Load forecasting based on knowledge flow and transfer learning

Wenjun ZHU; Sining WANG; Xiaoxin GAO; Qian ZHENG

doi:10.11959/j.issn.1000-0801.2022074

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Load forecasting based on knowledge flow and transfer learning

Research and Development | 更新时间：2024-06-05

- Load forecasting based on knowledge flow and transfer learning
- Telecommunications Science Vol. 38, Issue 5, Pages: 114-123(2022)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2022074
  CLC： TM714；TM715；TP181
- Published Online：2022-05，
  
  Published：20 May 2022
- 稿件说明：
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Wenjun ZHU, Sining WANG, Xiaoxin GAO, et al. Load forecasting based on knowledge flow and transfer learning[J]. Telecommunications science, 2022, 38(5): 114-123.
DOI：

Wenjun ZHU, Sining WANG, Xiaoxin GAO, et al. Load forecasting based on knowledge flow and transfer learning[J]. Telecommunications science, 2022, 38(5): 114-123. DOI： 10.11959/j.issn.1000-0801.2022074.

摘要

在万物互联、全面感知、智能决策的大数据信息化时代，大数据信息的采集、大量信号的处理等仍存在数据冗余、计算量大、成本高、不及时和无特征性的缺点。通过迁移学习方法，利用基于权重影响因子进行信息融合的知识流动体系，为物联感知系统提供协助分析并简化计算。在物联感知系统采用迁移学习加数据融合的知识流动方式，以地区用电功率部分数据做短期负荷预测的仿真计算，分析用户用电行为影响因子，训练得到影响因子最佳权重分配，为用电耗率预判提供依据。结果表明，通过该方式，能够清晰辨别用电行为特征，并根据用电特征预判用电耗能。

Abstract

In all things connected

comprehensive perception

intelligent decision-making information era of big data

in the information acquisition of big data and a large amount of signal processing

there are still large amount of data redundancy

calculation and the shortcoming of high cost

not in time

and no marked.The transfer learning was applied

and the knowledge flow system based on the weight impact factor for information fusion was integrated to assist the analysis and simplify the calculation for the IoT sensing system.The knowledge flow mode of transfer learning and data fusion was adopted in the IoT sensing system

and the simulation calculation of short-term load prediction was made based on the partial data of regional power consumption.The influencing factors of users’ electricity consumption behavior were analyzed

and the optimal weight distribution of influencing factors was obtained through training

so as to predict the power consumption rate.The results show that in this way

it can clearly identify the characteristics of electricity consumption behavior

and predict the energy consumption according to the characteristics of electricity consumption.

关键词

Keywords

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