The ongoing transition of the European energy sector and the increasing complexity of grid management have made accurate forecasting within electricity markets a critical necessity. Short-term electricity load forecasting is fundamentally important for Transmission System Operators to efficiently schedule secondary energy resources, balance supply and demand, and maintain grid frequency at its nominal value. This thesis addresses these operational challenges through a dual-focus approach: modeling short-term electricity load dynamics in Italy and conducting a comparative econometric analysis of electricity prices across distinct European markets. Methodologically, this research employs robust statistical time series forecasting techniques, specifically utilizing Exponential Smoothing State Space Models, ETS, Autoregressive Integrated Moving Average, ARIMA, and Generalized Autoregressive Conditional Heteroskedasticity, GARCH, models.