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Non-intrusive Load Composition Estimation from Aggregate ZIP Load Models using Machine Learning
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DOI：10.1016/j.ijepes.2018.08.016.png)
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Abstract
En 中文
Having continuous load structure and composition information of substations has a great relevance in power system analysis such as load modeling, load forecasting and demand-side management. In this paper, a parsimonious approach for load composition estimation using non-intrusive load disaggregation techniques for low voltage substations is presented with a concept of using ZIP load model characteristics of the aggregate active and reactive powers as predictor features. The disaggregation system uses machine learning algorithms such as Function Fitting Multi-Layer Perceptron Artificial Neural Network (MLP-ANN), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). During the study, a simulation dataset was generated using Monte Carlo simulation. Moreover, a comparative analysis with a benchmarked paper has been assessed and the proposed approach significantly outperforms.
Keywords:
Artificial neural network
Load composition estimation
Load disaggregation
Non-intrusive load monitoring
Substation
ZIP modeling
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I
IF:
5
Papers:
1.1W
Citations:
3.1W