Integrated Load Forecasting Model of Multi-Energy System Based on Markov Chain Improved Neural Network

Abstract

Aiming at the uncertain relationship between renewable energy power generation or functional system output and energy consumption load in multi-energy system, a multi-energy comprehensive load forecasting model is studied to solve the problem of source-load matching. This paper studies the load characteristic analysis method of multi-energy system after cooling, heating and electricity load integration, establishes a comprehensive load data processing model based on membership function, and establishes a comprehensive load neural network forecasting model according to the time characteristics of comprehensive load. The non-linear correlation of thermal and cooling load in time is studied, and a modified method of comprehensive load neural network prediction model based on Markov chain is proposed. Finally, taking a typical multi-energy system as an example, a comprehensive load forecasting simulation system model is established. The simulation results show that the accuracy of the proposed algorithm can meet the multi-energy scheduling requirements of multi-energy system.