Industrial users load pattern extraction method based on multidimensional electrical consumption feature construction - Wang, Li; Zhao, Jian; Xia, Xiangwu; Liu, Jun; Lu, Yang; Zhao, Lei (Frontiers in Energy Research, 2023)
The rapid development of renewable energy generation aggravates the imbalance between supply and demand in power grid, and exploring the potential of demand side resource can effectively improve such problems. Industrial users(IU) is an important demand response resource of power grid, and mining the load patterns of IU is the basis of studying the demand response ability of IU, which plays an important role in the safe operation and lean management of power grid. Lately, the popularity of advanced metering infrastructures provides data support for studying the load patterns of IU. However, the high dimension and the complex nonlinear relationship of IU's load data bring difficulties to the task of clustering. To solve the above problems, this paper proposes a load pattern extraction method based on multidimensional electrical consumption feature construction. Firstly, industrial load characteristic set of IU is created with five load characteristic indexes weighted by improved entropy weight method. In addition, convolutional autoencoder is established to extract the temporal feature of industrial load data which is combined with industrial load characteristic set to build a multidimensional feature set(MFS) for IU and finish multidimensional electrical consumption feature construction(MECFC). Then, MFS is used as the input of Self-Organization Map network to select the initial clustering centers of K-means algorithm, overcoming the problem of local optimal solution, and complete the IU daily load clustering. Finally, in case study the algorithm based on MECFC is verified to have better performance in stability and clustering effect than traditional methods.