Industrial Ultra-Short-Term Load Forecasting With Data Completion

Accurate and efficient ultra-short-term load forecasting is crucial for industrial power users to have stabilized and optimized operations. In this paper, we develop novel strategies for industrial power users to handle their challenges in ultra-short-term load forecasting. Firstly, this paper propo...

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Veröffentlicht in:IEEE access Jg. 8; S. 158928 - 158940
Hauptverfasser: Jiang, Haoyu, Wu, Angjian, Wang, Bo, Xu, Peizhe, Yao, Gang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Back propagation networks
clonal immune algorithm
combined model
Cubature Kalman filter
Data loss
Data mining
Data models
Dynamical systems
Electricity consumption
Forecasting
Genetic algorithms
grey neural network
Kalman filters
least squares support vector machine
Load forecasting
Load modeling
Mathematical models
Missing data
Neural networks
Nonlinear dynamics
Nonlinear systems
Particle swarm optimization
Prediction algorithms
Prediction models
Predictive models
PSO-SVR
Support vector machines
ISSN:2169-3536, 2169-3536
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Zusammenfassung:Accurate and efficient ultra-short-term load forecasting is crucial for industrial power users to have stabilized and optimized operations. In this paper, we develop novel strategies for industrial power users to handle their challenges in ultra-short-term load forecasting. Firstly, this paper proposes a two-way Genetic Algorithm Back Propagation Neural Networks (GABPNN) missing data completion model to handle data loss, which is common power load data mining. A particle swarm optimization - supporting vector regression (PSO-SVR) algorithm is further used to integrate the two-way completion results with better accuracy. In addition, the paper introduces a combined ultra-short-term load forecasting model for industrial power users. The proposed model combines the Cubature Kalman filter (CKF) prediction model with good performance in nonlinear dynamic systems and the least square support vector machine (LS-SVM) prediction model with good performance in small-scale data prediction. The grey neural network is used to integrate the two algorithms, which further improves the accuracy of ultra-short-term load forecasting. Lastly, we test the proposed strategy in case study with real industrial data and demonstrate that the proposed model has a high degree of precision in load forecasting.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.3017655