Results 141 to 150 of about 14,317 (308)

A Short-Term Load Forecasting Method Considering Multiple Factors Based on VAR and CEEMDAN-CNN-BILSTM

Energies
Short-term load is influenced by multiple external factors and shows strong nonlinearity and volatility, which increases the forecasting difficulty. However, most of existing short-term load forecasting methods rely solely on the original load data or ...
Bao Wang, Li Wang, Yanru Ma, Dengshan Hou, Wenwu Sun, Shenghu Li   +5 more
doaj   +1 more source

An Attention-Based Multilayer GRU Model for Multistep-Ahead Short-Term Load Forecasting^†. [PDF]

Sensors (Basel), 2021
Jung S, Moon J, Park S, Hwang E.
europepmc   +1 more source

Comparing the Effect of Semi‐Immersive Virtual Reality, Computerized Cognitive Training, and Traditional Rehabilitation on Cognitive Function in Multiple Sclerosis: A Randomized Clinical Trial

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background Cognitive impairment is a common non‐motor symptom in Multiple Sclerosis (MS), negatively affecting autonomy and Quality of Life (QoL). Innovative rehabilitation strategies, such as semi‐immersive virtual reality (VR) and computerized cognitive training (CCT), may offer advantages over traditional cognitive rehabilitation (TCR ...
Maria Grazia Maggio, Desiree Latella, Lilla Bonanno, Paulo Cezar Rocha dos Santos, Marcos Maldonado‐Díaz, Meir Plotnik, Rosaria De Luca, Angelo Quartarone, Rocco Salvatore Calabrò   +8 more
wiley   +1 more source

The Short-Term Load Forecasting Using an Artificial Neural Network Approach with Periodic and Nonperiodic Factors: A Case Study of Tai'an, Shandong Province, China. [PDF]

Comput Intell Neurosci, 2021
Sun J, Dong H, Gao Y, Fang Y, Kong Y.
europepmc   +1 more source

White Matter Hyperintensity Burden and Short‐Interval Change Associated With Sleep Apnoea in the UK Biobank

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background and Purpose White matter hyperintensities (WMH) are a core neuroimaging marker of cerebral small vessel disease (CSVD). Sleep apnoea (SA) is a recognized vascular risk factor, but its associations with regional WMH burden, short‐interval WMH change and cognitive performance in population‐based cohorts remain incompletely defined. We
Peng Cheng, Diego Z. Carvalho, Shahid Karim, Anwar Chahal, Virend K. Somers   +4 more
wiley   +1 more source

Short-term load estimation based on improved DBN-LSTM

Sustainable Energy Research
Aiming at the rapid change and low forecasting accuracy of short-term power load forecasting, a forecasting model based on the improved deep belief network and long short-term memory network is proposed.
Nan Dong, Yuwen Wu, Buyun Su, Zhanzhi Liu   +3 more
doaj   +1 more source

Optimizing Models and Data Denoising Algorithms for Power Load Forecasting

Energies
To handle the data imbalance and inaccurate prediction in power load forecasting, an integrated data denoising power load forecasting method is designed.
Yanxia Li, Ilyosbek Numonov Rakhimjon Ugli, Yuldashev Izzatillo Hakimjon Ugli, Taeo Lee, Tae-Kook Kim   +4 more
doaj   +1 more source

Seasonal dynamic factor analysis and bootstrap inference : application to electricity market forecasting [PDF]

Year-ahead forecasting of electricity prices is an important issue in the current context of electricity markets. Nevertheless, only one-day-ahead forecasting is commonly tackled up in previous published works.
Carolina Garcia-Martos, Julio Rodriguez, andrés M. Alonso, Maria Jesus Sanchez   +3 more
core  

Load Forecasting Model Using LSTM for Indian State Load Dispatch Centre

This paper presents an approach to address the critical challenge of load forecasting in the Indian state of Odisha. Motivated by the necessity for accurate predictions to support efficient planning and operation of the power system network, the work ...
Matushree Kochar, Mohammad Pazoki, Anamika Yadav, Rashmi Bareth   +3 more
core   +1 more source

Short-Term Load Forecasting Using a Novel Deep Learning Framework

, 2018
Short-term load forecasting is the basis of power system operation and analysis. In recent years, the use of a deep belief network (DBN) for short-term load forecasting has become increasingly popular. In this study, a novel deep-learning framework based
Xiaoyu Zhang, Yabing Zha, Tao Zhang, Yajie Liu, Rui Wang   +4 more
core   +1 more source