Neuroevolution of recurrent neural networks for time series forecasting of coal-fired power plant operating parameters

Abstract

This work presents how the Evolutionary eXploration of Augmenting Memory Models (EXAMM) neuroevolution algorithm is incorporated into Microbeam Technologies' condition-based monitoring power plant optimization software using a workflow that integrates coal-fired power plant data collection, evolved RNN predictions and analytic performance indices predictions. To the authors' knowledge, it is the first use of a neuroevolution strategy to evolve recurrent neural networks (RNNs) for forecasting of power plant parameters where the evolved networks have been incorporated into production software used at a coal-fired power plant. A preliminary exploration of the plant's performance shows that after incorporating this software, the amount of revenue lost due to power plant derates and outages decreased by $7.3 million, a savings of 42%, and increased efficiency under medium and low load conditions. A further investigation of the effect of training sequence length and time series data normalization methods on evolving and training RNNs for this system is given, providing practical results useful for real world time series forecasting. It is shown that dividing long time series sequences up into shortened training sequences can dramatically speed up training, and that using different normalization methods (min-max vs. z-score) can provide statistically significant results, dependent on the data sets.