A Weighted Mean-Squared Error Optimization Model with both Controllable and Noise Input Variables for a Cuboidal Design Region

2019 ◽  
Author(s):  
Akın Özdemir
Keyword(s):  
Optimization Model ◽  
Mean Squared Error ◽  
Weighted Mean ◽  
Squared Error ◽  
Input Variables ◽  
Noise Input ◽  
Error Optimization

A novel weighted mean-squared error optimization model to obtain optimal conditions of adsorption factors for a lead removal process

2021 ◽  
Vol 218 ◽  
pp. 294-302
Author(s):  
Akın Özdemir ◽  
Çağatay Teke ◽  
Hüseyin Serencam ◽  
Metin Uçurum ◽  
Ali Gündoğdu
Keyword(s):  
Optimization Model ◽  
Mean Squared Error ◽  
Lead Removal ◽  
Optimal Conditions ◽  
Weighted Mean ◽  
Removal Process ◽  
Squared Error ◽  
Error Optimization

Perceptually Weighted Mean Squared Error Based Rate-Distortion Optimization for HEVC

2020 ◽  
Vol 66 (4) ◽  
pp. 824-834
Author(s):  
Xiuzhe Wu ◽  
Hanli Wang ◽  
Sudeng Hu ◽  
Sam Kwong ◽  
C.-C. Jay Kuo
Keyword(s):  
Mean Squared Error ◽  
Rate Distortion ◽  
Rate Distortion Optimization ◽  
Weighted Mean ◽  
Squared Error

Using BPNN to Predict Number of Low-Income Households in Taiwan Based on Seasonally Adjusted Annualized Rates for Real GDP

2014 ◽  
Vol 1044-1045 ◽  
pp. 1824-1827
Author(s):  
Yi Ti Tung ◽  
Tzu Yi Pai
Keyword(s):  
Neural Network ◽  
Low Income ◽  
Mean Squared Error ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Percentage Error ◽  
Real Gdp ◽  
Squared Error ◽  
Input Variables ◽  
Low Income Households

In this study, the back-propagation neural network (BPNN) was used to predict the number of low-income households (NLIH) in Taiwan, taking the seasonally adjusted annualized rates (SAAR) for real gross domestic product (GDP) as input variables. The results indicated that the lowest mean absolute percentage error (MAPE), mean squared error (MSE), root mean squared error (RMSE), and highest correlation coefficient (R) for training and testing were 4.759 % versus 19.343 %, 24429972.268 versus 781839890.859, 4942.669 versus 27961.400, and 0.945 versus 0.838, respectively.

scholarly journals Deep Learning-Assisted Short-Term Load Forecasting for Sustainable Management of Energy in Microgrid

Inventions ◽  
2021 ◽  
Vol 6 (1) ◽  
pp. 15
Author(s):  
Arash Moradzadeh ◽  
Hamed Moayyed ◽  
Sahar Zakeri ◽  
Behnam Mohammadi-Ivatloo ◽  
A. Pedro Aguiar
Keyword(s):  
Deep Learning ◽  
Power Systems ◽  
Correlation Coefficient ◽  
Mean Squared Error ◽  
Load Forecasting ◽  
Short Term ◽  
Squared Error ◽  
Short Term Load Forecasting ◽  
Input Variables ◽  
Lstm Network

Nowadays, supplying demand load and maintaining sustainable energy are important issues that have created many challenges in power systems. In these types of problems, short-term load forecasting has been proposed as one of the management and energy supply modes in power systems. In this paper, after reviewing various load forecasting techniques, a deep learning method called bidirectional long short-term memory (Bi-LSTM) is presented for short-term load forecasting in a microgrid. By collecting relevant features available in the input data at the training stage, it is shown that the proposed procedure enjoys important properties, such as its great ability to process time series data. A microgrid in rural Sub-Saharan Africa, including household and commercial loads, was selected as the case study. The parameters affecting the formation of household and commercial load profiles are considered as input variables, and the total household and commercial load profiles of the microgrid are considered as the target. The Bi-LSTM network is trained by input variables to forecast the microgrid load on an hourly basis by recognizing the consumption pattern. Various performance evaluation indicators such as the correlation coefficient (R), mean squared error (MSE), and root mean squared error (RMSE) are utilized to analyze the forecast results. In addition, in a comparative approach, the performance of the proposed method is compared and evaluated with other methods used in similar studies. The results presented for the training phase show an accuracy of R = 99.81% for the Bi-LSTM network. The test and load forecasting stage are performed by the Bi-STLM network, with an accuracy of R = 99.34% and forecasting errors of MSE = 0.1042 and RMSE = 0.3243. The results confirm the high performance of the proposed Bi-LSTM technique, with a high correlation coefficient when compared to other methods used for short-term load forecasting.

An ERBF Network Prediction of Taiwan’s Crude Birth Rate Taking Economic Growth Rate and National Income Indices as Input Variables

2014 ◽  
Vol 1044-1045 ◽  
pp. 1721-1724
Author(s):  
Yi Ti Tung ◽  
Tzu Yi Pai
Keyword(s):  
Economic Growth ◽  
Growth Rate ◽  
Birth Rate ◽  
Mean Squared Error ◽  
National Income ◽  
Crude Birth Rate ◽  
Percentage Error ◽  
Economic Growth Rate ◽  
Squared Error ◽  
Input Variables

In this study, exact radial basis function (ERBF) network was used to predict Taiwan’s crude birth rate (CBR), taking the economic growth rate (EGR) and national income indices (NII) as input variables. To establish the ERBF network model, the EGR and NII were taken as the input variables, and the CBR was taken as the output variable. The results indicated that the minimum mean absolute percentage error (MAPE), mean squared error (MSE), root mean squared error (RMSE), and maximum correlation coefficient (R) was 0.00 %, 0.00, 0.00, and 1.00, respectively when training. Those for testing were 52.41 %, 27.88, 5.28, and 0.00, respectively. According to the results, ERBF network could predict CBR by taking the EGR and NII as input variables.

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