Intelligent Load Forecasting: Leveraging Machine Learning for Accurate Demand Estimation

Abstract

Accurate short-term load forecasting is essential for efficient smart grid operation and demand-side energy management. Traditional statistical models often struggle to capture nonlinear and irregular residential electricity consumption patterns. This paper proposes a hybrid deep learning architecture combining Temporal Convolutional Networks (TCN) and Long Short-Term Memory (LSTM) networks for submeterlevel load forecasting. The model uses dilated causal convolutions to extract short-term temporal features, followed by LSTM units to capture long-term dependencies in energy consumption data. Data preprocessing includes normalization and slidingwindow sequence generation to convert historical load data into supervised learning samples. The model is evaluated using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) on both synthetically generated residential load profiles and the real-world UCI Household Electric Power Consumption dataset. The Hybrid TCN-LSTM model achieves an average RMSE of 0.0428 and MAE of 0.0317 on the synthetic dataset across 250 consumers and RMSE of 0.4551 kW with MAE of 0.2842 kW on the UCI dataset. Results demonstrate that the hybrid architecture effectively captures short-term fluctuations and longterm temporal dependencies, enabling reliable load forecasting for smart grid applications.