安徽省新能源利用与节能省级实验室（合肥工业大学），安徽省 合肥市 230009
(Anhui Provincial Laboratory of Renewable Energy Utilization and Energy Saving (Hefei University of Technology), Hefei 230009, China)
Loads in the renewable energy grid is more sensitive to various feature factors， it is a new challenge for short-term load forecasting method when facing the massive feature data. Aiming at the in the renewable energy grid with high-dimensional feature data, a short-term load forecasting method based on bi-directional long-short-term memory(Bi-LSTM) network considering feature selection is proposed. The sample data are firstly clustered and mapped into the weight-induced space according to density, by defining a structure of interval measurement data, the maximum interval sum is used as the objective function. In order to achieve the sparsity of the solution space, a regular term is added into the objective function, and the feature weights are solved by the gradient descent algorithm. Hyper-parameters such as the feature selection threshold are determined by pre-tests, and then the required feature factors are selected. Finally, the Bi-LSTM network is used to carry out load forecasting based on the selected data. Taking the renewable energy grid in a certain area of China as an example, the effectiveness of the proposed method is verified, compared with the traditional methods, the proposed method is more accurate and applicative.
YANG Long, WU Hongbin, DING Ming, et al. Short-term Load Forecasting in Renewable Energy Grid Based on Bi-directional Long-short-term Memory Network Considering Feature Selection[J/OL]. Automation of Electric Power Systems, http://doi. org/10.7500/AEPS20200202002.