Small sample load forecasting method considering characteristic distribution similarity based on improved WGAN
For a new user of an integrated energy sysytem,it is much more difficult to develop an accurate load forecast-ing model due to the lack of historical data.A small sample load forecasting method based on the improved Wasserstein generative adversarial nets(WGAN)is proposed based on the transfer learning theory.First,the maximal information co-efficient method is used to quantify the correlation among the impact characteristics and the load.Next,the source domain characteristic sequence is segmented and the edit distance on real sequence between each segmented sequence and the small sample in the target domain is calculated to determine the initial source domain.Then,the convolution neural network and long short-term memory model are introduced to establish the source domain prediction network.The spatial distribution of load characteristics both in target domain and source domain is aligned by WGAN,and the local characteristic loss is added to the optimal transport cost function to improve the stability and rapidity of training process.Finally,the network after adversarial training is used for the target domain load forecasting.The method proposed is used to test a small sample in a certain area and the result shows that the algorithm proposed in this paper turns out to be more accurate compared with other prediction models.
load forecasttransfer learningsmall sampleimproved Wasserstein generative adversarial netscharacter-istic distributionoptimal transport
万方数据
维普
