Results 131 to 140 of about 74,761 (297)
REGCMPNT : A Fortran Program for Regression Models with ARIMA Component Errors [PDF]
RegComponent models are time series models with linear regression mean functions and error terms that follow ARIMA (autoregressive-integrated-moving average) component time series models.
William R. Bell
core +1 more source
Energy Science &Engineering, EarlyView.This study analyzes energy consumption and economic growth across 39 Sub‐Saharan African countries using a PVAR model. Findings reveal that non‐renewable energy and labor force growth stimulate economic growth, while renewable energy does not stimulate economic growth in the short run.
Amadou Cham +4 more
wiley +1 more source
Ensemble Deep Learning–Based Wind Power Forecasting With Self‐Adaptive Osprey Optimization Algorithm
Energy Science &Engineering, EarlyView.Design of Self‐Adaptive Osprey (SAO) algorithm: The novel SAO algorithm is designed by integrating the exploration capability of the conventional Osprey algorithm by including the self‐adaptiveness for enhancing the convergence rate. Ensemble Deep Learning for wind power forecasting: The wind forecasting is employed using the proposed Ensemble learning
Johncy Bai Johnson +3 more
wiley +1 more source
Optimizing support vector machines and autoregressive integrated moving average methods for heart rate variability data correction. [PDF]
MethodsX, 2023 Svane J +3 more
europepmc +1 more source
Energy Science &Engineering, EarlyView.An AI‐driven CNN–LSTM forecasting framework is integrated with HOMER Pro to optimally design a grid‐connected PV–wind–BESS microgrid for a rural school in Bangladesh, achieving 91.7% renewable penetration, low energy cost (0.0397 USD/kWh), and an 81.5% reduction in CO2 emissions. ABSTRACT Hybrid renewable microgrid planning in HOMER Pro often relies on
Robiul Khan +5 more
wiley +1 more source
Forecasting New Tuberculosis Cases in Malaysia: A Time-Series Study Using the Autoregressive Integrated Moving Average (ARIMA) Model. [PDF]
Cureus, 2023 Ab Rashid MA +3 more
europepmc +1 more source
Kick Risk Forecasting and Evaluating During Drilling Based on Autoregressive Integrated Moving Average Model [PDF]
, 2019 Hu Yin +4 more
openalex +1 more source
Energy Consumption and CO2 Emissions Forecasting of Transport Sector Using Machine Learning
Energy Science &Engineering, EarlyView.The transport sector accounts for approximately one‐quarter of Iran's final energy consumption. The energy demand in this sector has the least variation, with petroleum products accounting for more than 85% of the demand. Furthermore, the accelerated growth of energy consumption and the sector's reliance on fossil fuels, which are the main cause of ...
Amir Hossein Akbari +2 more
wiley +1 more source
Energy Science &Engineering, EarlyView.The proposed framework operates as a continuous cycle: organizational data streams feed into predictive optimization, which generates energy efficiency targets. These targets are translated into behavioral directives through human resource management mechanisms.
Huang Juan, Aimi Binti Anuar
wiley +1 more source