A method of determining a time-dependent forecast error in an energy grid, the method comprising: introducing a data set defined by an identified generated power trend and an identified forecast power trend of the energy grid across a time period, each of the identified generated and forecast power trends being based on a physical process; producing a statistical structure by comparing the identified generated and forecast power trends and analyzing the data set to identify temporal error variations defined by temporal correlated fluctuations between the identified generated power trend and the forecast power trend; calculating the time-dependent forecast error comprised of a time-scaling error and a scaling error based on the statistical structure derived from the physical process, wherein the time scaling error is an error, which is constituted by a failure to predict short time, high frequency signal variations of the generated power trend, and wherein the scaling error is a difference in scaling exponents between the identified generated power trend and the forecast power trend, wherein the scaling exponents representing a fractal structure of fluctuations; and analyzing the time-dependent forecast error to determine a predicted value of energy fluctuations of the physical process associated with the identified generated and forecast power trends used to manage the energy grid, wherein when the fluctuations defined by turbulence exhibit fractal scaling, the method further comprising: quantifying correlated fluctuations with a scaling exponent using a predetermined structure function, and wherein the predetermined structure function is defined by S n (τ) = <[|x(t+τ) - x(t)|] n >, wherein each time-varying function x(t) at time t is subtracted from a time-varying signal x(t+τ) at a later time (t+τ) so that an absolute difference determines one of the plurality of correlated fluctuations. introducing a data set defined by an identified generated power trend and an identified forecast power trend of the energy grid across a time period, each of the identified generated and forecast power trends being based on a physical process; producing a statistical structure by comparing the identified generated and forecast power trends and analyzing the data set to identify temporal error variations defined by temporal correlated fluctuations between the identified generated power trend and the forecast power trend; calculating the time-dependent forecast error comprised of a time-scaling error and a scaling error based on the statistical structure derived from the physical process, wherein the time scaling error is an error, which is constituted by a failure to predict short time, high frequency signal variations of the generated power trend, and wherein the scaling error is a difference in scaling exponents between the identified generated power trend and the forecast power trend, wherein the scaling exponents representing a fractal structure of fluctuations; and analyzing the time-dependent forecast error to determine a predicted value of energy fluctuations of the physical process associated with the identified generated and forecast power trends used to manage the energy grid, wherein when the fluctuations defined by turbulence exhibit fractal scaling, the method further comprising: quantifying correlated fluctuations with a scaling exponent using a predetermined structure function, and wherein the predetermined structure function is defined by S n (τ) = <[|x(t+τ) - x(t)|] n >, wherein each time-varying function x(t) at time t is subtracted from a time-varying signal x(t+τ) at a later time (t+τ) so that an absolute difference determines one of the plurality of correlated fluctuations.