Forecasting natural gas consumption with hybrid neural networks — Artificial bee colony

Abstract

Natural gas distribution companies have different consumer types including manufacturing industry, organized industrial zones, food and beverage industry, household and other low consuming enterprises, etc. Leading two categories of these consumers are household and low consuming enterprises as they have high consumption in winter whereas low in summer. The paper studies consumption demand forecasting for certain consumption group using artificial neural network (ANN). Prepared consumption data is divided into two groups. First three years daily consumption data is kept for training while forth year data is kept for testing. For consumption forecasting its own historical data is used. The research is completed by applying two different model types having eleven different sub-models each. Sub-models have different numbers of neurons and three hidden layers at most. Estimations are done with twenty-two different scenarios in total. In two distinct models, ANN weights are trained with backpropagation (BP) and artificial bee colony (ABC) algorithms. After training stage, network structures are tested by test datasets. As a result, it is concluded that ABC model with two hidden layered scenarios gives better results in demand forecasting than the others.