A novel stacked generalization ensemble-based hybrid LGBM-XGB-MLP model for Short-Term Load Forecasting. (1st January 2021)

• Record Type: Journal Article
• Authors: Massaoudi, Mohamed; Refaat, Shady S.; Chihi, Ines; Trabelsi, Mohamed; Oueslati, Fakhreddine S.; Abu-Rub, Haitham
• Is Part Of: Energy. Volume 214(2021)
• Publication Date: 2021-01-01
• Subjects: Light gradient boosting machine (LGBM) -- Multi-layer perceptron (MLP) -- Short-term load forecasting (STLF) -- Staking approach -- Extreme gradient boosting machine (XGB) -- Hyperparameter optimization; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2020.118874
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: This paper proposes an effective computing framework for Short-Term Load Forecasting (STLF). The proposed technique copes with the stochastic variations of the load demand using a stacked generalization approach. This approach combines three models, namely, Light Gradient Boosting Machine (LGBM), eXtreme Gradient Boosting machine (XGB), and Multi-Layer Perceptron (MLP). The inner mechanism of Stacked XGB-LGBM-MLP model consists of generating a meta-data from XGB and LGBM models to compute the final predictions using MLP network. The performance of the proposed Stacked XGB-LGBM-MLP model is validated using two datasets from different locations: Malaysia and New England. The main contributions of this paper are: 1) A novel stacking ensemble-based algorithm is proposed; 2) An effective STLF technique is introduced; 3) A critical multi-study analysis for hyperparameter optimization with five techniques is comprehensively performed; 4) A performance comparative study using two datasets and reference models is conducted. Several case studies have been carried out to prove the performance superiority of the proposed model compared to both existing benchmark techniques and hybrid models. Highlights: A novel Stacked XGB-LGBM-MLP model is proposed to improve the overall regression performance. A novel STLF technique is explored and developed in this study. A comparative analysis of five hyperparameter optimization algorithms was comprehensively presented for STLF. An assessment of the proposed technique is conducted using two real datasets. A comparative study with the recent benchmark techniques is performed.