## 1 Introduction  
The expansion of the renewable energy sector is rapidly occurring in response to the pressing need to address climate change and depletion of carbon-intensive energy sources [1]. Additionally, finite reserves of fossil fuels such as coal, oil, and natural gas contribute to the greenhouse effect when converted into electricity, exacerbating a significant environmental concern [2]. Among the various renewable energy sources, photovoltaic (PV) solar energy plays a critical role in providing clean energy as it does not emit carbon and reduces dependence on fossil fuels in the development of the economy and society. The utilization of PV technology for power generation has witnessed a significant expansion, thereby assuming a pivotal position in the international energy industry. The large-scale PV market is characterized by narrow profit margins and aggressive competition. The challenges faced in this sector are significant and the need for innovative solutions is pressing. A decrease in performance can significantly affect a project's overall profitability. Therefore, achieving maximum performance is essential for ensuring long-term profitability [3]. Considerable focus has been directed towards online predictive maintenance [4] and advanced energy forecasting methods for PV installations [5]. Accurate forecasting has gained importance in ensuring a balance between energy supply and demand, owing to the intermittent and difficult-to-control nature of renewable energy generation caused by high variability in weather parameters. Accurate prediction of PV power outputs is essential for more reliable and precise management of grid systems, benefiting planners, decision-makers, power plant operators, and grid operators [6]. The problem of PV power forecasting (PVPF) has been studied extensively. A wide variety of forecasting solutions have been proposed that typically exploit weather stations and/or satellites to provide rich metadata that methods can rely on. The data assimilation technique analyzes patterns of satellite information and actual climate conditions. Numerical weather prediction (NWP) and satellite data are often unavailable and weather stations are scarce, making the weather station nearest to a PV plant potentially unreliable [7]. In addition, geostationary satellite data are expensive and not always readily accessible [8]. In these scenarios, the only available data are the power measurements generated by smart meters for each plant [9]. Therefore, a different approach is required for implementing the PVPF method. This approach involves analyzing the historical data of active power production to make more precise predictions with less computational complexity, fewer input parameters, and longer time dependencies. Time-series models include statistical approaches, such as the Holt-Winters (HW) exponential smoothing method, which is an effective approach for forecasting seasonal time series, particularly for scenarios with limited historical data. The HW method is well known for its high forecasting accuracy and low demand for training data, making it a useful choice for newly installed PV plants. However, the HW method has limitations for long-range prediction. The Long Short-Term Memory (LSTM) model is a highly useful tool for conducting time-series analyses with long-range dependencies and is particularly useful for time-series forecasting tasks, such as predicting PV power output. LSTM models have built-in memory that allows them to retain information over long periods, making them well suited for tasks such as PVPF, where the underlying patterns and trends can change over time [10]. Recent advancements in the field of data-driven modeling of dynamic systems, where the goal is to deduce system dynamics from data, have shown that more sophisticated models can be applied to the analysis of chaotic time series [11]. Despite these progressive improvements, this study focused on the LSTM model because of its noteworthy performance in PVPF identification and prediction.