Natural Gas Forecasting - A Comparison of Two Models
Authors: Haenel Richard D. 
Published: December 2007

This paper was prepared for the 48th Annual Fall Meeting of the Society of Petroleum Engineers of AIME, to be held in Las Vegas, Nev., Sept. 30-Oct. 3, 1973. Permission to copy is restricted to an abstract of not more than 300 words. Illustrations may not be copied. The abstract should contain conspicuous acknowledgement of where and by whom the paper is presented. Publication elsewhere after publication in the JOURNAL OF PETROLEUM TECHNOLOGY or the SOCIETY OF PETROLEUM ENGINEERS JOURNAL is usually granted upon request to the Editor of the appropriate journal provided agreement to give proper credit is made.
Discussion of this paper is invited. Three copies of any discussion should be sent to the Society of Petroleum Engineers office. Such discussion may be presented at the above meeting and, with the paper, may be considered for publication in one of the two SPE magazines.

Abstract
During the past few years the Southern California Gas Company has devoted considerable effort to developing an analytical model to forecast daily natural gas sendout on a continuous basis. Many techniques for data reduction and modeling were tried—some successful, some not. It is the purpose of this paper to present the results of this study and to demonstrate these techniques which were found to be most helpful. In essence, the model is based on a multiple stepwise regression procedure with various data weighting methods applied. Beyond this, the success of the model is attributed to the technique applied to data reduction using a forecasting validation procedure and continual updating to avoid model obsolescence.

Introduction
There are 32,000 miles of gas mains in the distribution, transmission and storage of natural gas to service over three million customers in the Southern California area. The annual sales can amount to over one trillion cubic feet of natural gas with a peak demand as high as 3.6 billion cubic feet recorded in 1972. A dispatcher error of about 5% on any given day can mean an average of about ninety million cubic feet which the dispatcher must account for in planning at a later period of time. The time frame in which he reacts to this over or under estimate is extremely important because of the oscillatory effect it will have on planning the next day or the day after utilization of facilities.
In effect, a sizable error in either direction may have a drastic influence on the efficiency of the system and the subsequent revenue obtained from sales of gas.
In order to perform a load forecast, the dispatcher has a rather comprehensive analysis to perform and in a relatively short time. Three major areas are: weather profile, type of day, and customer characteristics. Included in the weather profile are gas load changes caused by temperature, rate of temperature change, wind speed, wind direction, humidity, precipitation, and cloud cover. The type of precipitation, and cloud cover. The type of day category covers corrections for working hours and production rates of commercial and industrial customers and domestic habits of residential customers. The final category, customer characteristics, suggests effects caused by number and growth of customers. To further complicate his problem, there are inherent nonlinearities in each of the above.