eForecaster: Unifying Electricity Forecasting with Robust, Flexible, and Explainable Machine Learning Algorithms
Keywords:Electricity Forecasting, Time Series Analysis And Forecasting, Machine Learning Toolkit
AbstractElectricity forecasting is crucial in scheduling and planning of future electric load, so as to improve the reliability and safeness of the power grid. Despite recent developments of forecasting algorithms in the machine learning community, there is a lack of general and advanced algorithms specifically considering requirements from the power industry perspective. In this paper, we present eForecaster, a unified AI platform including robust, flexible, and explainable machine learning algorithms for diversified electricity forecasting applications. Since Oct. 2021, multiple commercial bus load, system load, and renewable energy forecasting systems built upon eForecaster have been deployed in seven provinces of China. The deployed systems consistently reduce the average Mean Absolute Error (MAE) by 39.8% to 77.0%, with reduced manual work and explainable guidance. In particular, eForecaster also integrates multiple interpretation methods to uncover the working mechanism of the predictive models, which significantly improves forecasts adoption and user satisfaction.
How to Cite
Zhu, Z., Chen, W., Xia, R., Zhou, T., Niu, P., Peng, B., Wang, W., Liu, H., Ma, Z., Wen, Q., & Sun, L. (2023). eForecaster: Unifying Electricity Forecasting with Robust, Flexible, and Explainable Machine Learning Algorithms. Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 15630-15638. https://doi.org/10.1609/aaai.v37i13.26853
IAAI Technical Track on deployed Highly Innovative Applications of AI