Research on the application of deep learning technology in power load forecasting and scheduling optimization

In order to optimize scheduling and ensure the power system operates safely and steadily, accurate electrical load forecasting is crucial. Traditional load forecasting techniques are no longer accurate enough to fulfill the demands of modern power systems due to the fast growth of socio-economics. An emerging field of study, deep learning-based energy load forecasting is being made possible by the abundance of load data made possible by smart grid devices and the ever-increasing sophistication of AI. Although electrical load is inherently unpredictable, load curves reveal commonalities in load fluctuations. Improving the accuracy of load forecasting is as simple as selecting key load impacting elements as features. The study uses historical load values, holidays, temperature, and other data as inputs for the load forecasting model, and then performs a correlation analysis of loads and load affecting factors. A model for short-term power demand forecasting using the Inception network and Long Short-Term Memory (Inception-LSTM) is suggested, taking into account the non-linearity and temporal features of loads. We compare the Inception-LSTM model to four others using real load data sets to ensure it is successful. Over the whole test set, the findings show that the suggested model has better prediction accuracy.