Article
Version 1
Preserved in Portico This version is not peer-reviewed
A Hybrid Stacking Model for Enhanced Short-Term Load Forecasting
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Version 1
: Received: 20 June 2024 / Approved: 21 June 2024 / Online: 24 June 2024 (04:24:43 CEST)
A peer-reviewed article of this Preprint also exists.
Guo, F.; Mo, H.; Wu, J.; Pan, L.; Zhou, H.; Zhang, Z.; Li, L.; Huang, F. A Hybrid Stacking Model for Enhanced Short-Term Load Forecasting. Electronics 2024, 13, 2719. Guo, F.; Mo, H.; Wu, J.; Pan, L.; Zhou, H.; Zhang, Z.; Li, L.; Huang, F. A Hybrid Stacking Model for Enhanced Short-Term Load Forecasting. Electronics 2024, 13, 2719.
Abstract
The high penetration of distributed energy resources poses significant challenges to the dispatch and operation of power systems. Improving the accuracy of short-term load forecasting (STLF) can optimize grid management, leading to increased economic and social benefits. Currently, some simple AI and hybrid models has issues to deal with and struggle with multivariate dependencies, long-term dependencies, and non-linear relationships. This paper proposes a novel hybrid model for short-term load forecasting (STLF) that integrates multiple AI models with Lasso regression using the stacking technique. The base learners include ANN, XgBoost, LSTM, Stacked LSTM, and Bi-LSTM, while Lasso regression serves as the meta-learner. By considering factors such as temperature, rainfall, and daily electricity prices, the model aims to more accurately reflect real-world conditions and enhance predictive accuracy. Empirical analyses on real-world datasets from Australia and Spain show significant improvements in forecasting accuracy, with a substantial reduction in Mean Absolute Percentage Error (MAPE) compared to existing hybrid models and individual AI models. This research highlights the efficiency of the stacking technique in improving STLF accuracy, suggesting potential operational efficiency benefits for the power industry.
Keywords
Smart Grid; Short Term Load Forecasting; Deep Learning; Stacking Approach; Time Series Analysis
Subject
Engineering, Electrical and Electronic Engineering
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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