[PERSON] and [PERSON] used a reference physical radiative transfer model [33]. [PERSON] et al., evaluated solar irradiance modeling uncertainty with the estimation of multiple confidence intervals [34]. [PERSON] et al., presented a short-term forecasting method with satellite model coupling [35]. [PERSON] et al., showed a prediction method using ray-tracing techniques [36]. [PERSON] and [PERSON] presented a solar energy prediction method using linear and non-linear regularization models [37]. [PERSON] and [PERSON] also presented a solar irradiance prediction method using weather forecasts [8]. This study explored deduction strategies for solar irradiance and sensitivity analysis based on actual local climatic parameters using four prediction algorithms: linear regression, support vector machines (SVM), a multi-layer neural network (MLNN), and a long short-term memory (LSTM) network. The comparison is illustrated through impact factor and addressed. We evaluated the performance of these four models using weather data collected over eight years (70,080 hourly data points) as a training set, and one year of data (8760 hourly data points) as a test set. The obtained deduction results were compared with real solar irradiance values. Therefore, this study hypothetically estimates that if weather condition is predictable, solar irradiance is also deduced using ANNs methodologies. To investigate how each environmental element influences the solar irradiance in a city depending on seasonal changes, we also analyzed the performance sensitivity using the four prediction models. From impact factor analysis, we could determine which climate factors strongly influence the solar irradiance, and which factors can be neglected when considering solar irradiance prediction. This study shows the relationship between climate factors and solar irradiance in the four models examined. In this paper, we aim to predict solar irradiance with environmental parameters. Solar irradiance is mainly affected by the angle of incidence and is also influenced by cloud coverage and seasonal changes such as temperature and humidity ratio. This means that machine learning (ML) using an artificial neural network (ANN) structure makes it possible to estimate solar irradiance using satellite data [38; 39]. Some previous reports have already discussed the prediction of building energy using environmental elements as inputs [40; 41]. Further, it has been reported that the most influential factors for determining electricity usage are temperature and working day information [4]. However, getting information on solar irradiance is limited because the available information is not decisive. This study collected weather data from Seoul, which is the capital and a mega city in South Korea, from the Korea Metrological Administration (KMA) [42] for research purposes. The location of observatory station in Seoul is Songwol-dong, Jongno-gu, Seoul. We used long-term historical hourly weather data for a period of nine years (2008-2016) and the main elements of the weather conditions are temperature, humidity ratio, air pressure, global horizontal irradiance (GHI), cloud data, and wind speed and direction. ANNs training with long term eight-year historical data and solar irradiance data will estimate one-year solar irradiance and the results will be analyzed. Additionally, this study evaluates accuracy of the deducted solar irradiance compared with measured solar irradiance by variable linear and nonlinear data-driven approaches. In the results, this study could define which of the weather elements can significantly impact on the solar irradiance and which elements could be neglected to obtain the information of solar irradiance. Therefore, using the considered weather elements, ANNs can be used to show how each weather parameter or climate changes impact on amount of solar irradiance in the future. Weather is chaotic and in principle not reliably predictable, however there are several works whose solar irradiance prediction based on weather forecasts [8; 14].