# Predictive Modeling of Photovoltaic Energy Yield Using an ARIMA Approach
## 1 Introduction
The paper [17] reviews five leading artificial neural network (ANN) models for photovoltaic power forecasting, including MLP, RNN, LSTM, GRU, and CNN, and evaluates their structures, influencing factors, and performance across standalone and hybrid configurations. The findings suggest that bidirectional GRU and LSTM models offer superior accuracy, with hybrid and metaheuristic-enhanced models further boosting forecasting performance, though practical implementation challenges remain. The authors of [18] introduce a hybrid model for short-term photovoltaic power forecasting, leveraging multi-frequency decomposition techniques combined with a deep convolutional neural network (CNN) to enhance forecast reliability. Tested on three PV plants in Algeria, the proposed model outperformed both standalone and other hybrid models, achieving superior accuracy with RMSE values between 0.454 and 1.54, highlighting its effectiveness for grid-connected PV systems. The study [19] proposes a hybrid model combining an attention mechanism with a CNN and BiLSTM to improve photovoltaic power prediction accuracy and stability under variable weather conditions. Validated on data from a 1 MW PV station in China, the model outperforms eight advanced models, with ablation experiments demonstrating the critical role of CNNs and attention components in enhancing prediction precision and stability. The review [20] addresses the challenges in photovoltaic forecasting, highlighting the lack of standardized datasets, metrics, and benchmarking practices in time series forecasting. By analyzing 36 recent studies, the review compares model types, hyperparameters, and evaluation metrics, underscoring the need for universal error metrics to facilitate clearer global comparisons and outlining trends and challenges to advance the field. However, ARIMA-type methods are also used, although scarcely reported in the literature, for PV data forecasting, from the long to short term. The study [21] addressesthe challenges of multisite long-term photovoltaic forecasting by proposing the Variable-Adaptive Channel-Independent (VACI) architecture and a deep tree-structured multi-scale gated component (DTM block), forming the DTMGNet model. Validated on a real-world solar energy benchmark dataset, DTMGNet achieved state-of-the-art performance, notably reducing MSE in a 720-step ultra-long forecasting window by 7.44%, demonstrating its effectiveness in handling complex multivariate long-term predictions. In [22], the authors introduce a hybrid intelligent method for short-term renewable power production forecasting (RPPF), combining LSTM, GRU, ARIMA, MLP, and ANFIS models with the Modified Relief-Mutual Information (MRMI) feature selection technique. Validated on solar farms in Mahan and Rafsanjan, Iran, the method improves forecasting accuracy by leveraging the strengths of individual models and optimizing input data selection, offering an effective solution for addressing the uncertainties of renewable energy production. The paper [23] combines ARIMA and LSTM models to forecast electricity supply in China and assess optimal sites for PV power generation. By analyzing geographic regions with the EWM-TOPSIS combinatorial model and evaluating key indicators, the research identifies the most suitable provinces and regions for PV plant construction, providing a data-driven framework to support China's transition to renewable energy. The article [24] utilizes an ARIMA (2,1,3) model for very-short-term forecasting of global solar radiation with a 5 min interval, based on data collected from Naresuan University in Thailand. By achieving high accuracy on clear sky days with an RMSE of 0.9125 and a MAPE of 0.059, the model demonstrates the potential to enhance electricity supply planning and solar power plant design, particularly when ARIMA is combined with neural networks to improve forecasting precision. In this article, a photovoltaic system--5 kW--is considered and installed on the roof of a residential building in Simitli, Blagoevgrad, Bulgaria (Figures 1 and 2).