# Applications of Probabilistic Forecasting in Smart Grids: A Review
## 3 Renewable Generation and Load Probabilistic Forecasting
### Load Probabilistic Forecasting  
Similar to renewable generations, probabilistic forecasting of loads has a wide range of variety based on the type of forecasts and the forecasting horizons [8]. In terms of the variety of forecasting models, different methods have been adopted, such as hybrid Kalman filters [53], Gaussian and lognormal processes [54; 55], artificial neural networks [56; 57] QR [58; 59], RF [60], and stochastic time-series combined with Bayesian inference (BI) [61]. In terms of very short-term (real-time or near-real-time) probabilistic load forecasting, reference [53] is one of the early works proposing hybrid Kalman Filters to probabilistically forecast demand considering the 5 min temporal resolution. The authors of [58] performed half-hour resolution probabilistic forecasting of electricity consumption using QR that is integrated with gradient boosting. In [55], the authors used Gaussian processes to develop a probabilistic forecasting model for residential load considering half-hour resolutions. In [54], the authors performed half-hour-resolution forecasts of residential loads utilizing a lognormal process. In one of the recent study, proposed by [62], the Markov-chain \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref.** & **Forecast Horizon\_Forecast Resolution** & **Forecasting Methods** & **Advantages of the Work** \\ \hline \multirow{3}{*}{[51]} & \multirow{3}{*}{Hour-ahead\_5 min} & \multirow{3}{*}{Hybrid Kalman Filters} & The work proved the effectiveness of hybrid Kalman filters to capture different characteristics of load components from Independent System Operator’s viewpoint \\ \cline{1-2}  & & & Boosting Additive Quantile Regression & The model was designed to probabilistically predict loads at the disaggregated level \\ \hline \multirow{3}{*}{[53]} & \multirow{3}{*}{30-min-ahead\_30 min} & \multirow{3}{*}{Gaussian Processes} & The work analyzed the effects of several covariance functions on load forecasts \\ \cline{1-2}  & & & \\ \cline{1-2}  & & & \\ \cline{1-2}  & & & \\ \hline \multirow{3}{*}{[52]} & \multirow{3}{*}{Multi-step-ahead\_30 min} & Gaussian and log-normal processes & The work proved that the log-normal model can generate a varying sharpness for load forecast \\ \cline{1-2}  & & & \\ \hline \multirow{3}{*}{[60]} & \multirow{3}{*}{30-min-ahead\_30 min} & Markov-chain mixture distribution model & The proposed model was proved not to be computationally expensive and can be insensitive to the settings of parameters \\ \hline \multirow{3}{*}{[61]} & \multirow{3}{*}{Day-ahead\_1 h} & Load probabilistic forecast based on weather ensemble prediction & The work proved that using weather ensemble predictions can improve the accuracy and uncertainty analysis of load forecasts \\ \cline{1-2}  & & Estimation of load’s confidence intervals based on the quantiles of past forecast errors & The method can be adopted for security analysis of power systems since it was able to generate demand scenarios at a specified risk level \\ \hline \multirow{3}{*}{[63]} & \multirow{3}{*}{Day-ahead\_1 h} & Partially linear additive quantile regression & The work combined the forecasting model with the unit commitment problem \\ \cline{1-2}  & & & \\ \cline{1-2}  & & & \\ \cline{1-2}  & & & \\ \cline{1-2}  & & & \\ \cline{1-2}  & & & \\ \hline \multirow{3}{*}{[64]} & \multirow{3}{*}{Day-nine-, one-hour-, two-hours-, and four-hours-ahead\_30 min} & \multirow{3}{*}{LSTM-based} & The model was designed to probabilistically forecast the individual consumer’s load \\ \cline{1-2}  & & & \\ \cline{1-2}  & & & \\ \cline{1-2}  & & & \\ \hline \multirow{3}{*}{[65]} & \multirow{3}{*}{Day-ahead\_1 h} & \multirow{3}{*}{Probabilistic methods} & The novel work focused on determining the reserves based on forecasting net loads. The work demonstrated that the method can decrease the amount of reserves bought for the system \\ \cline{1-2}  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: An overview of selected papers developing probabilistic forecasting models for electricity load.