## 2 Overview of PVF Methods
These methods, including machine learning (ML) and deep learning (DL), offer more complex structures and numerous parameters than the conventional models. DL models can learn from parsed data representations using a general learning process without requiring the user to perform specific feature extraction processes, which distinguishes them from conventional ML models. Efficient DL variants, such as LSTM [22], Bidirectional LSTM (BiLSTM) [23], and Gated Recurrent Units (GRU) [24], have been proven to extract and analyze nonlinear and non-stationary characteristics of time-series data and have been employed to predict renewable energy production. * Hybrid methods, which combine the previously discussed methods, are also widely considered in the recent literature [25]. HMs aim to produce better outcomes than simple methods but can be more complex and time-consuming to train. Typically, they are effective for short- and long-term forecasting [26]. The data requirements for HMs depend on the models used in the hybrid approach. For instance, if two sub-models require different types of data, HM will require four types of data. HMs can use physical or statistical models as a foundation and then incorporate DL techniques to capture nonlinearities or rectify model biases. Figure 1: PVPF classification by: (i) the forecasting horizon, (ii) the forecast result, (iii) the type of algorithm. (Our elaboration). Related Works  
In [27], an HM method based on a Convolutional Neural Network (CNN) and LSTM was proposed for the prediction of electric charges, and its performance was compared with that of other models. The work in [28] introduced a prediction error-based power forecasting method for PV for utility-scale application systems using model-based gray box neural networks, which require less training time than black box neural network-based models. In [29] the authors presented an LSTM system that learns from newly arrived data using a two-phase adaptive learning strategy and a sliding-window algorithm to detect concept drifts in PV systems. In [30] authors proposed an LSTM attention-embedding model based on BO to predict the day-ahead PV power generation. The model consists of an LSTM block, an embedding block, and an output block. In [31], a model based on a two-stage attention mechanism over LSTM was proposed to forecast day-ahead PV power, with BO applied to obtain the optimal combination of hyperparameters. A collection of DL techniques, such as LSTM, BiLSTM, GRU, CNN, and HM, was presented in [32]. In this study, the authors conducted a comparative analysis of PVPF methods by utilizing a BO algorithm, which demonstrated the improved performance of the LSTM model compared with other models. A research paper by [33] introduced a PVPF method integrated with the HW statistical forecasting method using historical PV profile data and a forecasting parameter (alpha). The primary advantages of the proposed method are its simplicity and satisfactory performance for short-term forecasting. Another study [34] proposed a hybrid method based on HW and ML to predict residential electricity consumption at a 15-min resolution, where the HW model was employed to model the stationary component of consumption. With regard to predictive maintenance, the authors of [35] presented an annual assessment of the performance degradation of three PV module technologies using four statistical methods including the HW method. In [36], the authors proposed a method that combines the multiplicative HW model and a fruit-fly optimization algorithm to determine the optimal smoothing parameters for the exponential smoothing method. The Mean Absolute Percentage Error (MAPE) was used as the evaluation metric, and the results showed that the proposed model significantly improves accuracy, even when limited training data are available. In [37], a PVPF method based on six time-series algorithms including HW and LSTM was developed for nonstationary data.