Forecasting energy demand is essential for optimizing energy generation and effectively predicting power system needs. Recently, many researchers have developed various models on tabular datasets to enhance the effectiveness of demand prediction, including neural networks, machine learning, deep learning, and advanced architectures such as CNN and LSTM. However, research on the CNN models has struggled to provide reliable outcomes due to insufficient dataset sizes, repeated investigations, and inappropriate baseline selection. To address these challenges, we propose a Tabular data-based Lightweight Convolutional Neural Network (TLCNN) model for predicting energy demand. It frames the problem as a regression task that effectively captures complex data trends for accurate forecasting. The BanE-16 dataset is preprocessed using normalization techniques for categorical and numerical data before training the model. The proposed approach dynamically selects relevant features through a two-dimensional convolutional structure that improves adaptability. The model’s performance is evaluated using MSE, MAE, and Accuracy metrics. Experimental results show that TLCNN achieves a 10.89% lower MSE than traditional ML algorithms, demonstrating superior predictive capability. Additionally, TLCNN’s lightweight structure enhances generalization while reducing computational costs, making it suitable for real-world energy forecasting tasks. This study contributes to energy informatics by introducing an optimized deep-learning framework that imp