NEURAL NETWORK FORECASTING MODEL OF ENERGY CONSUMPTION

Natural gas is transported to consumers via pipelines. The outgoing gas flow
along the pipelines is managed and monitored by a metering system. The metering
system must be ensured reliable and dependable at all cost to maintain the billing
integrity between distributors and customers. An existing system in Nur Metering
Station, PETRONAS Gas Berhad (PGB), Kulim is held responsible to calculate the
energy consumption from the sales gas produced. The system consists of a turbine
meter, measuring equipments which are pressure transmitter and temperature
transmitter, gas chromatography and flow computer. However, the system is a
standalone system that does not have any reference system to verify its integrity.
Customers are billed according to the amount of energy consumption calculated and any
error in calculation will cause loss of profit to the company and affect PETRONAS's
business credibility. Therefore a neural network forecasting model of energy
consumption is developed as a verification system. The model will forecast the energy
consumption of outgoing gas flow and compare it with the results of the existing
metering system to ensure the reliability and accuracy of the system. A few models are
developed and the best model is chosen based on the performance indicator. As a result,
the billing integrity between PETRONAS and the customers could be maintained and in
the future if the project is expanded, it will have the potential of saving of millions of
dollars to Malaysian oil and gas companies.