Description

Forecasts of meteorology-driven factors, such as intermittent renewable generation, are commonly included in electricity price forecasting models. We introduce an autoregressive multivariate linear model with exogenous variables and LASSO for variable selection and regularization. Electricity prices fluctuate substantially over time, because the possibility to economically store electricity is limited. In our process, we have to take the input as weather electricity price dataset. After that, we can implement the different machine learning algorithms such as lasso regression and ridge regression. The experimental results shows that some error values for each algorithm. Furthermore, the forecasting horizon is shown to impact the choice of the regularization penalty that tends to increase at longer forecasting horizons.