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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.14983 |
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| _version_ | 1866918162371444736 |
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| author | Triebe, Oskar Passow, Fletcher Wittner, Simon Wagner, Leonie Arend, Julio Sun, Tao Zanocco, Chad Miltner, Marek Ghesmati, Arezou Tsai, Chen-Hao Bergmeir, Christoph Rajagopal, Ram |
| author_facet | Triebe, Oskar Passow, Fletcher Wittner, Simon Wagner, Leonie Arend, Julio Sun, Tao Zanocco, Chad Miltner, Marek Ghesmati, Arezou Tsai, Chen-Hao Bergmeir, Christoph Rajagopal, Ram |
| contents | The reliability of local power grid infrastructure is challenged by sustainable energy developments increasing electric load uncertainty. Transmission System Operators (TSOs) need load forecasts of higher spatial resolution, extending current forecasting operations from zonal aggregates to individual nodes. However, nodal loads are less accurate to forecast and require a large number of individual forecasts, which are hard to manage for the human experts assessing risks in the control room's daily operations (operator). In collaboration with a TSO, we design a multi-level system that meets the needs of operators for hourly day-ahead load forecasting. Utilizing a uniquely extensive dataset of zonal and nodal net loads, we experimentally evaluate our system components. First, we develop an interpretable and scalable forecasting model that allows for TSOs to gradually extend zonal operations to include nodal forecasts. Second, we evaluate solutions to address the heterogeneity and volatility of nodal load, subject to a trade-off. Third, our system is manageable with a fully parallelized single-model forecasting workflow. Our results show accuracy and interpretability improvements for zonal forecasts, and substantial improvements for nodal forecasts. In practice, our multi-level forecasting system allows operators to adjust forecasts with unprecedented confidence and accuracy, and to diagnose otherwise opaque errors precisely. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_14983 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Extending Load Forecasting from Zonal Aggregates to Individual Nodes for Transmission System Operators Triebe, Oskar Passow, Fletcher Wittner, Simon Wagner, Leonie Arend, Julio Sun, Tao Zanocco, Chad Miltner, Marek Ghesmati, Arezou Tsai, Chen-Hao Bergmeir, Christoph Rajagopal, Ram Machine Learning Human-Computer Interaction Systems and Control The reliability of local power grid infrastructure is challenged by sustainable energy developments increasing electric load uncertainty. Transmission System Operators (TSOs) need load forecasts of higher spatial resolution, extending current forecasting operations from zonal aggregates to individual nodes. However, nodal loads are less accurate to forecast and require a large number of individual forecasts, which are hard to manage for the human experts assessing risks in the control room's daily operations (operator). In collaboration with a TSO, we design a multi-level system that meets the needs of operators for hourly day-ahead load forecasting. Utilizing a uniquely extensive dataset of zonal and nodal net loads, we experimentally evaluate our system components. First, we develop an interpretable and scalable forecasting model that allows for TSOs to gradually extend zonal operations to include nodal forecasts. Second, we evaluate solutions to address the heterogeneity and volatility of nodal load, subject to a trade-off. Third, our system is manageable with a fully parallelized single-model forecasting workflow. Our results show accuracy and interpretability improvements for zonal forecasts, and substantial improvements for nodal forecasts. In practice, our multi-level forecasting system allows operators to adjust forecasts with unprecedented confidence and accuracy, and to diagnose otherwise opaque errors precisely. |
| title | Extending Load Forecasting from Zonal Aggregates to Individual Nodes for Transmission System Operators |
| topic | Machine Learning Human-Computer Interaction Systems and Control |
| url | https://arxiv.org/abs/2510.14983 |