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Natural gas load forecasting with combination of adaptive neural networks

The focus of this paper is on combination of artificial neural network (ANN) forecasters with application to the prediction of daily natural gas consumption needed by gas utilities. A two-stage system is proposed with the first stage containing three ANN forecasters. The first forecaster is a multilayer feedforward network trained with backpropagation, the second one is another multilayer feedforward network trained with Levenberg-Marquad algorithm, and the third one is a one-layer functional link network These three separate forecasts are nonlinearly combined in the second stage using a functional link ANN combiner. A scheme is introduced to make all of the ANNs adaptive, with their weights changing throughout the forecasting phase. The performance is tested on real data from four different gas utilities for a period of several months. The results show that the proposed forecast combination approach does result in more accurate forecasts compared to using a single forecaster. The overall performance of the system is also quite good from an operational point of view.