Short-Term Load Forecasting of Distributed Energy Supply System Based on Elman Neural Network

Nowadays, the situation of energy utilization in the world is becoming more and more serious, and the environmental problems can not be ignored. How to achieve energy efficiency, environmental protection has become a global hot issue. Distributed energy supply system will become an important and effective way to realize the coordinated development of economy and energy. In the design of distributed energy supply system, the characteristics of distributed energy supply system are often not fully taken into account, which results in too large capacity selection of the unit, which can not reflect the superiority and benefit of the distributed energy supply system. Therefore, it is more important for the success of the project to carry out accurate load forecasting of cooling heating and power load. A method of load forecasting for distributed energy supply system based on Elman neural network is presented in this paper, in order to be able to predict the load more accurately.