Model izbora optimalne kratkoročne strategije pivovare (An Optimal Short-Term Strategy Selection Model for a Brewery)

2013 ◽  
Author(s):  
Zoran Lukić
Keyword(s):  
Selection Model ◽  
Strategy Selection ◽  
Short Term

scholarly journals Short-Term Load Forecasting Algorithm Using a Similar Day Selection Method Based on Reinforcement Learning

Energies ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 2640 ◽  
Author(s):  
Rae-Jun Park ◽  
Kyung-Bin Song ◽  
Bo-Sung Kwon
Keyword(s):  
Reinforcement Learning ◽  
Predictive Accuracy ◽  
Learning Algorithm ◽  
Load Forecasting ◽  
Selection Method ◽  
Selection Model ◽  
Short Term ◽  
Short Term Load Forecasting ◽  
Similar Day ◽  
Reinforcement Learning Algorithm

Short-term load forecasting (STLF) is very important for planning and operating power systems and markets. Various algorithms have been developed for STLF. However, numerous utilities still apply additional correction processes, which depend on experienced professionals. In this study, an STLF algorithm that uses a similar day selection method based on reinforcement learning is proposed to substitute the dependence on an expert’s experience. The proposed algorithm consists of the selection of similar days, which is based on the reinforcement algorithm, and the STLF, which is based on an artificial neural network. The proposed similar day selection model based on the reinforcement learning algorithm is developed based on the Deep Q-Network technique, which is a value-based reinforcement learning algorithm. The proposed similar day selection model and load forecasting model are tested using the measured load and meteorological data for Korea. The proposed algorithm shows an improvement accuracy of load forecasting over previous algorithms. The proposed STLF algorithm is expected to improve the predictive accuracy of STLF because it can be applied in a complementary manner along with other load forecasting algorithms.

Is Openness Penalized? Stock Returns around Earnings Warnings

2007 ◽  
Vol 82 (4) ◽  
pp. 1055-1087 ◽  
Author(s):  
Jennifer W. Tucker
Keyword(s):  
Stock Returns ◽  
Selection Bias ◽  
Selection Model ◽  
Bad News ◽  
Short Term ◽  
Heckman Selection Model ◽  
Earnings Announcement ◽  
Self Selection ◽  
Earnings Warnings ◽  
Heckman Selection

Prior research finds that firms warning investors of an earnings shortfall experience lower returns than non-warning firms with similar risks and earnings news. Openness thus appears to be penalized by investors. Yet, this finding may be due to a self-selection bias that occurs when firms with a larger amount of unfavorable non-earnings news (“other bad news”) are more likely to warn. In this paper I use a Heckman selection model to infer the amount of other bad news and document that, on average, warning firms have a larger amount of other bad news than non-warning firms. After controlling for this effect, I find that warning firms' returns remain lower than those of non-warning firms in a short-term window ending five days after earnings announcement. When this window is extended by three months, however, warning and non-warning firms exhibit similar returns. My evidence suggests that openness is ultimately not penalized by investors.

Strategy selection model for network survivability based on stochastic game

2013 ◽  
Vol 32 (9) ◽  
pp. 2609-2612
Author(s):  
Xiao LIANG ◽  
Xiang-ru MENG ◽  
Xu-chun ZHUANG ◽  
Wen WU
Keyword(s):  
Stochastic Game ◽  
Network Survivability ◽  
Selection Model ◽  
Strategy Selection
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