Speaker
Description
Natural gas is primarily used for heating and industrial purposes. Predicting its consumption is of vital importance in gas distribution. It is mostly influenced by weather conditions (mainly temperature), but there is also a human factor which is hard to quantify. An inaccurate prediction, whether higher or lower than the actual consumption, results in unwanted expense for the gas distributor. Therefore, there is a need to create accurate forecasting models for gas consumption.
We primarily focused on Zagreb, whose consumption ranges from 1 to 25 million kWh. It is clear that tomorrow's consumption depends on today's and perhaps, to a lesser extent, on yesterday's consumption, so ARIMA model with regression was imposed as a logical candidate for a model. However, extremely large jumps in consumption occur during the colder period (sometimes up to 50\%) so we had to look for a different approach. Partial improvement was provided using neural networks. We present several different models created along the way and discuss their strengths and weaknesses.
