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Regular Paper | Open Access

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

Kangping Li1,2, Yuqing Wang3, Ning Zhang2( ), Fei Wang3,4,5, Chunyi Huang6
College of Smart Energy, Shanghai Jiao Tong University, Shanghai 200240, China
Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
Department of Electrical Engineering, North China Electric Power University, Baoding 071003, China
State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources (North China Electric Power University), Beijing 102206, China
Hebei Key Laboratory of Distributed Energy Storage and Microgrid (North China Electric Power University), Baoding 071003, China
Key Laboratory of Control of Power Transmission and Conversion, Ministry of Education (Shanghai Jiao Tong University), Shanghai 200240, China
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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.

Keywords

SpatiotemporalElectricity consumption forecastingGreedy clusteringGrid searching

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CSEE Journal of Power and Energy Systems
Volume 9 Issue 5,
September 2023
Pages 1980-1984
DOI: 10.17775/CSEEJPES.2022.01040
Cite this article:
Li K, Wang Y, Zhang N, et al. Spatio-temporal Granularity Co-optimization Based Monthly Electricity Consumption Forecasting. CSEE Journal of Power and Energy Systems, 2023, 9(5): 1980-1984. https://doi.org/10.17775/CSEEJPES.2022.01040

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Received: 20 February 2022
Revised: 25 May 2022
Accepted: 03 July 2022
Published: 12 October 2022
© 2022 CSEE.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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