Exploring Load Forecasting: Bridging Statistical Methods to Deep Learning Techniques in Real-World Environment

Load forecasting has a significant impact on energy management and planning, facilitating efficient allocation of resources and grid operations. In this study, a comparative analysis of traditional statistical methods and deep learning techniques is conducted utilizing a real-world dataset from the Ikaria islanded grid. This paper focuses on four different forecasting approaches: Autoregressive Integrated Moving Average (ARIMA), Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX), Long Short-Term Memory (LSTM) networks, and Deep Neural Networks (DNN). Through the appropriate processing of the data, extensive experimentation took place, aiming to capture the complex and nonlinear patterns of the dataset. The results indicated that LSTM and DNN outperformed both ARIMA and SARIMAX in all three evaluation metrics, achieving 0.13, 0.09, and 2.11%, RMSE, MAE, and MAPE, respectively. As a result, this study validates the superiority of deep learning techniques in real-world islanded grid environments being capable of accurately predicting future load values based on historical data.