Neuro-evolutionary for time series forecasting and its application in hourly energy consumption prediction

摘要

This paper proposed an ensemble methodology comprising neural networks, modified differential evolution algorithm and nonlinear autoregressive network with exogenous inputs (NARX) (called neuro-evolutionary NARX or NE-NARX model) for time series forecasting. In NE-NARX, the structure is designed by connecting the neural model and NARX model, and the weight value connection is optimized by a modified differential evolution algorithm. The effectiveness of the proposed NE-NARX model is tested on two well-known benchmark datasets, including the Canadian lynx and the Wolf sunspot. The proposed model is compared to other models, including the classical backpropagation algorithm, particle swarm optimization, differential evolution (DE) and DE variants. Additionally, an ARIMA model is employed as the benchmark for evaluating the capacity of the proposed model. And then, NE-NARX model is used for hourly energy consumption prediction through comparison with other machine learning models including gated recurrent units, convolutional neural networks (CNN), long short-term memory (LSTM), a hybrid CNN-LSTM and sequence-to-sequence learning. Results show convincingly the superiority of the proposed NE-NARX model over other models.