# Applications of Probabilistic Forecasting in Smart Grids: A Review
## 3 Renewable Generation and Load Probabilistic Forecasting
### Wind Probabilistic Forecasting
[50] also combined improved kernel density estimation with regular vine copulas and ensemble learning to obtain an advanced probabilistic forecasting model. Similar to solar forecast, there are some studies proposing NWP models. For instance, ref. [51] suggested wind power probabilistic forecasting using data processing techniques and ensemble NWPs. This methodology comprises data preprocessing techniques, the model of adaptive-network-based fuzzy inference system (ANFIS) integrated with fuzzy c-means (FCM) clustering model, as well as LUBE for prediction of forecast intervals. The work tried to prove that data preprocessing and post-processing processes are very \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref.** & **Forecast Horizon\_Forecast Resolution** & **Forecasting Methods** & **Advantages of the Work** \\ \hline \multirow{2}{*}{[41]} & \multirow{2}{*}{10-min-ahead\_1 min} & Parametric (mixtures of generalized version of logit–normal distributions) & The work considered the non-linear nature and double-bounded characteristics of wind power forecast \\ \hline \multirow{2}{*}{[42]} & \multirow{2}{*}{8-h-ahead\_15 min} & Parametric (censored normal distribution) & The work considered the effects of spatio-temporal on wind power forecast \\ \hline \multirow{2}{*}{[43]} & \multirow{2}{*}{Two-day-ahead\_1 h} & Several parametric probabilistic forecasts & It tested several distribution functions and found Beta distribution function as the most appropriate distribution for wind power \\ \hline \multirow{2}{*}{[44]} & \multirow{2}{*}{Day-ahead and week-ahead\_1 min} & Parametric (versatile distribution) & The model was integrated with the economic dispatch problems which could simplify uncertainties of wind power \\ \hline \multirow{2}{*}{[46]} & \multirow{2}{*}{Hour-ahead\_10 min} & Direct quantile regression combined with mashing learning methods & The model was proved to be high efficient, reliable, and flexible to probabilistically forecast wind power \\ \hline \multirow{2}{*}{[47]} & \multirow{2}{*}{One-hour-ahead and several-hour-ahead\_15 min and 1 h} & Joint quantile regression & The forecasting model was improved by meta-heuristics algorithm \\ \hline \multirow{2}{*}{[17]} & \multirow{2}{*}{Different horizons from days to hours\_30 and 60 min} & A tri-level adaptation function integrated with a fuzzy inference system & The model outperformed other similar approaches in terms of computational efficiency and practicality since it avoided any pre-assumptions about forecast errors and data noises, and considered cost-based optimization problems in the model \\ \hline \multirow{2}{*}{[48]} & \multirow{2}{*}{1-6-h ahead and day-ahead\_different time steps} & Multi-distribution ensemble (MDE) forecasting model integrated with competitive and cooperative strategies & The work tried to explore the best probabilistic forecasting accuracies by considering different forecasting horizons \\ \hline \multirow{2}{*}{[49]} & \multirow{2}{*}{Not specified} & Three neural network-based models & Reinforcement learning was also utilized to combine and improve three kinds of deep learning networks \\ \hline \multirow{2}{*}{[50]} & \multirow{2}{*}{Different horizons from 1-24 h\_1 h} & Kernel density estimation with regular vine copulas and ensemble learning & The work proved that multi-distribution mega-trend-diffusion can improve the forecast when there are insufficient data \\ \hline \multirow{2}{*}{[51]} & \multirow{2}{*}{Hour-ahead\_1 h} & Data processing techniques integrated with ensemble NWPs & The work proved that data processing techniques can improve the probabilistic forecast of wind power considerably \\ \hline \hline \end{tabular}
\end{table} 
Table 3: An overview of selected papers developing probabilistic forecasting models for wind generation. important to improving the forecast models. For this purpose, it compared the model with the ANFIS model, disregarding data preprocessing. The paper concluded that the model utilizing preprocessing outperformed the other models not deploying this technique.