Comparative studies of Statistical and Neural Networks Models for Short and Long Term Load Forecasting: a Case Study in the Brazilian Amazon Power Suppliers

2008 ◽  
pp. 275-286
Author(s):  
Guilherme A. B. Conde ◽  
Ádamo L. De Santana ◽  
Carlos Renato L. FrancÊs ◽  
Cláudio A. Rocha ◽  
Liviane Rego ◽  
...  
Keyword(s):  
Neural Networks ◽  
Comparative Studies ◽  
Load Forecasting ◽  
Brazilian Amazon ◽  
Short And Long Term

Towards a Methodology for Monitoring and Analyzing the Supply Chain Behavior

2011 ◽  
pp. 186-208
Author(s):  
Reinaldo Moraga ◽  
Luis Rabelo ◽  
Alfonso Sarmiento
Keyword(s):  
Neural Networks ◽  
Sensitivity Analysis ◽  
Supply Chain ◽  
System Dynamics ◽  
Eigenvalue Analysis ◽  
Background Information ◽  
Short And Long Term ◽  
And Performance

In this chapter, the authors present general steps towards a methodology that contributes to the advancement of prediction and mitigation of undesirable supply chain behavior within short- and long- term horizons by promoting a better understanding of the structure that determines the behavior modes. Through the integration of tools such as system dynamics, neural networks, eigenvalue analysis, and sensitivity analysis, this methodology (1) captures the dynamics of the supply chain, (2) detects changes and predicts the behavior based on these changes, and (3) defines needed modifications to mitigate the unwanted behaviors and performance. In the following sections, some background information is given from literature, the general steps of the proposed methodology are discussed, and finally a case study is briefly summarized.

scholarly journals Deep Neural Network and Long Short-Term Memory for Electric Power Load Forecasting

2020 ◽  
Vol 10 (18) ◽  
pp. 6489
Author(s):  
Namrye Son ◽  
Seunghak Yang ◽  
Jeongseung Na
Keyword(s):  
Power Consumption ◽  
Short Term Memory ◽  
Load Forecasting ◽  
Power Demand ◽  
Short Term ◽  
Power Load ◽  
Short And Long Term ◽  
Long Short Term Memory ◽  
Electric Power Load

Forecasting domestic and foreign power demand is crucial for planning the operation and expansion of facilities. Power demand patterns are very complex owing to energy market deregulation. Therefore, developing an appropriate power forecasting model for an electrical grid is challenging. In particular, when consumers use power irregularly, the utility cannot accurately predict short- and long-term power consumption. Utilities that experience short- and long-term power demands cannot operate power supplies reliably; in worst-case scenarios, blackouts occur. Therefore, the utility must predict the power demands by analyzing the customers’ power consumption patterns for power supply stabilization. For this, a medium- and long-term power forecasting is proposed. The electricity demand forecast was divided into medium-term and long-term load forecast for customers with different power consumption patterns. Among various deep learning methods, deep neural networks (DNNs) and long short-term memory (LSTM) were employed for the time series prediction. The DNN and LSTM performances were compared to verify the proposed model. The two models were tested, and the results were examined with the accuracies of the six most commonly used evaluation measures in the medium- and long-term electric power load forecasting. The DNN outperformed the LSTM, regardless of the customer’s power pattern.

Short and Long Term Extreme Reliability Analysis Applied to Floating Wind Turbine Design

2011 ◽  
Author(s):  
Timothe´e Perdrizet ◽  
Daniel Averbuch
Keyword(s):  
Wind Turbine ◽  
International Energy Agency ◽  
Energy Agency ◽  
Floating Wind Turbine ◽  
Extreme Response ◽  
Short And Long Term ◽  
Turbine Design ◽  
Wave Induced

This paper describes and exemplifies an efficient methodology to assess, jointly and in a single calculation, the short and long terms failure probabilities associated to the extreme response of a floating wind turbine, subjected to wind and wave induced loads. This method is applied to the realistic case study OC3-Hywind used in phase IV of the IEA (International Energy Agency) Annex XXIII Offshore Code Comparison Collaboration. The key point of the procedure, derived from the outcrossing approach, consists in computing the mean of the outcrossing rate of the floating wind turbine response in the failure domain over both the short term variables and the ergodic variables defining long term parameters.

Long-term exposure to manganese: A case study in a mining community in the western Brazilian Amazon

2010 ◽  
Vol 196 ◽  
pp. S66-S67
Author(s):  
B. Hacon ◽  
S. Hacon ◽  
C. Vega ◽  
L. Viana ◽  
W. Bastos ◽  
...  
Keyword(s):  
Brazilian Amazon ◽  
Mining Community

Short- and long-term energy flux prediction using Multi-Task Evolutionary Artificial Neural Networks

2020 ◽  
Vol 216 ◽  
pp. 108089
Author(s):  
David Guijo-Rubio ◽  
Antonio M. Gómez-Orellana ◽  
Pedro A. Gutiérrez ◽  
César Hervás-Martínez
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Energy Flux ◽  
Term Energy ◽  
Short And Long Term ◽  
Artificial Neural
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