Results 111 to 120 of about 127,521 (300)

Application of Deep Learning Long Short-Term Memory in Energy Demand Forecasting

, 2019
The smart metering infrastructure has changed how electricity is measured in both residential and industrial application. The large amount of data collected by smart meter per day provides a huge potential for analytics to support the operation of a ...
D Alahakoon, Damminda Alahakoon, H-T Pao, J Barton, K Kwang-Ho, K Zhao, L Han, MQ Raza, MR Fernández, P Mirowski, P-H Kuo, S Hochreiter, SV Verdu, W Kong, Y Wang   +14 more
core   +1 more source

Short-Term Load Forecasting With Exponentially Weighted Methods

IEEE Transactions on Power Systems, 2012
Short-term load forecasts are needed for the efficient management of power systems. Although weather-based modeling is common, univariate models can be useful when the lead time of interest is less than one day. A class of univariate methods that has performed well with intraday data is exponential smoothing.
openaire   +2 more sources

Re‐Awakening Public Attention to the Silent Pandemic of Cancer Among Older Adults in Low‐ and Middle‐Income Countries

Aging and Cancer, EarlyView.
ABSTRACT As global populations age, cancer is increasingly becoming a leading cause of morbidity and mortality among older adults, particularly in low‐ and middle‐income countries (LMICs). Despite accounting for the majority of new cancer cases and deaths, older individuals remain underrepresented in cancer research, clinical guidelines, and health ...
Ibrahim Bidemi Abdullateef, Sunkanmi Folorunsho, Abdulazeez Opeyemi Abdulganiyu   +2 more
wiley   +1 more source

Wavelet-based short-term load forecasting using optimized anfis [PDF]

, 2016
This paper focuses on forecasting electric load consumption using optimized Adaptive Neuro-Fuzzy inference System (ANFIS). It employs the use of Particle Swarm Optimization (PSO) to optimize ANFIS, with aim of improving its speed and accuracy.
Abubakar, I., Khalid, S. N., Mustafa, M. W., Mustapha, M.   +3 more
core  

Tracking Motor Progression and Device‐Aided Therapy Eligibility in Parkinson's Disease

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To characterise the progression of motor symptoms and identify eligibility for device‐aided therapies in Parkinson's disease, using both the 5‐2‐1 criteria and a refined clinical definition, while examining differences across genetic subgroups.
David Ledingham, Sahana Sathyanarayana, Charlotte B. Stewart, Robyn Iredale, Victoria Foster, Debra Galley, Mark Baker, Nicola Pavese   +7 more
wiley   +1 more source

Enhanced Neuro-Fuzzy Architecture for Electrical Load Forecasting [PDF]

, 2010
Previous researches about electrical load time series data forecasting showed that the result was not satisfying. This paper elaborates the enhanced neuro-fuzzy architecture for the same application.
Ferdinando, Hany, Kuswanto , Henry, Pasila, Felix   +2 more
core   +2 more sources

Prediction of Myasthenia Gravis Worsening: A Machine Learning Algorithm Using Wearables and Patient‐Reported Measures

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background Myasthenia gravis (MG) is a rare disorder characterized by fluctuating muscle weakness with potential life‐threatening crises. Timely interventions may be delayed by limited access to care and fragmented documentation. Our objective was to develop predictive algorithms for MG deterioration using multimodal telemedicine data ...
Maike Stein, Haoqi Sun, Sophie Lehnerer, Lea Gerischer, Maximilian Mönch, Christian Meisel, Andreas Meisel, Pushpa Narayanaswami   +7 more
wiley   +1 more source

Short-Term Load Forecasting Using a Novel Deep Learning Framework

Energies, 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, Rui Wang, Tao Zhang, Yajie Liu, Yabing Zha   +4 more
doaj   +1 more source

Forecasting wholesale electricity prices: A review of time series models [PDF]

In this paper we assess the short-term forecasting power of different time series models in the electricity spot market. We calibrate autoregression (AR) models, including specifications with a fundamental (exogenous) variable - system load, to ...
Weron, Rafal
core   +1 more source

Intelligent short-term load forecasting in Turkey

International Journal of Electrical Power & Energy Systems, 2006
Abstract A method is proposed to forecast Turkey’s total electric load one day in advance by neural networks. A hybrid learning scheme that combines off-line learning with real-time forecasting is developed to use the available data for adapting the weights and to further adjust these connections according to changing conditions.
Ayca Kumluca Topalli, Ismet Erkmen, Ihsan Topalli   +2 more
openaire   +1 more source