Spatio-temporal Granularity Co-optimization Based Monthly Electricity Consumption Forecasting

Kangping Li; Yuqing Wang; Ning Zhang; Fei Wang; Chunyi Huang

doi:10.17775/CSEEJPES.2022.01040

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Spatio-temporal Granularity Co-optimization Based Monthly Electricity Consumption Forecasting

Regular Papers | 更新时间：2026-10-15

- Spatio-temporal Granularity Co-optimization Based Monthly Electricity Consumption Forecasting
- CSEE Journal of Power and Energy Systems Vol. 9, Issue 5, Pages: 1980-1984(2023)
- 作者机构：
  
  1. College of Smart Energy, Shanghai Jiao Tong University,Shanghai,China
  2. Department of Electrical Engineering, Tsinghua University,Beijing,China
  3. Department of Electrical Engineering, North China Electric Power University,Baoding,China
  4. State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University,Beijing,China
  5. Hebei Key Laboratory of Distributed Energy Storage and Microgrid, North China Electric Power University,Baoding,China
  6. Key Laboratory of Control of Power Transmission and Conversion, Ministry of Education (Shanghai Jiao Tong University),Shanghai,China
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2022.01040
  CLC：
- Published：01 September 2023
- 稿件说明：
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Kangping Li, Yuqing Wang, Ning Zhang, et al. Spatio-temporal Granularity Co-optimization Based Monthly Electricity Consumption Forecasting[J]. CSEE Journal of Power and Energy Systems, 2023, 9(5): 1980-1984.
DOI：

Kangping Li, Yuqing Wang, Ning Zhang, et al. Spatio-temporal Granularity Co-optimization Based Monthly Electricity Consumption Forecasting[J]. CSEE Journal of Power and Energy Systems, 2023, 9(5): 1980-1984. DOI： 10.17775/CSEEJPES.2022.01040.

摘要

Abstract

Monthly electricity consumption forecasting (ECF) plays an important role in power system operation and electricity market trading. Widespread popularity of smart meters enables collection of fine-grained load data

which provides an opportunity for improvement of monthly ECF accuracy. In this letter

a spatio-temporal granularity co-optimization-based monthly ECF framework is proposed

which aims to find an optimal combination of temporal granularity and spatial clusters to improve monthly ECF accuracy. The framework is formulated as a nested bi-layer optimization problem. A grid search method combined with a greedy clustering method is proposed to solve the optimization problem. Superiority of the proposed method has been verified on a real smart meter dataset.

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Keywords

references

11. Ausgrid. ( 2020, Aug. 31 ). Distribution Zone Substation Information Data to Share . [Online ] . Available: http://www.ausgrid.com.au/Common/About-us/Corporate-information/Data-to-share/DistZone-subs.aspx#.WYD6KenauUl.

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