Machine learning algorithm for generation and consumption forecasting in an electrical network

Abstract: Power generation and consumption are directly affected by weather, and the weather variables are fluctuating, which means that generation and consumption also fluctuate. These fluctuations can damage equipment or cause other problems in an electrical network. Having accurate predictions to adjust the infrastructure ahead of time can therefore mitigate these problems. Extensive research has been done on short-term forecasting using machine learning (ML) in electrical networks where some of them also use weather data. However, often the research studies do not cover many of the algorithms that are known to be suitable for short-term forecasting in an electrical network, or the importance of the features is not shown. Our study aims to compare a wide range of ML algorithms that are known to be good for short-term forecasting with time-series data. Additionally, we want to investigate which of the available features, including weather data, have the greatest impact on the results of the models. The research consists of a literature study to find the most suitable algorithms for time-series short-term forecasting in an electrical network using weather information. To find the best algorithms, several algorithms are trained and tested on the full data set. Then, the algorithms are retrained on the same data set after feature selection and compared to the models trained on the full data set. The results of our experiment are that BiLSTM and CNN are the best models and that the features most closely related to the target are the most important while the weather features still have some impact.