Seasonality Effect Exploration for Energy Demand Forecasting in Smart Grids

Effective energy forecasting is essential for the efficient and sustainable management of energy resources, especially as energy demand fluctuates significantly with seasonal changes. This paper explores the impact of seasonality on forecasting algorithms in the context of energy consumption within Smart Grids. Using three years of data from four different countries, the study evaluates and compares both seasonal models – such as Seasonal Autoregressive Integrated Moving Average (SARIMA), Seasonal Long Short-Term Memory (Seasonal-LSTM), and Seasonal eXtreme Gradient Boosting (Seasonal-XGBoost) – and their non-seasonal counterparts. The results demonstrate that seasonal models outperform non-seasonal ones in capturing complex consumption patterns, offering improved accuracy in energy demand prediction. These findings provide valuable insights for energy companies or in the design of intelligent Energy Management Systems, suggesting optimized strategies for resource allocation and underscoring the importance of advanced forecasting methods in supporting sustainable energy practices in urban environments.