# Applications of Probabilistic Forecasting in Smart Grids: A Review
## 3 Renewable Generation and Load Probabilistic Forecasting
### Solar Probabilistic Forecasting  
Network operators, generation agents as well as premises need PV generation to be forecasted at various horizons, including very short-term, hourly and intra-hour, intra-day, as well as day-ahead forecasts [2]. In this regard, future PV generation is forecasted either based on solar irradiance or PV generations of previous times. If the forecast reference is based on solar irradiance, it builds a model according to the past meteorological data or present observations. To construct the model, it utilizes the data from weather stations, satellites, and local sensors and images as inputs [6]. It then develops a model by mapping the inputs to the solar generation. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref.** &
\begin{tabular}{c} **Forecast Horizon\_Forecast** \\ **Resolution** \\ \end{tabular} & **Forecasting Methods** & **Advantages of the Work** \\ \hline
[26] & Day-ahead\_1 h &
\begin{tabular}{c} NWP-based solar \\ irradiance forecast \\ \end{tabular} & The proposed probabilistic forecasting model could reflect the effects of atmospheric conditions on forecast errors \\ \hline
[27] & Two-day-ahead\_30 min &
\begin{tabular}{c} NWP-based solar \\ irradiance forecast \\ \end{tabular} & The paper used a post processing method that was able to improve the performance of the forecasting algorithm \\ \hline
[28] & Day-ahead\_1 h &
\begin{tabular}{c} NWP-based solar \\ irradiance forecast \\ \end{tabular} & It determined several confidence intervals for each region \\ \hline
[29] & Day-ahead and hour-ahead\_10 min &
\begin{tabular}{c} Different parametric and \\ non-parametric models \\ \end{tabular} & It assessed the effects of reconciliation on the improvement of forecasts \\ \hline
[30] & Long-term\_1 h & Three parametric models & The model could describe the stochastic characteristics and features of solar irradiance \\ \hline
[31] & Intra-day (1–6 h)\_1 s &
\begin{tabular}{c} Two models developed using \\ quantile regression \\ \end{tabular} & It conducted graphical analysis of reliability to compare the performance of the forecasts \\ \hline
[32] & Three-day-ahead\_1 h &
\begin{tabular}{c} ANN-based combined with \\ Analog Ensemble \\ \end{tabular} & The combination of these two methods yielded best results compared to the individual models \\ \hline
[33] & day-ahead\_1 h & LSTM-based & The proposed model performed better compared to the simpler models but got the same results as the fully connected ANN-based model \\ \hline \hline \end{tabular}
\end{table} 
Table 2: An overview of selected papers developing probabilistic forecasting models for solar generation. In order to probabilistically forecast PV generation, some works proposed using numerical weather prediction (NWP) models under various scenarios. References [26; 27; 28] are some examples that employed NWP to obtain ensemble forecasts for solar irradiance and build an uncertainty model for PV generation. An NWP adopts some measured meteorological inputs, such as temperature, humidity and pressure. It then solves a set of partial differential equations to simulate the process through which solar irradiance is obtained [34]. To obtain a probabilistic forecast, an NWP method uses a training set consisting of the NWP ensemble members at timeslot \(t\). After that, each member is weighted equally and utilized to make an empirical cumulative distribution function. NWP methods are usually very straightforward to implement. However, it was argued that NWP models are computationally expensive to implement, and thus it would be better to run them only a few times a day [35]. In the short-run, such as real-time and near real-time forecast horizons, probabilistic forecasters mainly utilize data-driven models including statistical and machine-learning ones [36]. For example, ref.