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Title: Forecasting Daily Sendout Demand With Artificial Neural Networks
Author: Ronald H. Brown, Timothy m. Richardson, John E. Buchanan
Source: American Gas Association 1999
Year Published: 1999
Abstract: The Local Distribution Company (LDC) faces many challenges in the business of supplying gas to their customers. The gas supply system of an LDC consists of gate stations, compressors, gas storage, and customers. The LDC must operate these systems to assure delivery of gas in adequate volumes at required pressures under all circumstances. For efficient, economical, and safe operation, the daily gas sendout demanded by the customers must be known with some degree of accuracy. The customer base of the LDC consists of many individual customers, each with unique demand characteristics. Customers use gas for space heating, known as heating load, for heating water, drying, cooking and baking, and other processes, known as base load, and for electric power generation. The customer base is generally divided into three categories: residential, commercial, and industrial. The demand characteristics of these three categories differ significantly. The residential customer demands are typically temperature sensitive, increasing on weekends. The commercial customers are also typically temperature sensitive, but decreasing on weekends. Industrial customer demand is much less temperature sensitive, decreasing significantly on weekends.

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