Survey on Natural Gas Load Forecasting Technology

2012 ◽  
Vol 485 ◽  
pp. 249-252
Author(s):  
Wei Ya Shi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Natural Gas ◽  
Statistical Learning ◽  
Learning Theory ◽  
Statistical Learning Theory ◽  
Load Forecasting ◽  
Future Research ◽  
Forecasting Methods ◽  
Forecasting Method

In this paper, we give a short survey on natural gas load forecasting technology. Because the variation of gas load is influenced by weather and people activity, the traditional forecasting results and the precision is low. The forecasting methods based on statistical learning theory and artificial neural network are discussed in detail. We also give some future research on the forecasting method of gas load.

A comparative study of short-term load forecasting methods in distribution network

2021 ◽  
Vol 15 (1) ◽  
pp. 23-35
Author(s):  
Tuan Ho Le ◽  
◽  
Quang Hung Le ◽  
Thanh Hoang Phan
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Artificial Neural Network ◽  
Arima Model ◽  
Distribution Network ◽  
Load Forecasting ◽  
Short Term ◽  
Load Demand ◽  
Forecasting Methods ◽  
Short Term Load Forecasting

Short-term load forecasting plays an important role in building operation strategies and ensuring reliability of any electric power system. Generally, short-term load forecasting methods can be classified into three main categories: statistical approaches, artificial intelligence based-approaches and hybrid approaches. Each method has its own advantages and shortcomings. Therefore, the primary objective of this paper is to investigate the effectiveness of ARIMA model (e.g., statistical method) and artificial neural network (e.g., artificial intelligence based-method) in short-term load forecasting of distribution network. Firstly, the short-term load demand of Quy Nhon distribution network and short-term load demand of Phu Cat distribution network are analyzed. Secondly, the ARIMA model is applied to predict the load demand of two distribution networks. Thirdly, the artificial neural network is utilized to estimate the load demand of these networks. Finally, the estimated results from two applied methods are conducted for comparative purposes.

scholarly journals AGILE FORECASTING OF DYNAMIC LOGISTICS DEMAND

Transport ◽  
2008 ◽  
Vol 23 (1) ◽  
pp. 26-30 ◽  
Author(s):  
Xin Miao ◽  
Bao Xi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Supply Chain ◽  
Kalman Filter ◽  
Influencing Factors ◽  
Extended Kalman Filter ◽  
Comparison Analysis ◽  
Forecasting Methods ◽  
Forecasting Method ◽  
Logistics Demand

The objective of this paper is to study the quantitative forecasting method for agile forecasting of logistics demand in dynamic supply chain environment. Characteristics of dynamic logistics demand and relative forecasting methods are analyzed. In order to enhance the forecasting efficiency and precision, extended Kalman Filter is applied to training artificial neural network, which serves as the agile forecasting algorithm. Some dynamic influencing factors are taken into consideration and further quantified in agile forecasting. Swarm simulation is used to demonstrate the forecasting results. Comparison analysis shows that the forecasting method has better reliability for agile forecasting of dynamic logistics demand.

scholarly journals LUMPY DEMAND FORECASTING USING LINEAR EXPONENTIAL SMOOTHING, ARTIFICIAL NEURAL NETWORK, AND BOOTSTRAP

2018 ◽  
Vol 10 (2) ◽  
pp. 107
Author(s):  
Sinta Rahmawidya Sulistyo ◽  
Alvian Jonathan Sutrisno
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Inventory Management ◽  
Spare Part ◽  
Service Level ◽  
Exponential Smoothing ◽  
Spare Parts ◽  
Forecasting Methods ◽  
Forecasting Method ◽  
Spare Parts Inventory

Lumpy demand represents the circumstances when a demand for an item has a large proportion of periods having zero demand. This certain situation makes the time series methods might become inappropriate due to the model’s inability to capture the demand pattern. This research aims to compare several forecasting methods for lumpy demand that is represented by the demand of spare part. Three forecasting methods are chosen; Linear Exponential Smoothing (LES), Artificial Neural Network (ANN), and Bootstrap. The Mean Absolute Scaled Error (MASE) is used to measure the forecast performance. In order to gain more understanding on the effect of the forecasting method on spare parts inventory management, inventory simulation using oil and gas company’s data is then conducted. Two inventory parameters; average inventory and service level; are used to measure the performance. The result shows that ANN is found to be the best method for spare part forecasting with MASE of 0,761. From the inventory simulation, the appropriate forecasting method on spare parts inventory management is able to reduce average inventory by 11,9% and increase service level by 10,7%. This result justifies that selecting the appropriate forecasting method is one of the ways to achieve spare part inventory management’s goal.

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