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September 10, 2025Open Access

Application of machine learning with gradient descent method for load forecasting: a performance analysis

SPSaroj Kumar PandaNational Institute of Technology Rourkela MPManoj Kumar PandaGIET University

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.

Abstract

Abstract In the discipline of power system engineering, predicting power system demand is essential. This is because accurate forecasting models provide the foundation for the majority of system planning and operation tasks. The primary purpose of entire power infrastructures is to supply and support energy consumption. As a result, building reliable and effective predictive models is essential to delivering precise load predictions. One method of forecasting, short-term load forecasting (STLF) is used in this research, and machine learning like deep neural network (DNN) is the method used here for the analysis of STLF. To improve the overall forecasting and address the challenges posed by some category predictors, new predictive variables are added. Based on the choice of input sample and root mean square error (RMSE), the DNN comparison is carried out. To confirm the findings and determine whether or not these models are statistically equivalent, statistical tests are run. The findings show that the DNN model is statistically the same and appropriate for STLF. Further, for the reduction of RMSE value, this study used the gradient descent method as an optimization technique with DNN and the best RMSE values for STLF are 0.0322, 0.0970, 0, 0.0087, 0.0141, and 0.0204, respectively, as compared to without the use of an optimization technique.

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