Power Spot Market Supply and Demand Forecasting and Indicator Anomaly Detection Method Integrating Autoformer and Bayesian Optimization
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Abstract
Accurate forecasting of electricity spot market supply and demand and timely anomaly detection are essential for intelligent energy management and communication-assisted monitoring systems operating over distributed electromagnetic information networks. To improve prediction accuracy under high-frequency non-stationary fluctuations a nd a ccelerate h yperparameter c onvergence i n multi-source feature spaces, this study proposes a power market forecasting and indicator anomaly detection framework integrating Autoformer with particle swarm optimization and Bayesian optimization (PSO-BO). The Autoformer model exploits trend–seasonal decomposition to capture long- and short-term temporal dependencies, while PSO performs global exploration and Bayesian optimization refines local hyperparameters for efficient model tuning. A Bayesian probabilistic anomaly detection model based on a normal-inverse-gamma prior is further established to quantify residual uncertainty and achieve adaptive online warning. Experimental results on a 24-hour rolling forecasting task demonstrate MAE/RMSE values of 1.23/1.75 GW for load prediction and 1.85/2.53 GW for generation prediction, with anomaly detection accuracy reaching 93.87%. The proposed framework exhibits strong sensitivity to rapid market fluctuations and provides an effective solution for realtime intelligent scheduling and secure monitoring. Moreover, its distributed forecasting and adaptive decision mechanisms offer valuable support for communication-enabled energy systems and electromagnetic sensing infrastructures requiring reliable information transmission and operational awareness.
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References
M. Pavlík, F. Kurimsky, and K. Sevc, “Renewable energy and price stability: An analysis of volatility and market shifts in the European electricity sector (2015–2025),” Applied Sciences, vol. 15, no. 12, pp. 6397-6433, 2025, doi: 10.3390/app15126397.
A. Bara, S. V. Oprea, and A. C. Băroiu, “Forecasting the Spot Market Electricity Price with a Long Short-Term Memory Model Architecture in a Disruptive Economic and Geopolitical Context,” International Journal of Computational Intelligence Systems, vol. 16, no. 1, pp. 130-151, 2023, doi: 10.1007/s44196-023-00309-3.
B. F. Hobbs, V. Krishnan, J. Zhang, H. F. Hamann, C. Siebenschuh, R. Zhang, et al., “How can probabilistic solar power forecasts be used to lower costs and improve reliability in power spot markets? A review and application to flexiramp requirements,” IEEE Open Access Journal of Power and Energy, vol. 9, no. 1, pp. 437-450, 2022, doi: 10.1109/OAJPE.2022.3217909.
S. Deng, J. Inekwe, V. Smirnov, A. Wait, and C. Wang, “Seasonality in deep learning forecasts of electricity imbalance prices,” Energy Economics, vol. 137, no. 1, pp. 1-10, 2024, doi: 10.1016/j.eneco.2024.107770.
H. Qiu, R. Hu, J. Chen, and Z. Yuan, “Short-Term Electricity Load Forecasting Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Improved Sparrow Search Algorithm– Convolutional Neural Network–Bidirectional Long Short-Term Memory Model,” Mathematics, vol. 13, no. 5, pp. 813-845, 2025, doi: 10.3390/math13050813.
R. Jain and V. Mahajan, “Load forecasting and risk assessment for energy market with renewable based distributed generation,” Renewable Energy Focus, vol. 42, no. 1, pp. 190-205, 2022, doi: 10.1016/j.ref.2022.06.007.
K. Wang, J. Zhang, X. Li, and Y. Zhang, “Long-term power load forecasting using LSTM-informer with ensemble learning,” Electronics, vol. 12, no. 10, pp. 2175-2193, 2023, doi: 10.3390/electronics12102175.
W. Waheed, Q. Xu, M. Aurangzeb, S. Iqbal, S. H. Dar, and Z. M. S. Elbarbary, “Empowering data-driven load forecasting by leveraging long short-term memory recurrent neural networks,” Heliyon, vol. 10, no. 24, pp. 1-15, 2024, doi: 10.1016/j.heliyon.2024.e40934.
