# A Review on Neural Network Based Models for Short Term Solar Irradiance Forecasting
## 2 The Impact Factor of Solar Power Forecasting
### Weather Classification  
Solar radiation is known as the key feature used in the determination of PV power potential. Various meteorological input features influence the availability of solar irradiance, such as cloud types, temperature, relative humidity, wind speed, pressure, and aerosol index. Based on this fact, weather changes can be considered as the main factor that influences solar irradiance forecasting models in terms of accuracy. This result demonstrates that weather classification is critical for forecasting algorithm performance and durability. According to multiple studies, weather classification is the main factor involved at the short-term solar irradiance forecasting pre-processing stage [63; 64; 65]. The lack of data available for model training is considered to be a major roadblock in weather classification. A study reported using 33 weather types that were classified into 10 weather classifications by combining several types of weather into a single type [66]. This limitation can be addressed by classifying weather into three or four types [67; 68]. Accordingly, ref. [34] fed surface weather and irradiance data into K-means clustering to analyze variations in cloud conditions. In addition, regime-dependent ANN models were utilized for solar irradiance forecasting and demonstrated better accuracy. Following that study, few researchershave concentrated on using Self-Organizing Maps (SOM) to classify meteorological input features into different categories for forecasting models [61; 69]. However, before that study, ref. [66] presented four major weather categories based on solar irradiation features for each weather type [66]. In addition, ref. [70] predicted solar irradiance in northeastern Brazil based on an Artificial Neural Network (ANN). The data were divided into two climates--dry and rainy--and the clustering method was used to identify the homogenous climatic zones present.  
The comparisons between the GAN-based approach and the other five established forecasting models were presented in [33]. The study adopted 10 weather classes as the input data, and a confusion matrix was utilized for objectivity [71]. The literature makes several comparisons between weather categorization and photovoltaic power forecasting (PVPF) models, making it difficult for PVPF researchers to select the best option. This statistical tool analyses the effectiveness of classifier algorithms using a table layout to determine how frequently a classifier becomes confused and mislabels two weather classes that are close together. These classifiers include CNN with a 1-D convolution layer (CNN1D), CNN with a 2-D convolution layer (CNN2D), multilayer perceptron (MLP), and SVM with a k-nearest neighbor (KNN). The findings showed better accuracy in weather classification and data augmentation, as well as improved data imbalance weather categories using GAN, especially for short-term sample size. The authors suggested GAN-CNN2D for PVPF modeling, although CNN2D achieved the highest scores due to its ability to eliminate non-linear input-output correlations. Correspondingly, the previous literature emphasized the importance of using weather categorization to ensure the effectiveness and accuracy of solar irradiance forecasting models, as weather conditions should always be prioritized for forecasting [52; 72; 73].