面向省内电网企业代理购电的液体神经网络推理模型OA

Intra-provincial power grid enterprises face dual uncertainties in agency power procurement due to fluctuations of renewable energy output and electricity spot market prices.Traditional price-volume prediction models are poorly suited for the highly volatile scenarios of agency power procurement.To address this issue,an inference model for trading trends based on a liquid neural network(LNN)is developed.This model considers the multi-factor coupling characteristics of agency power procurement and quantifies the spot trading trends using a vertical-horizontal filter indicator from financial domains.By incorporating feature data such as day-ahead settlement power volume,settlement power price,cumulative temperature,and supply-demand ratio,the LNN is adopted to dynamically adjust neuron states and liquid time constants,achieving the adaptive learning of complex temporal patterns in the electricity market.Finally,the verification is conducted by using the price-volume data from the electricity spot market in a region of Hebei Province,China.The results demonstrate that the proposed model can effectively identify both the trend and oscillation intervals in agency power procurement data,outperforming conventional price-volume models in terms of trend segmentation and inflection point prediction.