Ensemble echo state networks trained collectively for reliable energy forecasting in facilities

Abstract

Appendix A. Supplementary data. Supplementary material related to this article can be found online at https://doi.org/10.1016/j.compeleceng.2025.110854 © 2025 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ ).

Accurate and reliable short-term energy consumption forecasting remains a central challenge in modern energy systems, where heterogeneous patterns make uncertainty quantification particularly difficult. This problem is often addressed using ensembles of lightweight recurrent neural networks, such as Echo State Networks (ESNs), which can be efficiently trained individually and then averaged. We propose a novel one-shot ensemble technique for ESNs that constructs multiple diverse reservoirs while jointly training a single shared readout layer. This approach combines the diversity required for effective ensembling with the efficiency of collective optimization, enhancing two key ingredients for accurate and reliable forecasting. Comprehensive experiments demonstrate that our method consistently outperforms conventional ensemble techniques. On educational buildings, the proposed approach achieves an 87.3% improvement in accuracy and an 18.8% enhancement in uncertainty quantification. In manufacturing facilities, where system dynamics are more complex, our method achieves a 13.6% gain in accuracy and a 7.5% improvement in uncertainty estimation. Notably, it provides the best balance between precision and reliability compared to several state-of-the-art models.

Supported by the ‘Siemens Energy AI Chair: Energy Sustainability for a Decarbonized Society 5.0’ (TSI-100930-2023-5), funded by the Secretary of State for Digitalization and Artificial Intelligence through the ENIA 2022 Chairs call, and co-funded by the European Union-Next Generation EU. The authors thank Mayra Ramirez Chavez for her guidance on energy-related considerations during the early stages of this research.

Peer Reviewed

7 - Energia Assequible i No Contaminant

12 - Producció i Consum Responsables

13 - Acció per al Clima

Postprint (published version)