###### Abstract  
Solar energy is one of the most important renewable energies, with many advantages over other sources. Many parameters affect the electricity generation from solar plants. This paper aims to study the influence of these parameters on predicting solar radiation and electric energy produced in the Salt-Jordan region (Middle East) using long short-term memory (LSTM) and Adaptive Network-based Fuzzy Inference System (ANFIS) models. The data relating to 24 meteorological parameters for nearly the past five years were downloaded from the MeteobeBleu database. The results show that the influence of parameters on solar radiation varies according to the season. The forecasting using ANFIS provides better results when the parameter correlation with solar radiation is high (i.e., Pearson Correlation Coefficient PCC between 0.95 and 1). In comparison, the LSTM neural network shows better results when correlation is low (PCC in the range 0.5-0.8). The obtained RMSE varies from 0.04 to 0.8 depending on the season and used parameters; new meteorological parameters influencing solar radiation are also investigated. solar energy forecasting: Pearson Correlation Coefficient PCC; principal component analysis filter; deep learning LSTM; machine learning ANFIS +
Footnote †: journal: Future Internet  
solar radiation and environmental parameters is through the wide application of Machine Learning (ML) or Deep Learning (DL) techniques, providing an accurate prediction of the energy that will be produced. Therefore, different databases worldwide have been collected and generated to aid researchers in generating the best conditions and methods for forecasting solar radiation. Thus, the location, weather specifications, ML algorithm, and the number of selected parameters play an important role in forecasting operations. In other words, the forecasting procedure can be different from place to place, depending on which parameters are used and their total number. For this reason, project lenders and designers must rely on accurate and dependable forecasting models to protect their investments. Jordan primarily depends on imported crude oil for electricity generation, becoming heavily dependent on imports to meet its energy needs. The local energy produced in 2020 (by natural gas, crude oil, and renewable energy) reached around 610 ktoe (thousand tons of oil equivalent), representing only 6% of the energy needed to meet demands. The cost of imported energy is as much as 19% of GDP (Gross Domestic Product). The shortage in electrical energy is covered by importing energy from Egypt through a submarine cable in the seasons of high demand for electrical energy. Jordan has been blessed with bountiful solar energy located within the Sunbelt. The intensity of direct solar irradiance in Jordan is one of the highest globally, with an annual daily average of 4-8 kWh/m\({}^{2}\), which corresponds to 1400-2300 kWh/m\({}^{2}\) per year. The sun shines on more than 300 days per year. According to the national strategy, the Jordanian government plans to boost electricity generation capacity from renewable sources to 3.22 GW by 2025. The peak load reached 4010 MW in 2022. The production of electrical energy grew at an average rate of about 12% during the last 20 years. The Government of Jordan is keen to expand solar energy usage domestically for small- and large-scale solar power generation. It is taking important steps to make solar energy a major contributor to overall energy needs, such as the legislation, the compulsory energy efficiency code, and incentives for investing in the solar energy sector. However, compared to dispatchable plants, the variability of renewable sources, including solar energy, creates a major reliability problem for any electrical power system. Two of the most difficult aspects of integrating renewables into the Jordanian power system are unpredictability and intermittent electricity delivery. Therefore, solar power forecasting is very important for stable electric grid operation and optimal dispatch.