Many Commercial Buildings have employed smart meters to measure load consumption data at real-time intervals and then utilized by the Energy Management System (EMS). Load Forecasting based on historical load data is of key importance for effective operation, planning, and optimization of energy for Commercial Buildings. However, designing an accurate Load Forecasting Model is still an on-going challenge. Our methodology involved the usage of Deep Neural Networks (DNN) for Short-Term Load Forecasting. A special architecture of 1-Dimensional Convolutional Neural Networks (1D CNN) known as WaveNet was employed in our method because of its ability to extract rich features from historical load data sequences. A benchmark load consumption dataset of a Commercial Building for the fiscal year 2017 in Kyushu-Japan was used as a case study. Our model was evaluated and compared to other Machine Learning techniques for Forecasting. When tested on the same dataset, it outperformed them all.