A novel stacked generalization ensemble-based hybrid LGBM-XGB-MLP model for Short-Term Load Forecasting

Massaoudi, Mohamed; Refaat, Shady S.; Chihi, Inès; Trabelsi, Mohamed; Oueslati, Fakhreddine S.; Abu‐Rub, Haitham

doi:10.1016/j.energy.2020.118874

Cited by 230 publications

(94 citation statements)

References 23 publications

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“…Future work within this specific research can include more historical load records to train models in particular situations, like holidays. Another approach to enhance the holidays' forecast is to develop a specific model for holidays and merge the predictions with a regular days model [16] and use stacking techniques to enhance the forecast accuracy, even though it increases the training and predicting time [73].…”

Section: Limitations and Future Workmentioning

confidence: 99%

Short-Term Electricity Load Forecasting with Machine Learning

Madrid

António

2021

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An accurate short-term load forecasting (STLF) is one of the most critical inputs for power plant units’ planning commitment. STLF reduces the overall planning uncertainty added by the intermittent production of renewable sources; thus, it helps to minimize the hydrothermal electricity production costs in a power grid. Although there is some research in the field and even several research applications, there is a continual need to improve forecasts. This research proposes a set of machine learning (ML) models to improve the accuracy of 168 h forecasts. The developed models employ features from multiple sources, such as historical load, weather, and holidays. Of the five ML models developed and tested in various load profile contexts, the Extreme Gradient Boosting Regressor (XGBoost) algorithm showed the best results, surpassing previous historical weekly predictions based on neural networks. Additionally, because XGBoost models are based on an ensemble of decision trees, it facilitated the model’s interpretation, which provided a relevant additional result, the features’ importance in the forecasting.

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Section: Limitations and Future Workmentioning

confidence: 99%

Short-Term Electricity Load Forecasting with Machine Learning

Madrid

António

2021

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“…PLS is a straightforward dimensionality reduction technique that maps the variables in a new feature space with lower dimensions. The Variable Importance of load Patterns (VIP) for 32 features is shown in Regarding Figure 9, the most important features are hour, workday, temperature and lagged load (t − x) with x ∈ [1,2,3,4,5,6,7,11,12,13,17,18,19,20,21,22,23]. Thus, the selected threshold is VIP = 0.5.…”

Section: Data Pre-processing and Feature Engineeringmentioning

confidence: 99%

“…Alternatively, Deep Learning (DL) presents cutting-edge technology to model complex and nonlinear systems with higher generalization capabilities and better reliability with the increase in depth [8]. Some recent advances of DL methods could be found in [9][10][11][12][13]. Nevertheless, DL techniques remain less explored compared to the large deployment of ML techniques.…”

Section: Introductionmentioning

confidence: 99%

PLS-CNN-BiLSTM: An End-to-End Algorithm-Based Savitzky–Golay Smoothing and Evolution Strategy for Load Forecasting

et al. 2020

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This paper proposes an effective deep learning framework for Short-Term Load Forecasting (STLF) of multivariate time series. The proposed model consists of a hybrid Convolutional neural network-Bidirectional Long Short-Term Memory (CBiLSTM) based on the Evolution Strategy (ES) method and the Savitzky–Golay (SG) filter (SG-CBiLSTM). The adopted methodology incorporates the virtue of different prepossessing blocks to enhance the performance of the CBiLSTM model. In particular, a data-augmentation strategy is employed to synthetically improve the feature representation of the CBiLSTM model. The augmented data is forwarded to the Partial Least Square (PLS) method to select the most informative features above the predefined threshold. Next, the SG algorithm is computed for smoothing the load to enhance the learning capabilities of the underlying system. The structure of the SG-CBiLSTM for the ISO New England dataset is optimized using the ES technique. Finally, the CBiLSTM model generates output forecasts. The proposed approach demonstrates a remarkable improvement in the performance of the original CBiLSTM model. Furthermore, the experimental results strongly confirm the high effectiveness of the proposed SG-CBiLSTM model compared to the state-of-the-art techniques.

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“…Furthermore, the key stimulus for energy generation, namely, the irradiation, is only available in the daytime. Therefore, forecasting models have been widely employed for PV systems to estimate the generated PV power from one side and load demand from the other side to ensure a smart demand response and effective energy management [3], [4]. During the last few years, Time Series Forecasting (TSF) becomes a dynamic research area supported by the exponential use of Big Data in all the research fields due to the explosive development of information and communication technologies and significant hardware improvement [5].…”

Section: Introductionmentioning

confidence: 99%

An Effective Hybrid NARX-LSTM Model for Point and Interval PV Power Forecasting

et al. 2021

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This paper proposes an effective Photovoltaic (PV) Power Forecasting (PVPF) technique based on hierarchical learning combining Nonlinear Auto-Regressive Neural Networks with exogenous input (NARXNN) with Long Short-Term Memory (LSTM) model. First, the NARXNN model acquires the data to generate a residual error vector. Then, the stacked LSTM model, optimized by Tabu search algorithm, uses the residual error correction associated with the original data to produce a point and interval PVPF. The performance of the proposed PVPF technique was investigated using two real datasets with different scales and locations. The comparative analysis of the NARX-LSTM with twelve existing benchmarks confirms its superiority in terms of accuracy measures. In summary, the proposed NARX-LSTM technique has the following major achievements: 1) Improves the prediction performance of the original LSTM and NARXNN models; 2) Evaluates the uncertainties associated with point forecasts with high accuracy; 3) Provides a high generalization capability for PV systems with different scales. Numerical results of the comparison of the proposed NARX-LSTM method with two real-world PV systems in Australia and USA demonstrate its improved prediction accuracy, outperforming the benchmark approaches with an overall normalized Rooted Mean Squared Error (nRMSE) of 1.98% and 1.33% respectively. INDEX TERMS Long Short-Term Memory (LSTM), photovoltaic power forecasting, Nonlinear Auto-Regressive Neural Networks with exogenous input (NARXNN), Tabu Search Algorithm (TSA).

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A novel stacked generalization ensemble-based hybrid LGBM-XGB-MLP model for Short-Term Load Forecasting

Cited by 230 publications

References 23 publications

Short-Term Electricity Load Forecasting with Machine Learning

Short-Term Electricity Load Forecasting with Machine Learning

PLS-CNN-BiLSTM: An End-to-End Algorithm-Based Savitzky–Golay Smoothing and Evolution Strategy for Load Forecasting

An Effective Hybrid NARX-LSTM Model for Point and Interval PV Power Forecasting

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