**2. Literature Review**

Numerous studies have developed different forecasting models to estimate the energy output of renewable energy systems. The studies, however, differ with regard to the crucial variables that are to be predicted. Brahimi [20], proposed an artificial neural network (ANN)-based method to forecast the daily wind speed in a number of locations in Saudi Arabia. The weather data were collected from multiple local meteorological measurement stations operated by King Abdullah City for Atomic and Renewable Energy (K.A.CARE.). For this research work, five machine learning (ML) algorithms were developed and compared with each other, including ANN, SVM, random tree, RF, and RepTree. The proposed model was a feed-forward neural network (NN) model that applied a back-propagation

algorithm with the administered learning technique. The similarity between predicted and actual data from meteorological stations exhibited a reasonably satisfactory agreement [20]. A study [4] analyzed various ML methods to predict the output power for uniform solar panels. The researchers used a distributed RF regression algorithm and independent variables, namely, the latitude, wind speed, month, time, cloud ceiling, ambient temperature, pressure and humidity. Another study [6] predicted the short-term, next-day global horizontal irradiance using the earlier day's meteorological and solar radiation observations. The models used for this investigation were based on computational intelligence methods of automated-design fuzzy logic systems. Fuzzy c-means clustering (FCM) and simulated annealing (SA) algorithms were utilized in fuzzy logic systems for optimization. The FCM model achieved 79.75% accuracy, and the agreement increased to 88.22% upon using the SA model. A research work conducted by [21] used ANNs to investigate the correlation between irradiance and PV output power. The model was designed for real-time prediction of the power produced the next day. The PV power output data used for the AI model were extracted from an installed PV system. The research findings revealed that ML algorithms exhibit a marked capacity for predicting power production based on various weather conditions and measures. The model helps in the management of energy flows and the optimization of PV plants' integration into power systems. In another study [22], different NN-based techniques were compared with the results procured by the simulation of a moderate manufacturing plant in the UK to forecast energy use and workshop conditions for manufacturing facilities based on output plans, environmental conditions, and the thermal characteristics of the factory building, along with building activity and usage, by comparing two deep neural networks (DNNs), namely feed-forward and recurrent. The recurrent (feed-forward) model can forecast building electricity with a precision of 96.82% (92.4%), workshop air temperatures with a precision of 99.40% (99.5%), and humidity with a precision of 57.60% (64.8%). Coupling modeling techniques with ML algorithms makes it possible to forecast and maximize energy consumption in the industrial industry using a low-cost, non-intrusive approach. Kharlova et al. [23] discussed the end-to-end forecasting of PV power output by introducing a monitored deep learning model. The suggested framework leverages numerical estimates of the weather's historical and high-resolution calculations to predict a binned probability distribution, rather than the prognostic variable's predicted values, over the prognostic time intervals. The suggested sequence-to-sequence model with focus achieved a 48.1% accuracy by root mean square error (RMSE) score on the test range, outperforming the best previously reported ability scores for a day-ahead forecast of 42.5–46.0% by a large margin [24,25]. Rajabalizadeh's study took a PV housing unit in Swanson, New Zealand. The copula method was used to model the stochastic association structure between meteorological variables, such as air temperature, wind speed, and solar radiation. The Clayton copula method was used to estimate a small-scale PV system's output power. The prediction error was crucial and, under all weather situations, copula increased forecasting results. The approach discussed in this report is expected to be sufficient for the control of energy in a smart home. As the model is easy to operate and precise, it will be accessible to residences [26]. The solar PV system was installed on the roof of the Faculty of Electrical engineering, Universiti Tun Hussein Onn Malaysia. The maximal PV output capacity on the roof will then be predicted by using the estimation process and the ANN. The experimental results have validated that ANN is capable of estimating PV performance similar to the approximation process [27]. In this research work, a microgrid residential model was developed in San Diego, California, in 2016. To verify the model precision, the solar irradiance and solar energy generated in the residential microgrid, those expected for 2017, were used in NN-based model. The two metrics used to calculate and compare the model's precision were mean absolute percentage error (MAPE) and mean squared error (MSE). The NN-based model was observed to be effective [28]. Another research work conducted by [10] developed an AI model that improved an ANN with tapped delay lines, built for one-day-ahead forecasting. The model achieved a seasonal mean absolute error that ranged between 12.2%

and 26.0% in different seasons around the year. The inputs of the model were the irradiation and the sampling hours. Monteiro et al. [29] developed models that could predict PV power using numerically predicted weather data and previous hourly values for PV electric power productions. The developed models, the analytical PV power forecasting model and multilayer perceptron PV forecasting model, achieved an RMSE between 11.95% and 12.10%. Wei [30] investigated the southern climate of Taiwan in 2016 to predict the power generation for the building roofs. This study was divided into three phases; the first phase used BP3 solar panels installed on the rooftops of buildings. The most effective model with regard to results is BP380(183.5 KWh/m2-y), BP3125(182.2 KWh/m2-y) with the performance of power conversion is 12.4%, 12.3%, respectively. In the second phase, a surface solar radiation measurement analysis was conducted to simulate meteorological instability during hourly PV generation; the results obtained by a DNN method are compared with backpropagation NN (BPN) and an LR model. In the third phase, a BP3125 panel was used for both the second and third phases, and DNN attained the minimum MAEs and RMSEs among the three models at lead times of 1 h, 3 h, 6 h, and 12 h, demonstrating its adequate predictive precision. The approach was validated as sufficient for evaluating the power-generation performance of a roof PV system. According to this paper, a centralized grid unit is constructed to which PV panels are installed on rooftops with an energy storage system, i.e., battery, under the power purchase agreement (PPA) scheme. The system's economic stability relies solely on the quality of the data. Therefore, AI techniques can be used to adequately forecast and control grid load in real-time via PV. This is beneficial for almost all the players concerned, i.e., the solar lease firm, the grid provider, and the end-users [31].

It has been asserted in the extant literature that the models that use numerically predicted weather data do not consider the effect of cloud cover and cloud formation when initializing [32]. Pelland et al. [33] used sky imaging and satellite data to predict the PV energy output. Another study [34] developed a model that predicts the global horizontal radiation for the next day in several weather stations in Saudi Arabia. Although these systems are primarily run and have proven remarkably helpful, they are referred to as unpredictable, uncontrollable, and non-scheduled power source systems. This is because, in line with the system's geographic region, a certain kind of power output is contingent on the atmospheric environment.
