###### Abstract  
In general, reliable PV generation prediction is required to increase complete control quality and avoid potential damage. Accurate forecasting of direct solar radiation trends in PV power production could limit the influence of uncertainties on photovoltaics, enhance organizational dependability, and maximize the utilization factor of the PV systems for something such as an energy management system (EMS) of microgrids. This paper proposes an intelligent prediction of energy production level in large PV plants through AUTO-encoder-based Neural-Network (AUTO-NN) with Restricted Boltzmann feature extraction. Here, the solar energy output may be projected using prior sun illumination and meteorological data. The feature selection and prediction modules use an AUTO encoder-based Neural Network to improve the process of energy prediction (AUTO-NN). Restricted Boltzmann Machines (RBM) can be used during a set of regulations for development-based feature extraction. The proposed model's result is evaluated using various constraints. As a result, the proposed AUTO-NN achieved 58.72% of _RMSE_ (Root Mean Square Error), 62.72% of _nRMSE_ (Normalized Root Mean Square Error), 48.04% of _MaxAE_ (Maximum Absolute Error), 48.66% of (Mean Absolute Error), and 46.76% of (Mean Absolute Percentage Error). energy generation; prediction; photovoltaic plants; feature extraction; neural network +
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Regarding electrical operators, striking a balance across power systems has become a major task. Further challenges that develop as a result of solar energy's unpredictable nature include voltage swings, poor power quality, as well as inertness. The primary technological issues electricity administrators face when combining alternative fuels with the power grid are variability, unpredictability, and parallel activities [7]. As more than just a result, reliable predicting of Photovoltaic solar output is essential for proper microgrids [8]. Photovoltaic prediction is necessary for scheduling, estimating stocks, managing produced electricity supply, improving power grid functioning, lowering costs of generated electrical energy, and managing congestion, and because of the aforementioned power issues currently, solar power forecast has become more crucial in prevalence among solar grid growth [9]. To manage energy variance, research recommends combining storage systems with clean energy forecasts. Storage technologies capture additional power, moderate oscillations, and keep the electricity flowing [10]. This whole article will address and relate various prediction methods for estimating PV output power in two directions: (i) straightforward prediction, which either forecasts additional power utilizing chronological PV energy data, and (ii) indirect predicting, which also utilizes solar insulation accurate forecasts, as well as other weather parameters that directly impact solar PV manufacturing [11]. Related to physical hypotheses about the air, a variety of methodologies enabling PV power prediction have indeed been presented. A good number of the extensively worn physiological model is the NWP [12]. The weather data forecast model is technologically complicated and challenging due to the atmosphere's fluctuation and unexpectedness [13]. Machine learning approaches are becoming more prominent as the area of computer engineering expands as well as its capacity to interact with non-linearity improves [14]. One such study analyses, assesses, and then contrasts several types of panel irradiance estimate algorithms and PV-performing metrics in the domain of PV power predictions [15], in addition to the immediate and oblique PV power forecasting utilizing ML approaches. There are two types of solar power PV panels. One is on a large scale, i.e., on account of its power capacity, in megawatts scale, being installed over a wide area [16]. Most of this electricity is used by the companies that installed them or sold them to power companies.