Comparison and Explanation of Forecasting Algorithms for Energy Time Series

In this work, energy time series forecasting competitions from the Schneider Company, the Kaggle Online platform, and the American society ASHRAE were considered. These competitions include power generation and building energy consumption forecasts. The datasets used in these competitions are based on reliable and real sensor records. In addition, exogenous variables are accurately added to the dataset. All of these ensure the richness of the information contained in the dataset, which is crucial for energy management. Therefore, (1) We choose to study forecast models suitable for energy management on these energy datasets; (2) Forecast models including popular algorithm structures such as neural network models and ensemble models. In addition, as an innovation, we introduce the Explainable AI method (SHAP) to explain models with excellent performance indicators, thereby strengthening its trust and transparency; (3) The results show that the performance of the integrated model in these competitions is more stable and efficient, and in the integrated model, the advantages of LightGBM are more obvious; (4) Through the interpretation of SHAP, we found that the lagging characteristics of the building area and target variables are important features.

Automated summary

This study focuses on energy time series forecasting competitions, such as power generation and building energy consumption forecasts, using reliable sensor records and accurate exogenous variables. By introducing the Explainable AI method (SHAP), models' performance is explained to strengthen trust and transparency. Results show that the integrated model performs more stable and efficient, with LightGBM showing significant advantages. Through SHAP interpretation, lagging characteristics of building area and target variables are identified as important features.