# Exploiting Vulnerabilities of Load Forecasting Through Adversarial Attacks

arXiv: https://arxiv.org/abs/1904.06606 · HTML: https://mlsanity.com/paper/1904.06606/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Apr 2019.

## Built on by (most cited first, 18 of 18)

- [The Threat of Offensive AI to Organizations](https://mlsanity.com/paper/2106.15764/) (2021)
- [Targeted Adversarial Attacks on Wind Power Forecasts](https://mlsanity.com/paper/2303.16633/) (2023)
- [Robust Bandit Learning with Imperfect Context](https://mlsanity.com/paper/2102.05018/) (2021)
- [Spatially Focused Attack against Spatiotemporal Graph Neural Networks](https://mlsanity.com/paper/2109.04608/) (2021)
- [Learning for Robust Combinatorial Optimization: Algorithm and Application](https://mlsanity.com/paper/2112.10377/) (2021)
- [Adversarial Observations in Weather Forecasting](https://mlsanity.com/paper/2504.15942/) (2025)
- [Learning Data-Driven Uncertainty Set Partitions for Robust and Adaptive Energy Forecasting with Missing Data](https://mlsanity.com/paper/2503.20410/) (2025)
- [Towards Adversarial-Resilient Deep Neural Networks for False Data Injection Attack Detection in Power Grids](https://mlsanity.com/paper/2102.09057/) (2021)
- [Physics-Constrained Backdoor Attacks on Power System Fault Localization](https://mlsanity.com/paper/2211.04445/) (2022)
- [Interpretable Short-Term Load Forecasting via Multi-Scale Temporal Decomposition](https://mlsanity.com/paper/2402.11664/) (2024)
- [Data Poisoning: An Overlooked Threat to Power Grid Resilience](https://mlsanity.com/paper/2407.14684/) (2024)
- [Adversarial Attacks on Deep Learning-Based False Data Injection Detection in Differential Relays](https://mlsanity.com/paper/2506.19302/) (2025)
- [INTARG: Informed Real-Time Adversarial Attack Generation for Time-Series Regression](https://mlsanity.com/paper/2604.11928/) (2026)
- [Adversarial Attacks and Defense Methods for Power Quality Recognition](https://mlsanity.com/paper/2202.07421/) (2022)
- [Learning to Attack Powergrids with DERs](https://mlsanity.com/paper/2204.11352/) (2022)
- [Impact of Data Poisoning Attacks on Feasibility and Optimality of Neural Power System Optimizers](https://mlsanity.com/paper/2502.05727/) (2025)
- [Privacy-Preserving Machine Learning for IoT: A Cross-Paradigm Survey and Future Roadmap](https://mlsanity.com/paper/2603.13570/) (2026)
- [Does Demand Response Increase Vulnerability to Cyber Attacks by Adversarial Data Modifications?](https://mlsanity.com/paper/2607.06632/) (2026)

## Abstract (arXiv)

Load forecasting plays a critical role in the operation and planning of power systems. By using input features such as historical loads and weather forecasts, system operators and utilities build forecast models to guide decision making in commitment and dispatch. As the forecasting techniques becomes more sophisticated, however, they also become more vulnerable to cybersecurity threats. In this paper, we study the vulnerability of a class of load forecasting algorithms and analyze the potential impact on the power system operations, such as load shedding and increased dispatch costs. Specifically, we propose data injection attack algorithms that require minimal assumptions on the ability of the adversary. The attacker does not need to have knowledge about the load forecasting model or the underlying power system. Surprisingly, our results indicate that standard load forecasting algorithms are quite vulnerable to the designed black-box attacks. By only injecting malicious data in temperature from online weather forecast APIs, an attacker could manipulate load forecasts in arbitrary directions and cause significant and targeted damages to system operations.