## 1 Introduction
Based on the indicators, the forecasting models based on the ANN showed better performance than all other models. When comparing DL and the ANN in terms of solar power output prediction, it was found that the ANN was better than DL in predicting the output power of the photovoltaic system. On the other hand, when comparing the different machine learning algorithms (SVM and k-NN), it was highlighted that SVM was better than k-NN in predicting the photovoltaic power output. Previous studies on solar power output prediction typically have employed various algorithms to calculate the output power of photovoltaic systems. The selection of multiple algorithms for output power prediction depends on the input data. The performance of the forecasting algorithms may vary depending on the models developed by the authors. In general, however, all models give very similar results in terms of evaluation metrics. Therefore, a more significant number of metrics would allow for a better comparison when evaluating the forecasting models [31]. There are two main types of forecasting for the characterization of a photovoltaic system: direct and indirect. We train a direct PV power forecasting system using existing data. On the other hand, indirect forecasting estimates the performance depending on system parameters, such as solar radiation and temperature, which are not directly related to the PV system parameters. Our work was further motivated by the lack of a comprehensive review of the latest advances in parameter forecasting for photovoltaic systems. The uncertainty in the available solar resources is challenging for a reliable forecasting system. In the case of economic analyses, researchers mainly focus on probabilistic forecasts rather than regression analysis. Researchers have found that even statistical techniques outperform traditional parametric methods. A convolutional Neural Network (CNN) best collaborates with other machine learning methods for short-term forecasting. Therefore, these works show that researchers have been using PV performance forecasting methods for a long time. In terms of its contribution, this paper not only significantly reduces the time needed to search for machine learning-based papers for PV system parameter prediction but also serves as an initial reference for individuals embarking on their PV system research [32]. We optimize the PV size and location by combining short time series and GAs for the analysis and using fuzzy set theory. This results in cost savings of up to 31% by minimizing the production and distribution costs of the microgrid system. Most models rely on several characteristics related to the PV module and several meteorological attributes. However, in most cases, the performance and quality of a photovoltaic module are determined by four factors that are influenced by the intensity of solar radiation and the temperature of the module. Standard tests are usually conducted under specific parameters, including an air mass equivalent to 1.5, a cell temperature maintained at 25 degrees Celsius, and an irradiance spectrum of 1000 W/m\({}^{2}\). However, keeping these standards in real life is quite a challenge. As a result, test results vary and may be inaccurate. Another common problem is that a significant quantity of data is required before regression analysis. In addition, limited computing capacity hinders the testing procedure. This limitation can lead to inaccurate results due to insufficient data and processing resources. Different models focus on the thermal characteristics of photovoltaic modules in addition to these electrical equivalent models. While some models are based on heat capacity, others are based on the total heat loss coefficient. However, since manufacturers do not provide adequate details about these functions, these models may not be reliable. Our paper uses multi-objective optimization based on genetic algorithms and time series to find the best parameter values for short-term forecasting models that use fuzzy set theory tools within a reasonable time [33].