Input vector selection in NARX models using statistical techniques to improve the generated power forecasting in PV systems

This paper uses collinearity and causality tests to choose variables for an input vector to forecast the electrical power generated by a photovoltaic system. The collinearity test determines redundant variables, and the causality test determines which variables cause the electric power. The chosen input vector is used to train nonlinear autoregressive models with external inputs neural networks (NARX-NN). We develop an algorithm to generate NARX models with an all variable combinations algorithm (AVCA) to validate the results. Finally, we compare the results of the proposed methodology against the best results obtained by the AVCA; the algorithm tests 502 input vectors with the NARX model to forecast 26 steps (a day ahead) of the electrical power. The best model chosen using the collinearity and causality techniques has an RMSE of 308 W for the electric power using four variables in the input vector; the best model using the AVCA has an RMSE of 305 W using five variables in the input vector. Results show that the collinearity and causality techniques are a direct way to select the input vector without affecting the models performance and results in a reduction of the input vector length.