scholarly journals Short-Term Forecasting of Landings of Ocellate Puffer Takifugu rubripes Migrating around a Spawning Area in the Inland Sea of Japan

1995 ◽  
Vol 61 (3) ◽  
pp. 428-433 ◽  
Author(s):  
Tadashi Tokai ◽  
Ryozo Sato ◽  
Hiroshi Ito ◽  
Takeru Kitahara
Keyword(s):  
Sea Of Japan ◽  
Takifugu Rubripes ◽  
Short Term ◽  
Spawning Area ◽  
Inland Sea Of Japan ◽  
Short Term Forecasting

scholarly journals Year-Class Strength of the Ocellate Puffer around a Spawning Area in the Inland Sea of Japan.

1993 ◽  
Vol 59 (2) ◽  
pp. 245-252 ◽  
Author(s):  
Tadashi Tokai ◽  
Ryozo Sato ◽  
Hiroshi Ito ◽  
Takeru Kitahara
Keyword(s):  
Sea Of Japan ◽  
Spawning Area ◽  
Class Strength ◽  
Inland Sea Of Japan

Diatoms in the Amur Bay sediments (Sea of Japan) and the impact of typhoons on their accumulation over the last 2000 years

2019 ◽  
pp. 252-256
Author(s):  
Irina A. Prushkovskaya ◽  
Ira B. Tsoy
Keyword(s):  
Sea Of Japan ◽  
Short Term ◽  
Catastrophic Events ◽  
Bromine Content ◽  
Amur Bay ◽  
The Impact

The study of diatoms in the sediments of the Amur Bay (Sea of Japan), formed over the last 2000 years, showed that the sharp short-term drops in the concentration of diatoms coincide with the minima of bromine content, which can be explained by the influence of typhoons or other catastrophic events leading to floods and used later in paleoreconstructions.

MODELING AND SHORT-TERM FORECASTING OF TECHNOLOGY COMPONENT OF ELECTRICAL LOAD OF THE REGIONAL ELECTRIC POWER SYSTEM

2016 ◽  
Vol 2016 (4) ◽  
pp. 68-70 ◽  
Author(s):  
P. Chernenko ◽  
◽  
O. Martyniuk ◽  
V. Miroshnyk ◽  
◽  
...  
Keyword(s):  
Power System ◽  
Electric Power ◽  
Electric Power System ◽  
Electrical Load ◽  
Short Term ◽  
Short Term Forecasting

The causes of poverty in Russia

2020 ◽  
Vol 13 (1) ◽  
pp. 21-36
Author(s):  
I.S. Ivanchenko
Keyword(s):  
Regression Analysis ◽  
Regional Development ◽  
Financial Market ◽  
Russian Federation ◽  
Short Term ◽  
Financial Situation ◽  
Macroeconomic Variables ◽  
Development Plans ◽  
The Russian Federation ◽  
Short Term Forecasting

Subject. This article analyzes the changes in poverty of the population of the Russian Federation. Objectives. The article aims to identify macroeconomic variables that will have the most effective impact on reducing poverty in Russia. Methods. For the study, I used the methods of logical, comparative, and statistical analyses. Results. The article presents a list of macroeconomic variables that, according to Western scholars, can influence the incomes of the poorest stratum of society and the number of unemployed in the country. The regression analysis based on the selected variables reveals those ones that have a statistically significant impact on the financial situation of the Russian poor. Relevance. The results obtained can be used by the financial market mega-regulator to make anti-poverty decisions. In addition, the models built can be useful to the executive authorities at various levels for short-term forecasting of the number of unemployed and their income in drawing up regional development plans for the areas.

Very short-term forecasting of wind power generation using hybrid deep learning model

2021 ◽  
Vol 296 ◽  
pp. 126564
Author(s):  
Md Alamgir Hossain ◽  
Ripon K. Chakrabortty ◽  
Sondoss Elsawah ◽  
Michael J. Ryan
Keyword(s):  
Power Generation ◽  
Deep Learning ◽  
Wind Power ◽  
Learning Model ◽  
Wind Power Generation ◽  
Short Term ◽  
Short Term Forecasting ◽  
Deep Learning Model

scholarly journals Improving short-term forecasting during ramp events by means of Regime-Switching Artificial Neural Networks

2011 ◽  
Vol 6 (1) ◽  
pp. 55-58 ◽  
Author(s):  
C. Gallego ◽  
A. Costa ◽  
A. Cuerva
Keyword(s):  
Time Series ◽  
Wind Power ◽  
Regime Switching ◽  
Neural Nets ◽  
Ann Model ◽  
Short Term ◽  
Explanatory Variables ◽  
Ramp Events ◽  
Artificial Neural ◽  
Short Term Forecasting

Abstract. Ramp events are large rapid variations within wind power time series. Ramp forecasting can benefit from specific strategies so as to particularly take into account these shifts in the wind power output dynamic. In the short-term context (characterized by prediction horizons from minutes to a few days), a Regime-Switching (RS) model based on Artificial Neural Nets (ANN) is proposed. The objective is to identify three regimes in the wind power time series: Ramp-up, Ramp-down and No-ramp regime. An on-line regime assessment methodology is also proposed, based on a local gradient criterion. The RS-ANN model is compared to a single-ANN model (without regime discrimination), concluding that the regime-switching strategy leads to significant improvements for one-hour ahead forecasts, mainly due to the improvements obtained during ramp-up events. Including other explanatory variables (NWP outputs, local measurements) during the regime assessment could eventually improve forecasts for further horizons.

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