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ISSN (Print): 2352-0965
ISSN (Online): 2352-0973

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Research Article

Hybrid Electricity Consumption Prediction Based on Spatiotemporal Correlation

Author(s): Shenzheng Wang*, Yi Wang, Sijin Cheng, Xiao Zhang, Xinyi Li and Tengchang Li

Volume 15, Issue 4, 2022

Published on: 17 August, 2022

Page: [289 - 300] Pages: 12

DOI: 10.2174/2352096515666220623120726

Price: $65

Abstract

Background: Electricity consumption forecast is an important basis for the power system to achieve regional electricity balance and electricity spot market transactions.

Objective: In view of the fact that many electricity consumption prediction models do not make good use of the correlation of data in the time dimension and space dimension, this paper proposes a day-ahead forecasting model based on spatiotemporal correction, which further improves the forecasting accuracy of electricity demand.

Methods: Firstly, the long short-term memory (LSTM) model is used to construct the forecasting model. Secondly, from the perspectives of time correlation and space correlation, meanwhile considering calendar factors and meteorological factors, the K-Nearest Neighbors (KNN) model is taken to construct correction models, which can correct the forecasting results of LSTM.

Results: According to the analysis of power consumption data of 9 areas in New England, the mean absolute percentage error (MAPE), mean absolute error (MAE), and root mean square error (RMSE) of time dimension correction model are reduced by 0.35%, 5.87% and 5.06%, and the 3 evaluation metrics in space dimension are decreased by 0.52%, 6.82% and 7.06% on average.

Conclusion: The results prove that the models proposed in this paper are effective.

Keywords: Electricity consumption forecast, long short-term memory, time dimension correction, space dimension correction, K-Nearest Neighbors, day-ahead power demand prediction.

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