Energy Production: A Comparison of Forecasting Methods using the Polynomial Curve Fitting and Linear Regression

In this study, two methods for forecasting the energy production are presented: the polynomial Curve fitting and linear regression. On the one hand, to combine the production of the wind and solar power. On the other hand, to sustain a continuous production and ensure the availability of as much energy as the consumption requires. The linear regression provided a fair result comparing to the first one of the model. In summary, Polynomial curve fitting model provided the highest R-square and adjusted R-square indicating that the model was the most appropriate among the two types of models. Polynomial curve fitting models are, therefore, recommended for forecasting the energy production applications. Simulation results obtained to analyze the relationship and interaction between the power demand and generation. Further, historical data are utilized to predict which type of source (conventional or renewable) can be effective for electricity production by means of adjustment through R-square measures and least square error (SSE).