Load forecasting is critical for power system operators to maintain a safe and efficient network. Load forecasting contributes to the supply-demand balance by ensuring that consumers receive adequate energy. Load aggregators, power marketers, and independent system operators can all benefit from load forecasting. Over-forecasting leads to excess production and waste of resources. An unexpectedly high load results in a power outage. Both scenarios result in inefficient generation scheduling and technical difficulties for the operator. It is not simple to create a forecasting model for a specific power network. Statistical and machine-learning techniques have been used in load forecasting. Deep learning techniques have recently gained popularity due to their improved ability to interpret complex data relationships. The purpose of this study was to compare deep learning forecasting techniques to some conventional forecasting techniques used by utilities to see if deep learning can better meet their needs.
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