Implementation of ANFIS +NN and nature-inspired optimization algorithms for solar radiation prediction
DOI:
https://doi.org/10.22517/23447214.25682Keywords:
ANFIS, Neural Networks, , Optimization Algorithms, , Solar Radiation Prediction, Fuzzy SystemsAbstract
This article presents a hybrid model that uses ANFIS (adaptive neuro-fuzzy inference system) hybridized with neural networks (NN) and optimized with algorithms based on natural behaviors, specifically ant colony optimization (ACO). The model is designed to predict primary resources in a specific region targeted for the planning and installation of distributed photovoltaic (PV) generation. The solar primary resource depends on the climatic conditions of the region to be evaluated, and its high variability over short periods presents a challenge in planning energy resources that rely on variable sources in the long term. In this study, the behavior of a hybrid ANFIS + NN + ACO model is designed, developed, evaluated, and validated. The methodology, based on data analysis, is detailed. First, work is carried out on climatic databases, which provide guidelines for preprocessing and cleaning. Second, the climatic variables used to predict solar radiation are identified. ANFIS membership functions are then based on the data to capture nonlinearity and extract relationships with predictors. Neural networks support the optimization of the membership functions, and finally, the optimizer refines and evaluates the response. The response is evaluated using metrics that demonstrate the robustness of the model in capturing and processing data. This study contributes by highlighting tools and alternatives to determine energy potential in climatic regions subject to future distributed generation.
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