Novel coronavirus (COVID-19) was discovered in Wuhan, China December 2019, and has affected millions of lives worldwide. On 29th April 2020, Malaysia reported more than 5,000 COVID-19 cases; the second highest Southeast Asian region after Singapore. Recently, a forecasting model developed to measure predict cases on daily basis for next 10 days using previously-confirmed cases. A Recurrent Fore...

Accurate short-term load forecasting can ensure the safe and stable operation of power grids, but nonlinear increases complexity forecasting. In order to solve problem modal aliasing in historical data, fully explore relationship between time series characteristics this paper proposes a gated cyclic network model (SSA–GRU) based on sparrow algorithm optimization. Firstly, complementary sets emp...

Novel coronavirus also known as COVID-19 was first discovered in Wuhan, China by end of 2019. Since then, the virus has claimed millions lives worldwide. In 29th April 2020, there were more than 5,000 outbreak cases Malaysia reported Ministry Health (MOHE). This study aims to evaluate trend analysis using Mann-Kendall test, and predict future Recurrent Forecasting-Singular Spectrum Analysis (RF...

Short-term power load forecasting is critical for ensuring system stability. A new algorithm that combines CNN, GRU, and an attention mechanism with the Sparrow to optimize variational mode decomposition (PSVMD–CGA) proposed address problem of effect random fluctuations on accuracy short-term forecasting. To avoid manual selection VMD parameters, adopted by decomposing data into multiple subseq...

Stimulus-specific adaptation (SSA) occurs when neurons decrease their responses to frequently-presented (standard) stimuli but not, or not as much, to other, rare (deviant) stimuli. SSA is present in all mammalian species in which it has been tested as well as in birds. SSA confers short-term memory to neuronal responses, and may lie upstream of the generation of mismatch negativity (MMN), an i...

Abstract Monthly runoff forecasting has always been a key problem in water resources management. As data-driven method, the least square support vector machine (LSSVM) method investigated by numerous studies forecasting. However, selecting appropriate parameters for LSSVM is to obtaining satisfactory model performance. In this study, we propose hybrid monthly forecasting, VMD-SSA-LSSVM short, w...

One of the major issues about operation power systems is prediction load demand. Moreover, forecasting prime concern to system operators. Recently, integration elements, such as renewable energy sources, storages and electricity vehicle, brings more challenges, particularly when there are large fluctuations in cycle. This study concentrates on short-term demand proposes a hybrid method that com...

Forecasting a hydrologic time series has been one of the most complicated tasks owing to the wide range of data, the uncertainties in the parameters influencing the time series and also due to the non availability of adequate data. Recently, Artificial Neural Networks (ANNs) have become quite popular in time series forecasting in various fields. This paper demonstrates the use of ANNs to foreca...

Forecasting a hydrologic time series has been one of the most complicated tasks owing to the wide range of data, the uncertainties in the parameters influencing the time series and also due to the non availability of adequate data. Recently Artificial Neural Networks (ANN) have become quite popular in time series forecasting in various fields. This paper demonstrates the use of ANN to forecast ...