###### Abstract
proposed a combination of bidirectional long short-term memory (BLSTM) with an ANN to predict power generation for a specific hour, using four types of historical input data: hourly PV generation (168 h ahead), hourly horizontal radiation, hourly ambient temperature, and hourly surface temperature. The results showed that the LSTM prediction model with the ANN estimation model using exponential moving average preprocessing exhibited higher accuracy [6]. Another related aspect of neural network application in the building's rooftop PV sector is consumption forecasting. This is a useful tool for the optimal sizing of PV systems for net-metering applications, including those with battery storage systems. Knowledge of the building's consumption profile is a necessary starting point for the improvement of the building's energy management and application of energy-saving actions. [PERSON] et al. proposed a method for predicting the consumption of household appliances by evaluating statistical distributions (Kolmogorov-Smirnov and Pearson's \(\chi^{2}\) tests) [22]. [PERSON] et al. proposed a model for the prediction of energy consumption in a passive solar building in order to generate a mapping between the above easily measurable inputs and the desired output, i.e., the building's energy consumption [23]. In addition to the above issues, photovoltaic applications must be based on special techniques for fault detection, degradation investigation, and other effects such as soiling and potential induced degradation. Neural networks offer great help in studying these effects. [PERSON] et al. proposed a four-layered feed-forward artificial neural network that learns the correlation of the IV curves with irradiance and temperature. This approach has been also been applied to detect anomalous increases in series resistance from a large experimental set of curves [24]. [PERSON] et al. proposed an ANN that uses a given set of working conditions parameters--solar irradiance and photovoltaic (PV) module's temperature--to predict a number of attributes such as current, voltage, and number of peaks in the I-V curve of the PV strings. The simulated attributes were then compared with real field measurements, leading to the identification of possible faulty operating conditions [25]. [PERSON] et al. studied the effect of soiling on large-scale photovoltaic plants using two different techniques: Four Bayesian neural network (BNN) models have been developed in order to calculate the performance at standard test conditions (STCs) of two plants installed in Southern Italy and compare against the results of a regression model. The results indicate that the losses due to dust accumulation on poly-crystalline Si PV modules' surfaces range from roughly 1 to 5% after 1 year of operation [26], which agrees with observations from other researchers [27]. Neural networks are a significant help in solar radiation forecasting which is necessary for the planning, operation, and maintenance procedures of PV plants. [PERSON] et al. [28] employed different ANN techniques to predict daily global radiation on a horizontal surface, based on the daily mean air temperature, relative humidity, and sunshine hours. [PERSON] et al. proposed a heterogeneous ensemble dynamic selection model, named HetDS, to forecast solar irradiance, choosing the most suitable forecasting model from a pool of seven well-known literature methods: ARIMA, support vector regression (SVR), multilayer perceptron neural networks, extreme learning machine (ELM), deep belief network (DBN), random forest (RF), and gradient boosting (GB). The experimental evaluation showed that the proposal can overcome the single models in almost all scenarios with a small dispersion [29]. It can be observed that the various approaches differentiate regarding the type of Neural Network, the selection of inputs, outputs, and the kind of data employed for the training and validation.