Results 21 to 30 of about 982,283 (286)

Load Forecasting Techniques for Power System: Research Challenges and Survey

IEEE Access, 2022
The main and pivot part of electric companies is the load forecasting. Decision-makers and think tank of power sectors should forecast the future need of electricity with large accuracy and small error to give uninterrupted and free of load shedding ...
Naqash Ahmad, Yazeed Ghadi, Muhammad Adnan, Mansoor Ali   +3 more
doaj   +1 more source

Evaluating Gaussian processes for matched-field processing localization using minimum mean squared error criterion [PDF]

JASA Express Letters
Gaussian processes (GPs) can densify and denoise sparsely sampled signals and have been applied in matched-field processing (MFP) localization to improve localization accuracy and robustness.
Shanru Lin, Haiqiang Niu, Peter Gerstoft, Zhenglin Li, Yonggang Guo   +4 more
doaj   +1 more source

A Prediction Model of Power Consumption in Smart City Using Hybrid Deep Learning Algorithm

JOIV: International Journal on Informatics Visualization, 2023
A smart city utilizes vast data collected through electronic methods, such as sensors and cameras, to improve daily life by managing resources and providing services. Moving towards a smart grid is a step in realizing this concept.
Salam Abdulkhaleq Noaman, Ali Mohammed Saleh Ahmed, Aseel Dawod Salman   +2 more
doaj   +1 more source

An Alternative Ratio-cum-Product Estimator of Population Mean Using a Coefficient of Kurtosis for Two Auxiliary Variates

Data Science Journal, 2010
An alternative ratio-cum-product estimator of population mean using the coefficient of kurtosis for two auxiliary variates has been proposed. The proposed estimator has been compared with a simple mean estimator, the usual ratio estimator, a product ...
Rajesh Tailor, Med Ram Verma, Balkishan Sharma   +2 more
doaj   +1 more source

ABCD Neurocognitive Prediction Challenge 2019: Predicting individual fluid intelligence scores from structural MRI using probabilistic segmentation and kernel ridge regression [PDF]

, 2019
We applied several regression and deep learning methods to predict fluid intelligence scores from T1-weighted MRI scans as part of the ABCD Neurocognitive Prediction Challenge (ABCD-NP-Challenge) 2019.
A Pfefferbaum, A Rakotomamonjy, A Rao, A Woolgar, AMJ MacLullich, BJ Casey, C Blaiotta, CE Rasmussen, G Varoquaux, GC Monté-Rubio, GD Batty, GD Batty, IJ Deary, IJ Deary, IJ Deary, J Ashburner, J Gläscher, J Schrouff, J Schrouff, JP Rushton, KL Narr, LS Gottfredson, MA McDaniel, ME Tipping, NA Goriounova, Natacha Akshoomoff, NC Andreasen, Neil P. Oxtoby, RB McCall, Rex E. Jung, S Fors, S Karama, SB Blumberg, T Rohlfing, W Johnson   +34 more
core   +2 more sources

A weighted error-minimizer parameter estimation technique for one-inflated positive Poisson distribution

Results in Control and Optimization
An error-minimizing estimator is always preferred in model fittings. However, each error-minimizing estimator minimizes error differently. This paper combines four error-minimizing estimators, which are root mean-squared error, mean absolute error, root ...
Razik Ridzuan Mohd Tajuddin
doaj   +1 more source

Estimating a Bounded Normal Mean Relative to Squared Error Loss Function [PDF]

Journal of Sciences, Islamic Republic of Iran, 2011
Let be a random sample from a normal distribution with unknown mean and known variance The usual estimator of the mean, i.e., sample mean is the maximum likelihood estimator which under squared error loss function is minimax and admissible ...
A. Karimnezhad
doaj  

Exact Mean Integrated Squared Error

The Annals of Statistics, 1992
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Marron, J. S., Wand, M. P.
openaire   +3 more sources

Convergence of statistical moments of particle density time series in scrape-off layer plasmas [PDF]

, 2015
Particle density fluctuations in the scrape-off layer of magnetically confined plasmas, as measured by gas-puff imaging or Langmuir probes, are modeled as the realization of a stochastic process in which a superposition of pulses with a fixed shape, an ...
O. E. Garcia, Pecseli H. L., R. Kube
core   +2 more sources

Comparative Analysis Using Multiple Regression Models for Forecasting Photovoltaic Power Generation

Energies
Effective machine learning regression models are useful toolsets for managing and planning energy in PV grid-connected systems. Machine learning regression models, however, have been crucial in the analysis, forecasting, and prediction of numerous ...
Burhan U Din Abdullah, Shahbaz Ahmad Khanday, Nair Ul Islam, Suman Lata, Hoor Fatima, Sarvar Hussain Nengroo   +5 more
doaj   +1 more source