Uncertainty-Aware Forecasting of Renewable Energy Sources

Abstract

Smart grid systems are designed to enable the efficient capture and intelligent distribution of electricity across a distributed set of utilities. They are an essential component of increasingly important renewable energy sources, where it is vital to forecast the levels of energy being fed into and drawn from the grid. However, because of the high levels of uncertainty affecting real-world environments, accurate forecasting for example of wind power generation -being directly dependent on meteorological parameters and climatic conditions -is extremely challenging. Fuzzy Logic systems are frequently used in control systems to leverage their capacity for handling varying levels of uncertainty. In most cases, while uncertainty affecting the systems is captured in fuzzy sets (FSs), the final output of such systems is reduced to a crisp number (e.g. a control output). The latter process, while providing an efficient pathway to generating a specific control output, at the same time implies substantial information loss, as the uncertainty information captured in the FS outputs of these systems is effectively discarded. In this paper, we explore the potential of Mamdani fuzzy logic system based forecasting in order to generate not only a numeric forecast of the energy generated, but to also generate uncertainty intervals around said forecast indicating the level of uncertainty associated with the prediction. The proposed model is explored using both synthetic and smart-grid specific real-world (wind power) time series datasets. The results of the study indicate that utilising the 'complete' FS output can provide valuable additional information in terms of the reliability of the forecast without any extra computational cost. At a general level, the approach indicates strong potential for leveraging the uncertainty information in fuzzy system outputs -which is commonly discarded -in real world applications.

Review on Fuzzy and Neural Prediction Interval Modelling for Nonlinear Dynamical Systems

“…In the work of [27] a Mamdani fuzzy logic system based on α-level cuts of the output is presented to generate the prediction intervals. An α-cut of a fuzzy set A is a crisp set A α that contains all the elements in U that have membership values in A greater than or equal to α, that is [28],…”

“…The general idea to obtain the prediction interval based on alpha-cut (α) approach is to decompose output fuzzy sets into a collection of crisp sets related together via the α levels [29]. Therefore, Alpha cuts on Mamdani Fuzzy logic system based output fuzzy sets (commonly defuzzified to a crisp number) are used to provide this prediction interval using the following 4-step process [27]:…”

“…where θ r and θ r are the parameter vectors of the fuzzy functions obtained in (27) and (28) respectively. The advantage of this method is its great flexibility for modeling the behavior of prediction error variance.…”

An Interval-Valued Time Series Forecasting Scheme With Probability Distribution Features for Electric Power Generation Prediction

“…ITS Forecasting Scheme [23], [13], [23], [24], [25], [26], [27], [28] It is a data-driven method, which is allowed new variable types to be introduced. The properties of ITS are described by these interval-valued variables.…”

“…The proposed hybrid model utilizes fuzzy C-mean clustering and the ITS autoregressive process to predict the short-term electricity price. Pekaslan, et al [28] used a fuzzy logic system to perform not only a numeric forecast of the power generation but also prediction by uncertainty intervals. The result indicates that a complete fuzzy system output can provide…”