scholarly journals Oil Demand Forecasting in Malaysia in Transportation Sector Using Artificial Neural Network

2019 ◽  
Vol 5 (01) ◽  
Author(s):  
Ali Mubarak Al-Qahtani ◽  
Jebaraj S.
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Error Analysis ◽  
Model Validation ◽  
Forecasting Model ◽  
Demand Forecast ◽  
Analysis Techniques ◽  
Transportation Sector ◽  
Oil Demand ◽  
Artificial Neural

Energy industry in Malaysia is one of critical sector that plays a major role in contributing the nation economic and industrial growth. A forecasting model is required to be developed to provide the oil demand forecast in transportation sector. This research analyses different forecasting models including Artificial Neural Network (ANN) model to predict the future oil demand in transportation sector in Malaysia. In order to select the best forecasting model, the model validation is done using the error analysis techniques. Based on the model validation result, it is found that the Artificial Neural Network (Multivariate) model gives the least error in all the error analysis techniques. The model forecast that the oil demand in transportation sector in Malaysia for the year 2020, 2025 and 2030 would be 559.44, 581.779 and 609.941 kg of oil equivalent respectively.

scholarly journals Operational Probabilistic Forecasting of Coastal Freak Waves by Using an Artificial Neural Network

2020 ◽  
Vol 8 (3) ◽  
pp. 165
Author(s):  
Dong-Jiing Doong ◽  
Shien-Tsung Chen ◽  
Ying-Chih Chen ◽  
Cheng-Han Tsai
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Forecasting Model ◽  
Probabilistic Forecasting ◽  
Freak Waves ◽  
Forecasting Models ◽  
Artificial Neural

Coastal freak waves (CFWs) are unpredictable large waves that occur suddenly in coastal areas and have been reported to cause casualties worldwide. CFW forecasting is difficult because the complex mechanisms that cause CFWs are not well understood. This study proposes a probabilistic CFW forecasting model that is an advance on the basis of a previously proposed deterministic CFW forecasting model. This study also develops a probabilistic forecasting scheme to make an artificial neural network model achieve the probabilistic CFW forecasting. Eight wave and meteorological variables that are physically related to CFW occurrence were used as the inputs for the artificial neural network model. Two forecasting models were developed for these inputs. Model I adopted buoy observations, whereas Model II used wave model simulation data. CFW accidents in the coastal areas of northeast Taiwan were used to calibrate and validate the model. The probabilistic CFW forecasting model can perform predictions every 6 h with lead times of 12 and 24 h. The validation results demonstrated that Model I outperformed Model II regarding accuracy and recall. In 2018, the developed CFW forecasting models were investigated in operational mode in the Operational Forecast System of the Taiwan Central Weather Bureau. Comparing the probabilistic forecasting results with swell information and actual CFW occurrences demonstrated the effectiveness of the proposed probabilistic CFW forecasting model.

scholarly journals Weather Forecasting Model using Artificial Neural Network

2012 ◽  
Vol 4 ◽  
pp. 311-318 ◽  
Author(s):  
Kumar Abhishek ◽  
M.P. Singh ◽  
Saswata Ghosh ◽  
Abhishek Anand
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Weather Forecasting ◽  
Forecasting Model ◽  
Artificial Neural
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