International Journal of Scientific Research and Engineering Development

The growing complexity of power systems, which is fueled by the arrival of distributed resources of energy and consumer-side variability, necessitates more dynamic and precise energy load forecasting. The traditional statistical models are not useful in explaining non-linear and time-varying complexity of the present electricity consumption trends. The following paper deals with the application of artificial intelligence (AI) or machine learning (ML) and deep learning (DL) techniques to short-term energy load prediction in the framework of smart grid environments. Our AI models are compared to the four following: random forest (RF), Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRU) and a convolutional neural network-long short-term memory (CNN-LSTM) hybrid. These models are trained using a abundant amount of historical energy consumption, calendar for varying weather and variables. We discovered that the hybrid CNN-LSTM model offers the best predictions and lowest error in forecasting. The experiment does not simply demonstrate that the AI is superior to the traditional methods but also demonstrates the power of smart predictions to power the demand-response system, optimization of generation schedules, and grid stability.