, the poor ventilation conditions in broiler houses may lead to high ammonia concentration, which affects the health of yellow-feather broilers or even causes the death of many broilers. This research used a machine learning model to predict the ammonia concentration in a broiler house during winter. After analysis, it was found that the ammonia generation in the broiler house was a gradual accumulation featured by non-linear data. After the broilers entered the broiler house for several days, and the ammonia concentration reached a certain value, a ventilation system was used for regulating the concentration. Firstly, the back-propagation (BP) neural network model and gated recurrent unit (GRU) model were used for predicting the ammonia concentration, respectively. Then, ensemble empirical mode decomposition (EEMD) was performed on the time series data of ammonia concentration in the broiler house. After that, the EEMD-GRU prediction model has been established for the intrinsic mode function (IMF) components and the temperature and humidity data in the broiler house. Finally, all component results were summarized to obtain the final prediction result. A comparison was conducted among the prediction results obtained by the above three models. The results show that the root mean square errors of the above three models are 6.2 ppm, 4.4 ppm, and 2.4 ppm, respectively, and the average absolute errors were 4.9 ppm, 2.8 ppm, and 1.6 ppm, respectively. It could be seen that the EEMD-GRU model had higher accuracy in predicting the ammonia concentration in the broiler house. The EEMD-GRU model can effectively predict the ammonia concentration in broiler houses, facilitating the feedback to the central system for timely adjustment.

scholarly journals Study on Fractal Multistep Forecast for the Prediction of Driving Behavior

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Longhai Yang ◽  
Hong Xu ◽  
Xiqiao Zhang ◽  
Shuai Li ◽  
Wenchao Ji
Keyword(s):  
Prediction Model ◽  
Real Time ◽  
Time Series Data ◽  
Back Propagation ◽  
Series Data ◽  
Vehicle Speed ◽  
Trajectory Data ◽  
Time Data ◽  
Characteristic Analysis ◽  
Real Time Data

The application and development of new technology make it possible to acquire real-time data of vehicles. Based on these real-time data, the behavior of vehicles can be analyzed. The prediction of vehicle behavior provides data support for the fine management of traffic. This paper proposes speed and acceleration have fractal features by R/S analysis of the time series data of speed and acceleration. Based on the characteristic analysis of microscopic parameters, the characteristic indexes of parameters are quantified, the fractal multistep prediction model of microparameters is established, and the BP (back propagation neural networks) model is established to estimate predictable step of fractal prediction model. The fractal multistep prediction model is used to predict speed acceleration in the predictable step. NGSIM trajectory data are used to test the multistep prediction model. The results show that the proposed fractal multistep prediction model can effectively realize the multistep prediction of vehicle speed.

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.

scholarly journals A New Hybrid Model Based on Data Preprocessing and an Intelligent Optimization Algorithm for Electrical Power System Forecasting

2015 ◽  
Vol 2015 ◽  
pp. 1-17
Author(s):  
Ping Jiang ◽  
Xuejiao Ma ◽  
Feng Liu
Keyword(s):  
Power System ◽  
Hybrid Model ◽  
Time Series Data ◽  
Electrical Power ◽  
Back Propagation ◽  
Data Preprocessing ◽  
Back Propagation Neural Network ◽  
Ensemble Empirical Mode Decomposition ◽  
Series Data ◽  
Electrical Power System

The establishment of electrical power system cannot only benefit the reasonable distribution and management in energy resources, but also satisfy the increasing demand for electricity. The electrical power system construction is often a pivotal part in the national and regional economic development plan. This paper constructs a hybrid model, known as the E-MFA-BP model, that can forecast indices in the electrical power system, including wind speed, electrical load, and electricity price. Firstly, the ensemble empirical mode decomposition can be applied to eliminate the noise of original time series data. After data preprocessing, the back propagation neural network model is applied to carry out the forecasting. Owing to the instability of its structure, the modified firefly algorithm is employed to optimize the weight and threshold values of back propagation to obtain a hybrid model with higher forecasting quality. Three experiments are carried out to verify the effectiveness of the model. Through comparison with other traditional well-known forecasting models, and models optimized by other optimization algorithms, the experimental results demonstrate that the hybrid model has the best forecasting performance.

