Submodular Load Clustering with Robust Principal Component Analysis

Wang, Yishen; Lu, Xiao; Xu, Yiran; Shi, Di; Yi, Zhehan; Duan, Jiajun; Wang, Zhiwei

doi:10.1109/pesgm40551.2019.8973775

Cited by 4 publications

(2 citation statements)

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“…According to the existing research [32][33][34], the daily load curves for several consecutive days in a given zone have consistent trends due to similar external factors such as weather, human behavior, and infrastructure. Therefore, daily load ahead of the forecasted hour for several consecutive days must contain a large amount of information for the inference.…”

Section: Model Input Datamentioning

confidence: 98%

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Spatiotemporal Feature Learning Based Hour-Ahead Load Forecasting for Energy Internet

Zhang

Wang

2020

Electronics

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In this paper, we analyze the characteristics of the load forecasting task in the Energy Internet context and the deficiencies of existing methods and then propose a data driven approach for one-hour-ahead load forecasting based on the deep learning paradigm. The proposed scheme involves three aspects. First, we formulate a historical load matrix (HLM) with spatiotemporal correlation combined with the EI scenario and then create a three-dimensional historical load tensor (HLT) that contains the HLMs for multiple consecutive time points before the forecasted hour. Second, we preprocess the HLT leveraging a novel low rank decomposition algorithm and different load gradients, aiming to provide a forecasting model with richer input data. Third, we develop a deep forecasting framework (called the 3D CNN-GRU) featuring a feature learning module followed by a regression module, in which the 3D convolutional neural network (3D CNN) is used to extract the desired feature sequences with time attributes, while the gated recurrent unit (GRU) is responsible for mapping the sequences to the forecast values. By feeding the corresponding load label into the 3D CNN-GRU, our proposed scheme can carry out forecasting tasks for any zone covered by the HLM. The results of self-evaluation and a comparison with several state-of-the-art methods demonstrate the superiority of the proposed scheme.

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Section: Model Input Datamentioning

confidence: 98%

“…In our cases, X stands for the base load matrix (BLM), which refers to the expectation of cyclical loads caused by relatively stable daily behavior. The noise E represents the fluctuation load matrix (FLM), which reflects the load generated by various random phenomena [34].…”

Section: Load Tensor Decompositionmentioning

confidence: 99%