Short-Term Load Forecasting of the Greek Electricity System

Featured Application In spite of the significant developments in machine learning methods employed for short-term electrical load forecasting on a Country level, the complexity and diversity of the problem points to the need for investing more research effort in the selection of representative input datasets for the training. This is demonstrated in the example of the Greek electricity system, where careful selection and quality assurance of input data resulted in quite acceptable levels of prediction accuracy, even when training standard, robust feed-forward artificial neural networks. Short-term load forecasting is an essential instrument in power system planning, operation, and control. It is involved in the scheduling of capacity dispatch, system reliability analysis, and maintenance planning for turbines and generators. Despite the high level of development of advanced types of machine learning models in commercial codes and platforms, the prediction accuracy needs further improvement, especially in certain short, problematic time periods. To this end, this paper employs public domain electric load data and typical climatic data to make 24-hour-ahead hourly electricity load forecasts of the Greek system based on two types of robust, standard feed-forward artificial neural networks. The accuracy and stability of the prediction performance are measured by means of the modeling error values. The current prediction accuracy levels of mean absolute percentage error, mean value mu = 2.61% with sigma = 0.33% of the Greek system operator for 2022, attained with noon correction, are closely matched with a simple feed-forward artificial neural network, attaining mean value mu = 3.66% with sigma = 0.30% with true 24-hour-ahead prediction. Specific instances of prediction failure in cases of unexpectedly high or low energy demand are analyzed and discussed. The role of the structure and quality of input data of the training datasets is demonstrated to be the most critical factor in further increasing the accuracy and reliability of forecasting.

Automated summary

Although there have been significant developments in machine learning methods for short-term electrical load forecasting at a Country level, the complexity and diversity of the problem indicate the need for more research effort in selecting representative input datasets for training. The example of the Greek electricity system demonstrates the importance of carefully selecting and ensuring the quality of input data to achieve acceptable levels of prediction accuracy. Short-term load forecasting plays a crucial role in power system planning, operation, and control.