Prediction the data consumption for power demands by elman neural network

Ismael, Lafta; Ismael, Ammar Issa

doi:10.11591/ijece.v9i5.pp4003-4009

Cited by 4 publications

(4 citation statements)

References 4 publications

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“…It can not only realize the modeling of static system, but also realize the mapping of dynamic system and directly reflect the dynamic characteristics of the system, it is better than propagation neural network in computing power and network stability. Elman neural network is very suitable for time-series prediction, in many fields of prediction research; it has achieved many successful results, such as Liu et al [29] did the analysis of wind speed prediction using ElmanNN; Ismael et al [30] predicted the data consumption for power demands by ElmanNN successfully; Li et al [31] did the prediction of Urban Rail Transit Sectional Passenger Flow Based on ElmanNN successfully; etc [32][33][34].…”

Section: Elman Neural Network Model [2728]mentioning

confidence: 99%

A comparative study of two methods to predict the incidence of hepatitis B in Guangxi, China

et al. 2020

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In recent years, the incidence of hepatitis B (HB) in Guangxi is higher than that of the national level; it has been increasing, so it is urgent to do a good predictive research of HB incidence, which can help analyze the early warning of hepatitis B in Guangxi, China. In the study, the feasibility of predicting HB incidence in Guangxi by autoregressive integrated moving average (ARIMA) model method and Elman neural network (ElmanNN) method was discussed respectively, and the prediction accuracy of the two models was compared. Finally, we established the ARIMA (0, 1, 1) model and ElmanNN with 8 neurons. Both ARIMA (0, 1, 1) model and ElmanNN model had good performance, and their prediction accuracy were high. The fitting and prediction root-mean-square error (RMSE) and mean absolute error (MAE) of ElmanNN were smaller than those of ARIMA (0, 1, 1) model, which indicated that ElmanNN was superior to ARIMA (0, 1, 1) model in predicting the incidence of hepatitis B in Guangxi. Based on the ElmanNN, the HB incidence from September 2019 to December 2020 in Guangxi was predicted, the predicted results showed that the incidence of HB in 2020 was slightly higher than that in 2019 and the change trend was similar to that in 2019, for 2021 and beyond, the ElmanNN model could be used to continue the predictive analysis.

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Section: Elman Neural Network Model [2728]mentioning

confidence: 99%

A comparative study of two methods to predict the incidence of hepatitis B in Guangxi, China

et al. 2020

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“…we used Elman's algorithm from research [19]. We use the two-condition criterion to stop iterating over the training data.…”

Section: Elman Neural Network Algorithmmentioning

confidence: 99%

“…ERNN is used for the prediction of vehicle interior noise [14]. ERNN describes powerful learning techniques such as uncertainty estimation [15], environmental adaptability [16], increasing the amount and accuracy of forecast data [17], improving the accuracy of time series forecasting [18], [19], early detection of circuit failures by combining with cuckoo search [20], improvement of accuracy of identification processing by combining with the Kalman filter [21], face recognition possible by connecting with principal component analysis (PCA) [22].…”

Section: Introductionmentioning

confidence: 99%

Eligibility of village fund direct cash assistance recipients using artificial neural network

Midyanti

Bahri

Suhardi

et al. 2023

IJ-AI

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<p>Bantuan Langsung Tunai Dana Desa (BLT-DD), or known as Village Fund Direct Cash Assistance is assistance from the Indonesian government which causes problems and conflicts in the community when the assistance is not on target. The classification algorithm is proven to use in determining BLT-DD recipients. In this study, the radial basis function (RBF) and elman recurrent neural network (ERNN) models compare to classify the eligibility of BLTDD recipients. In the experiment, the optimal performance of the RBF and ERNN compare in determining the eligibility of BLT-DD recipients. Also, it’s compared with the classification algorithm that implements the same data, namely BLT-DD data for Kubu Raya District. The experimental results show the effectiveness of the RBF model in recognizing test data, while the ERNN model is effective in identifying test data. The RBF and ERNN models can achieve the same total accuracy of 98.10%.</p>

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“…Power big data mining is mainly carried out from the prediction of power data [7], [8]. Machine learning is widely used in classification and prediction problems.…”

Section: Related Workmentioning

confidence: 99%

Household Power Consumption Prediction Method Based on Selective Ensemble Learning

2020

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In the context power big data, the household power consumption data on the user side has the characteristics of large quantity, wide distribution and many types. Ensemble learning is very excellent in the analysis and mining of power data with large amount of data, strong timeliness and many influencing factors. Based on the data of household power consumption, this paper analyzes and predicts the power consumption of some users in a city by using selective ensemble learning technology and combining with meteorological factors. In this paper, K-means algorithm is used to cluster the household power consumption, and then the clustering results are combined with the meteorological information. In the stage of power consumption prediction, a Filter Iterative Optimization Ensemble Strategy (FIOES) is proposed to selectively ensemble the basic learners and get the final prediction model. Experimental results show that the FIOES algorithm has better performance in time cost and prediction accuracy than the traditional ensemble learning algorithm. INDEX TERMS Power big data, ensemble learning, power consumption, iterative optimization.

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Prediction the data consumption for power demands by elman neural network

Cited by 4 publications

References 4 publications

A comparative study of two methods to predict the incidence of hepatitis B in Guangxi, China

A comparative study of two methods to predict the incidence of hepatitis B in Guangxi, China

Eligibility of village fund direct cash assistance recipients using artificial neural network

Household Power Consumption Prediction Method Based on Selective Ensemble Learning

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