eForecaster: Unifying Electricity Forecasting with Robust, Flexible, and Explainable Machine Learning Algorithms


  • Zhaoyang Zhu DAMO Academy, Alibaba Group
  • Weiqi Chen DAMO Academy, Alibaba Group
  • Rui Xia DAMO Academy, Alibaba Group
  • Tian Zhou DAMO Academy, Alibaba Group
  • Peisong Niu DAMO Academy, Alibaba Group
  • Bingqing Peng DAMO Academy, Alibaba Group
  • Wenwei Wang DAMO Academy, Alibaba Group
  • Hengbo Liu DAMO Academy, Alibaba Group
  • Ziqing Ma DAMO Academy, Alibaba Group
  • Qingsong Wen DAMO Academy, Alibaba Group
  • Liang Sun DAMO Academy, Alibaba Group




Electricity Forecasting, Time Series Analysis And Forecasting, Machine Learning Toolkit


Electricity 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. (2024). 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