An application of neural networks in microeconomics: Input–output mapping in a power generation subsector of the US electricity industry

Abstract

The use of the artificial neural networks in economics and business goes back to 1950s, while the major bulk of the applications have been developed in more recent years. Reviewing this literature indicates that the field of business benefits from the neural networks in a wide spectrum from prediction to classification, as most of the applications in economics primarily focus on the predictive power of the neural networks. Time series analysis and forecasting, econometrics, macroeconomics constitute the main areas of economics, where there is an increasing interest in application of neural networks. Although their promising contributions to the area of microeconomics, the applications of neural networks in this area are limited in number. This study provides a microeconomic application of an artificial neural network by input–output mapping for 82 US major investor-owned electric utilities using fossil-fuel fired steam electric power generation for the year 1996. We construct a multilayer feed-forward neural network (MFNN) with back-propagation to represent the relationship between a set of inputs and an electricity production as an output. The network is trained and tested by using approximately 80 percent and 20 percent of the data, respectively. The network is trained with 97% accuracy and performance of the network in testing is 96%. Therefore, this network can be used in calculating electricity output for the given inputs in this subsector of the US electricity market, and these estimations can be employed in policy design and planning.