a plurality of wind turbines;

a photovoltaic array comprising a plurality of wired photovoltaic modules connected in series;

a Moving Regression (MR) filter;

a State of Charge (SoC) feedback control;

a Battery Energy Storage System (BESS); and

an electrical grid,

wherein the photovoltaic array receives solar light signals and generates an unsmoothed solar photovoltaic power output, wherein the unsmoothed solar photovoltaic power output is electrically coupled to the MR filter and the SoC feedback control, and the photovoltaic array has a boost converter;

wherein each of the MR filter, the SoC feedback control and the BESS are electrically coupled to provide a combined smoothed solar photovoltaic power output, wherein the smoothed solar photovoltaic power output is electrically coupled to the electrical grid, and

wherein the MR filter is a non-parametric smoother that is configured to smooth an electrical input with machine learning linear regression over a plurality of time steps, and

the plurality of wind turbines, the photovoltaic array, the MR filter, the SoC feedback control, the BESS, and the electrical grid are electrically connected,

wherein the MR filter is a non-parametric smoother that utilizes a machine learning concept of linear regression to smooth out solar photovoltaic variations at every time step, and

wherein based on a first window size of the MR filter, k neighboring points of a target value are used as training values for a linear regression algorithm.