  The knowledge of the users' electricity consumption pattern is an important
coordinating mechanism between the utility company and the electricity
consumers in terms of key decision makings. The load decomposition is therefore
crucial to reveal the underlying relationship between the load consumption and
its characteristics. However, load decomposition is conventionally performed on
the residential and commercial loads, and adequate consideration has not been
given to the high-energy-consuming industrial loads leading to inefficient
results. This paper thus focuses on the load decomposition of the industrial
park loads (IPL). The commonly used parameters in a conventional method are
however inapplicable in high-energy-consuming industrial loads. Therefore, a
more robust approach is developed comprising a three-algorithm model to achieve
this goal on the IPL. First, the improved variational mode decomposition (IVMD)
algorithm is introduced to denoise the training data of the IPL and improve its
stability. Secondly, the convolutional neural network (CNN) and simple
recurrent units (SRU) joint algorithms are used to achieve a non-intrusive and
non-invasive decomposition process of the IPL using a double-layer deep
learning network based on the IPL characteristics. Specifically, CNN is used to
extract the IPL data characteristics while the improved long and short-term
memory (LSTM) network, SRU, is adopted to develop the decomposition model and
further train the load data. Through the robust decomposition process, the
underlying relationship in the load consumption is extracted. The results
obtained from the numerical examples show that this approach outperforms the
state-of-the-art in the conventional decomposition process.