L. Zhao, Z. Wu, and M. Du, “Improved Autuoformer Electricity load forecasting based on model fusion,” Advances in Engineering Technology Research, vol. 11, no. 1, pp. 8, 2024, doi: 10.56028/aetr.11.1.8.2024.
R. Lin, S. Chen, Z. He, B. Wu, H. Zou, X. Zhao, et al., “Electricity behavior modeling and anomaly detection services based on a deep variational autoencoder network,” Energies, vol. 17, no. 16, pp. 3904-3923, 2024, doi: 10.3390/en17163904.
Z. Wang, Z. Chen, Y. Yang, C. Liu, X. Li, and J. Wu, “A hybrid autoformer framework for electricity demand forecasting,” Energy Reports, vol. 9, no. 1, pp. 3800-3812, 2023, doi: 10.1016/j.egyr.2023.02.083.
M. J. Walczewski and H. Wohrle, “Prediction of electricity generation using onshore wind and solar energy in Germany,” Energies, vol. 17, no. 4, pp. 844-870, 2024, doi: 10.3390/en17040844.
D. Liu and H. Wang, “Time series analysis model for forecasting unsteady electric load in buildings,” Energy and Built Environment, vol. 5, no. 6, pp. 900-910, 2024, doi: 10.1016/j.enbenv.2023.07.003.
L. P. Dai, “Performance analysis of deep learning-based electric load forecasting model with particle swarm optimization,” Heliyon, vol. 10, no. 16, pp. 1-20, 2024, doi: 10.1016/j.heliyon.2024.e35273.
Y. Huang, Z. Huang, J. H. Yu, X. Dai, and Y. Li, “Short-term load forecasting based on IPSO-DBiLSTM network with variational mode decomposition and attention mechanism,” Applied Intelligence, vol. 53, no. 10, pp. 12701-12718, 2023, doi: 10.1007/s10489-022-04174-z.
S. Zeng, C. Liu, H. Zhang, B. Zhang, and Y. Zhao, “Short-term load forecasting in power systems based on the Prophet–BO–XGBoost model,” Energies, vol. 18, no. 2, pp. 227-241, 2025, doi: 10.3390/en18020227.
A. K. M. Nor, S. R. Pedapati, M. Muhammad, and V. Leiva, “Abnormality detection and failure prediction using explainable Bayesian deep learning: Methodology and case study with industrial data,” Mathematics, vol. 10, no. 4, pp. 554-590, 2022, doi: 10.3390/math10040554.
X. Zhao, X. Wang, and M. W. Golay, “Bayesian network–based fault diagnostic system for nuclear power plant assets,” Nuclear Technology, vol. 209, no. 3, pp. 401-418, 2023, doi: 10.1080/00295450.2022.2142445.
M. Reyad, A. M. Sarhan, and M. Arafa, “A modified Adam algorithm for deep neural network optimization,” Neural Computing and Applications, vol. 35, no. 23, pp. 17095-17112, 2023, doi: 10.1007/s00521-023-08568-z.
S. Sun and H. Gao, “Meta-AdaM: An meta-learned adaptive optimizer with momentum for few-shot learning,” Advances in Neural Information Processing Systems, vol. 36, no. 1, pp. 65441-65455, 2023, [Online]. Available: https://doi.org/10.52202/075280-2855.
Y. Hong and J. Lin, “On convergence of adam for stochastic optimization under relaxed assumptions,” Advances in Neural Information Processing Systems, vol. 37, no. 1, pp. 10827-10877, 2024, doi: 10.52202/079017-0346.
C. Zhang, Y. Shao, H. Sun, L. Xing, Q. Zhao, and L. Zhang, “The WuC-Adam algorithm based on joint improvement of Warmup and cosine annealing algorithms,” Math. Biosci. Eng, vol. 21, no. 1, pp. 1270-1285, 2024, doi: 10.3934/mbe.2024054.
O. V. Johnson, C. Xinying, K. W. Khaw, and M. H. Lee, “ps-CALR: periodic-shift cosine annealing learning rate for deep neural networks,” IEEE Access, vol. 11, no. 1, pp. 139171-139186, 2023, doi: 10.1109/ACCESS.2023.3340719.