# Applications of Probabilistic Forecasting in Smart Grids: A Review
## 1 Introduction  
### Motivation and Contribution  
Smart grids' operation and planning deal with different types of forecasts. The recent advances in information and communication technology (ICT) facilitate the real-time control of devices and resources based on the real-time system states. However, to establish an effective control, the mass of data received from smart meters should be processed and analyzed. These data are utilized to predict future system states. The results of this prediction are then employed to find the optimal control strategy. For example, in many studies, electricity consumption and renewable generation are forecasted and the results are used to determine the optimal commitment of the other conventional generation units and resources. In this regard, well-trained data-driven forecasting models often give forecasters overconfident and point-forecasted values [1]. This means that the data-driven algorithms giving us point forecasts cannot model the uncertainties and errors of the forecasts. In addition, the decision-making process may be subjected to an information gap. This information gap creates a disparity between the information that a decision maker has and the information that could be known. Thus, the information gap produces possibilities, and this range of possibilities increases as the information gap grows. In this way, decision makers may decide to base their decisions upon the best-informed available model and disregard the uncertainties, which results in insufficient decision-making [2]. To resolve this issue, probabilistic forecasting is suggested. A probabilistic forecast produces a predictive distribution of values rather than a single value. In general, there are two techniques to generate probabilistic forecasts, named parametric and non-parametric methods. Parametric methods associate a probability distribution with an uncertain variable and then try to estimate the parameters of thisprobability distribution function (PDF). On the other hand, non-parametric methods use quantiles and ensemble forecasts to derive different forecasted values. Different types of variables require different probabilistic forecasting models. The goodness of a probabilistic forecasting model should be evaluated according to two properties: \"calibration and sharpness\" [3]. A calibrated model is able to maintain statistical consistency between forecasts' distribution and the corresponding observations. On the other hand, the sharpness property is more concerned about the concentration of the obtained distribution function [3]. Accordingly, one can conclude that a good probabilistic forecasting model needs to be optimally sharp, subject to being calibrated. In a smart grid environment, stakeholders and operators employ probabilistic forecasting methods for their uncertain parameter values. Renewable generations, customers' consumption behavior, and electric vehicles' charging/discharging behavior are some examples of uncertain parameters. Thus, a comprehensive review of the applications of probabilistic forecasting can guide forecasters to focus on important uncertain parameters in smart grids. For this purpose, a limited number of reviews have been conducted. For example, ref. [4; 5] focused on the recent probabilistic forecasting models aiming to predict wind power. The main focus of [4] was on short-term wind power probabilistic forecasting and compared the forecasting models developed by the previous literature. In contrast, ref. [5] discussed the probabilistic forecasting models up until 2014, considering three different representations of wind power. Similarly, ref. [6; 7; 8] reviewed the methods and applications of probabilistic forecasting for PV generation. In this regard, ref. [6] mainly concentrated on integrating PV probabilistic forecasting models into power system decisions, while ref. [7; 8] mostly reviewed and compared the probabilistic forecasting techniques. Additionally, the methods applied to predict the distribution of electricity demand were reviewed in [9].