Analysis reveals that machine learning methods improve load forecasting accuracy, suggesting effective predictive models are essential in power systems.
Key Points
The DNN model effectively forecasts load with a root mean square error of 0.0087, demonstrating its predictive power.
Adding new predictive variables significantly reduces the RMSE, enhancing the reliability of the short-term load forecasting model.
Statistical testing confirms that the DNN model is statistically equivalent to other forecasting methods, supporting its validity.
The gradient descent optimization technique used with the DNN model shows significant improvement in forecasting accuracy, underscoring its importance.