Fuzzy inference systems based on multi-type features fusion for intra-hour solar irradiance forecasts

Abstract

The cloud cover information in ground-based cloud (GBC) images is important to direct normal irradiance (DNI) prediction. In order to obtain better performance, the DNI forecasting model needs to incorporate the features from GBC images and numerical time series. In this paper, to resolve the issues of feature fusion with various types, three novel fuzzy inference systems (FIS) models are proposed for intra-hour DNI prediction. The features of the GBC image and the numerical time series are firstly fuzzified by clustering and grid partition, respectively. Then, the hybrid fuzzification and optimization method lead to the presented two hierarchical fuzzy inference systems (HFIS) models and an adaptive neuro-fuzzy inference system (ANFIS) model for DNI prediction. The performance of the proposed models is validated with the data from the National Renewable Energy Laboratory (NREL) from January 1, 2018 to December 31, 2018. Experiments demonstrate that the proposed models outperform the reference model, and the best model is capable of achieving 19.66% improvement over the reference model for 10-minute ahead DNI prediction.