Summary
This study presents different types of neural network algorithm based model forecasting gas consumption for residential and commercial consumers in Istanbul in Turkey. Using seven neural networks algorithms as forecasting models, we tried to find the best solution on forecasting of monthly natural gas consumption. These models were validated and tested on real monthly data from a distribution area covering two different regions of Anatolian and European sides in Istanbul. The analysis of results obtained for training and test sets show that the seven proposed artificial neural network models could be useful for the natural gas consumption forecast problem. It was shown that a conjugate gradient descent neural network model presented a more efficient solution than the other models.
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Extract
Combination of Neural Networks Forecasters for Monthly Natural Gas Consumption Prediction
1. Introduction
The main objective of an energy distribution system is efficient and reliable distribution of energy from the source to the client. Efficient operation of modern energy distribution systems often requires forecasting the future energy demand [1-2] . Accurate forecasting of natural gas consumption for a specific distributive area is of great importance for economical and reliable operation of distributive network. Increasing of natural gas share in fulfillment of energy demand has been a strategic target in Turkey energy policy for a long time. The natural gas market requires forecasting for the optimization of leasing additional storage capacities. Consequently, natural gas distribution companies have an economic stimul...See the full content of this document
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