Maximizing long-term gas industry profits in two minutes in Lotus using neural network methods

• Published in: IEEE transactions on systems, man, and cybernetics Vol. 19; no. 2; pp. 315 - 333
• Main Author: Werbos, P.J.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.03.1989; Institute of Electrical and Electronics Engineers
• Subjects: 03 NATURAL GAS; 032000 - Natural Gas- Transport, Handling, & Storage; 990200 - Mathematics & Computers; 990300 - Information Handling; ANIMAL CELLS; Application software; Applied sciences; ARRAY PROCESSORS; Backpropagation; COMPUTER NETWORKS; COMPUTERIZED SIMULATION; COMPUTERS; DIGITAL COMPUTERS; Economic forecasting; EFFICIENCY; Energy; Energy economics; ENERGY SUPPLIES; Exact sciences and technology; Gas industry; GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; General, economic and professional studies; Industrial training; INDUSTRY; INFORMATION SYSTEMS; Load forecasting; Methodology. Modelling; MICROCOMPUTERS; NATURAL GAS INDUSTRY; NERVE CELLS; Neural networks; OPTIMIZATION; Optimization methods; PARALLEL PROCESSING; PERSONAL COMPUTERS; Predictive models; PROGRAMMING; RESEARCH PROGRAMS; SIMULATION; SOMATIC CELLS; USES; Drilling; Backpropagation; Sales; Benefit; Production; Markets; Neural network; Artificial intelligence; Long term; Modeling; Gas economy; Natural gas
• ISSN: 0018-9472; 2168-2909
• DOI: 10.1109/21.31036

Generalized methods that are commonly used in neural-network research have made it possible for the US Energy Information Administration (EIA) to solve a gas-industry optimization problem on a personal computer that would previously have required a mainframe computer because of the run time required. The resulting model was used to produce EIA's official energy forecasts published in 1988. It is shown how backpropagation can be used by modelers with no special training in neurocomputing. Earlier applications of backpropagation to modeling and to EIA problems are reviewed that antedate the practical applications to neural networks. Finally, the relations between backpropagation, the current EIA model, and economic issues related to modeling and the gas industry are discussed. Among these issues are optimization subject to constraints, and competition and efficiency in gas supply. It is also shown how more recent formulations of backpropagation are a special case of the proposed formulation.< >