Some statistical and CI models to predict chaotic high-frequency financial data

2020 ◽  
Vol 39 (5) ◽  
pp. 6419-6430
Author(s):  
Dusan Marcek
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Methodological Approach ◽  
Back Propagation ◽  
Large Data ◽  
Series Data ◽  
Data Set ◽  
Training Time ◽  
Optimal Population ◽  
Forecast Time

To forecast time series data, two methodological frameworks of statistical and computational intelligence modelling are considered. The statistical methodological approach is based on the theory of invertible ARIMA (Auto-Regressive Integrated Moving Average) models with Maximum Likelihood (ML) estimating method. As a competitive tool to statistical forecasting models, we use the popular classic neural network (NN) of perceptron type. To train NN, the Back-Propagation (BP) algorithm and heuristics like genetic and micro-genetic algorithm (GA and MGA) are implemented on the large data set. A comparative analysis of selected learning methods is performed and evaluated. From performed experiments we find that the optimal population size will likely be 20 with the lowest training time from all NN trained by the evolutionary algorithms, while the prediction accuracy level is lesser, but still acceptable by managers.

Ensemble Empirical Mode Decomposition Based Deep Learning Model for Short-Term Wind Power Forecasting

2022 ◽  
Author(s):  
J.M. González-Sopeña
Keyword(s):  
Deep Learning ◽  
Wind Power ◽  
Empirical Mode Decomposition ◽  
Time Series Data ◽  
Ensemble Empirical Mode Decomposition ◽  
Series Data ◽  
Intrinsic Mode Functions ◽  
Wind Power Forecasting ◽  
Mode Decomposition ◽  
Power Forecasting

Abstract. In the last few years, wind power forecasting has established itself as an essential tool in the energy industry due to the increase of wind power penetration in the electric grid. This paper presents a wind power forecasting method based on ensemble empirical mode decomposition (EEMD) and deep learning. EEMD is employed to decompose wind power time series data into several intrinsic mode functions and a residual component. Afterwards, every intrinsic mode function is trained by means of a CNN-LSTM architecture. Finally, wind power forecast is obtained by adding the prediction of every component. Compared to the benchmark model, the proposed approach provides more accurate predictions for several time horizons. Furthermore, prediction intervals are modelled using quantile regression.

scholarly journals A Study on Estimation and Prediction of Vector Time Series Model Using Financial Big Data (Interest Rates)

2021 ◽  
Vol 12 (5) ◽  
pp. 309-316
Author(s):  
Jae-Hyun Kim, Chang-Ho An
Keyword(s):  
Time Series ◽  
Interest Rate ◽  
Prediction Model ◽  
Interest Rates ◽  
Time Series Data ◽  
Information Criteria ◽  
Series Data ◽  
Call Rate ◽  
Causality Test ◽  
Vector Autoregressive

Due to the global economic downturn, the Korean economy continues to slump. Hereupon the Bank of Korea implemented a monetary policy of cutting the base rate to actively respond to the economic slowdown and low prices. Economists have been trying to predict and analyze interest rate hikes and cuts. Therefore, in this study, a prediction model was estimated and evaluated using vector autoregressive model with time series data of long- and short-term interest rates. The data used for this purpose were call rate (1 day), loan interest rate, and Treasury rate (3 years) between January 2002 and December 2019, which were extracted monthly from the Bank of Korea database and used as variables, and a vector autoregressive (VAR) model was used as a research model. The stationarity test of variables was confirmed by the ADF-unit root test. Bidirectional linear dependency relationship between variables was confirmed by the Granger causality test. For the model identification, AICC, SBC, and HQC statistics, which were the minimum information criteria, were used. The significance of the parameters was confirmed through t-tests, and the fitness of the estimated prediction model was confirmed by the significance test of the cross-correlation matrix and the multivariate Portmanteau test. As a result of predicting call rate, loan interest rate, and Treasury rate using the prediction model presented in this study, it is predicted that interest rates will continue to drop.

Development of a real-time prediction model of driver behavior at intersections using kinematic time series data

2017 ◽  
Vol 106 ◽  
pp. 428-436 ◽  
Author(s):  
Yaoyuan V. Tan ◽  
Michael R. Elliott ◽  
Carol A.C. Flannagan
Keyword(s):  
Time Series ◽  
Prediction Model ◽  
Real Time ◽  
Time Series Data ◽  
Driver Behavior ◽  
Series Data ◽  
Time Prediction
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