Journal of Shanghai Jiao Tong University ›› 2021, Vol. 55 ›› Issue (12): 1544-1553.doi: 10.16183/j.cnki.jsjtu.2021.296

Special Issue: 《上海交通大学学报》2021年“电气工程”专题 《上海交通大学学报》2021年12期专题汇总专辑

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A Multi-Level Collaborative Load Forecasting Method for Distribution Networks Based on Distributed Optimization

TAN Jia1, LI Zhiyi1(), YANG Huan1, ZHAO Rongxiang1, JU Ping1,2   

  1. 1. College of Electrical Engineering, Zhejiang University, Hangzhou 310007, China
    2. College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China
  • Received:2021-07-30 Online:2021-12-28 Published:2021-12-30
  • Contact: LI Zhiyi


At present, new elements such as distributed new energy and electric vehicles have emerged in the distribution network, which changes the composition of loads, enriches the connotation of loads, and poses severe challenges to load forecasting. In fact, loads are aggregated in a bottom-up manner in multiple voltage levels of the distribution network, but such hierarchical characteristics are rarely considered in current load forecasting researches. Therefore, a multi-level load collaborative forecasting method based on the distributed optimization algorithm is proposed aimed at ensuring the bottom-up aggregation consistency of loads and jointly improving the performance of load forecasting at all levels. First, the distributed optimization concept based on the alternating direction method of multipliers is adopted to construct a multi-level load collaborative forecasting framework which adapts to the hierarchical characteristics of distribution network and has less data interaction. Then, a specific forecasting method based on the long short term-memory neural network and federated learning is proposed. By aggregating the bottom load forecasting results step by step, the bottom-up integrated load forecasting of distribution network can be realized. The results of calculation examples show that the proposed method has a high accuracy and a great application prospect.

Key words: multi-level load forecasting, alternating direction method of multipliers, long short-term memory neural network, federated learning

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