Forecasting based on an ensemble Autoregressive Moving Average - Adaptive neuro - Fuzzy inference system – Neural network - Genetic Algorithm Framework

Prado, Francisco Prado; Minutolo, Marcel C.; Kristjanpoller, Werner

doi:10.1016/j.energy.2020.117159

Cited by 67 publications

(34 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Singh and Dwivedi [17] divided the benefits of education into intrinsic benefits and spillover benefits. Prado et al [18] also referred to the intrinsic benefits of education as the intrinsic function of human capital. Both intrinsic benefits and spillover benefits 4 Complexity refer to improving the labor productivity (rate) of the educated.…”

Section: E Effect Of Education On Economic Growthmentioning

confidence: 99%

[Retracted] Forecast and Evaluation of Educational Economic Contribution Based on Fuzzy Neural Network

Liu

2021

Complexity

View full text Add to dashboard Cite

According to the theory that the degree of education of workers in educational economics has a certain positive relationship with social labor productivity, the fuzzy system and neural network modeling mechanism are used to establish the fuzziness of laborers’ education level to social productivity (per capita national income). This article combines fuzzy theory and neural network theory to construct an empirical model for the analysis of the contribution of education economy and conduct an empirical analysis of statistical data from 2010 to 2020. Analysis shows that there is a great correlation between per capita years of education and per capita GDP, especially the number of college students per million people has a greater correlation with per capita GDP. This fully confirms that economic growth is increasingly dependent on education, especially higher education. The main body of this article is the improvement of the measurement model and the calculation of the contribution of our country’s education to economic growth using the fuzzy neural network measurement model. The final empirical conclusion shows that education has a significant role in promoting the development of our country’s economy.

show abstract

Section: E Effect Of Education On Economic Growthmentioning

confidence: 99%

[Retracted] Forecast and Evaluation of Educational Economic Contribution Based on Fuzzy Neural Network

Liu

2021

Complexity

View full text Add to dashboard Cite

show abstract

“…The publication [17] has displayed a novel hybrid model ANFIS which consolidates both ANN and fuzzy frameworks for prediction future power utilization; the result has proved that this hybridizing approach has the potential of improving prediction performance since it has more significant accuracy and leads to smaller errors contrasted with other models. Likewise, the advantages of the hybrid approach were also verified by many studies [18][19][20]。.…”

Section: Literature Reviewmentioning

confidence: 59%

Appling an Improved Method Based on ARIMA Model to Predict the Short‐Term Electricity Consumption Transmitted by the Internet of Things (IoT)

Guo

Chen

Wang

et al. 2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

The rapid development of the Internet of Things (IoT) has brought a data explosion and a new set of challenges. It has been an emergency to construct a more robust and precise model to predict the electricity consumption data collected from the Internet of Things (IoT). Accurately forecasting the electricity consumption is a crucial technology for the planning of the energy resource which could lead to remarkable conservation of the building electricity consumption. This paper is focused on the electricity consumption forecasting of an office building with a small-scale dataset, and 117 daily electricity consumption of the building are involved in the dataset, among which 89 values are selected as the training dataset and the remaining 28 values as the testing dataset. The hybrid model ARIMA (autoregression integrated moving average)-SVR (support vector regression) is proposed to predict the electricity consumption with different prediction horizons ranging from 1 day to 28 days. The model performances are assessed by three evaluation indicators, respectively, are the mean squared error (MSE), the root mean square error (RMSE), and the mean absolute percentage error (MAPE). The proposed model ARIMA-SVR is compared with the other four models, respectively, are the ARIMA, ARIMA-GBR (gradient boosting regression), LSTM (long short-term memory), and GRU (gated recurrent unit) models. The experiment result shows that the ARIMA-SVR model has lower prediction errors when the prediction horizon is within 20 days, and the ARIMA model is better when the prediction horizon is in the interval of 20 to 28 days. The provided method ARIMA-SVR has higher flexibility, and it is a great choice for electricity consumption prediction with more accurate results.

show abstract

“…Adaptive Neuro-Fuzzy Inference System (ANFIS): ANN gives a linear model based on fuzzy rules and expert systems close to human-like expert system [ 15 ]. Whereas ANFIS is a combinational model of FIS and ANN [ 33 ]. As ANFIS is a hybrid system, so its learning ability is more efficient than FIS models.…”

Section: Proposed Systemmentioning

confidence: 99%

Classification of COVID-19 individuals using adaptive neuro-fuzzy inference system

et al. 2021

View full text Add to dashboard Cite

Coronavirus is a fatal disease that affects mammals and birds. Usually, this virus spreads in humans through aerial precipitation of any fluid secreted from the infected entity’s body part. This type of virus is fatal than other unpremeditated viruses. Meanwhile, another class of coronavirus was developed in December 2019, named Novel Coronavirus (2019-nCoV), first seen in Wuhan, China. From January 23, 2020, the number of affected individuals from this virus rapidly increased in Wuhan and other countries. This research proposes a system for classifying and analyzing the predictions obtained from symptoms of this virus. The proposed system aims to determine those attributes that help in the early detection of Coronavirus Disease (COVID-19) using the Adaptive Neuro-Fuzzy Inference System (ANFIS). This work computes the accuracy of different machine learning classifiers and selects the best classifier for COVID-19 detection based on comparative analysis. ANFIS is used to model and control ill-defined and uncertain systems to predict this globally spread disease’s risk factor. COVID-19 dataset is classified using Support Vector Machine (SVM) because it achieved the highest accuracy of 100% among all classifiers. Furthermore, the ANFIS model is implemented on this classified dataset, which results in an 80% risk prediction for COVID-19.

show abstract

Forecasting based on an ensemble Autoregressive Moving Average - Adaptive neuro - Fuzzy inference system – Neural network - Genetic Algorithm Framework

Cited by 67 publications

References 33 publications

[Retracted] Forecast and Evaluation of Educational Economic Contribution Based on Fuzzy Neural Network

[Retracted] Forecast and Evaluation of Educational Economic Contribution Based on Fuzzy Neural Network

Appling an Improved Method Based on ARIMA Model to Predict the Short‐Term Electricity Consumption Transmitted by the Internet of Things (IoT)

Classification of COVID-19 individuals using adaptive neuro-fuzzy inference system

Contact Info

Product

Resources

About