Reducing Natural Gas Transmission Costs

The Case:

When you use natural gas in Europe you not only pay for the gas itself but you also pay a transition fee to the company that owns the pipes through which the gas reaches you. These fees can severely increase your bill depending on when you decide to pay them.


Paying these fees a year in advance is the cheapest option but that means you have to know exactly how much gas you’ll need for the next 365 days.

The Challenge:

Accurately predicting your demand for gas one year into the future is not an easy task. Gas demand is largely affected by the temperature outside. When it’s cold, people use more gas to heat their homes when it’s warm power plants use more gas to produce electricity to power ACs.


Certain activities such as road repairs can also increase the demand for gas in the short term as gas is used to melt the asphalt. All of these seemingly random events make year ahead forecasting very difficult.

The Solution:

To minimize the cost of gas caused by inaccurate forecasts we’ve developed a two part algorithm. The first part of the algorithm makes a prediction for how much gas will be needed for next year based on historical consumption and weather data.


The second part of the algorithm then takes this forecast and builds up a strategy on how much yearly, quarterly, monthly and daily transit to buy. Since gas demand fluctuates on a daily basis the 2nd part of the algorithm tries to balance its purchases in real time adapting to any sudden changes in temperature or unexpected events.

The Outcome:

To test this algorithm we used 3 years worth of data borrowed from a local gas distributor. The first part of our algorithm managed to predict the gas needs of a methane station operated by the distributor with an error rate of less than 12%.


Once we applied the strategy devised by the 2nd part of our algorithm we managed to lower the transmission costs of the distributor by 25%.