Open Access

ARTICLE

Investigating Periodic Dependencies to Improve Short-Term Load Forecasting

Jialin Yu^1,*, Xiaodi Zhang², Qi Zhong¹, Jian Feng¹

1 State Grid Zhejiang Jiaxing Electric Power Co., Ltd., Jiaxing, 314000, China
2 State Grid Zhejiang Electric Power Co., Ltd., Hangzhou, 310000, China

* Corresponding Author: Jialin Yu. Email:

Energy Engineering 2024, 121(3), 789-806. https://doi.org/10.32604/ee.2023.043299

Received 28 June 2023; Accepted 09 October 2023; Issue published 27 February 2024

Abstract

With a further increase in energy flexibility for customers, short-term load forecasting is essential to provide benchmarks for economic dispatch and real-time alerts in power grids. The electrical load series exhibit periodic patterns and share high associations with metrological data. However, current studies have merely focused on point-wise models and failed to sufficiently investigate the periodic patterns of load series, which hinders the further improvement of short-term load forecasting accuracy. Therefore, this paper improved Autoformer to extract the periodic patterns of load series and learn a representative feature from deep decomposition and reconstruction. In addition, a novel multi-factor attention mechanism was proposed to handle multi-source metrological and numerical weather prediction data and thus correct the forecasted electrical load. The paper also compared the proposed model with various competitive models. As the experimental results reveal, the proposed model outperforms the benchmark models and maintains stability on various types of load consumers.

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Keywords

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