Net Load Forecasting Method Based on Feature-Weighted Stacking Ensemble Learning

doi:10.12204/j.issn.1000-7229.2022.09.011

Abstract

Abstract:

At present, a large amount of new energy is connected to the user side, and the actual power load minus the power generated by the new energy (hereinafter referred to as “net load”) is usually used for prediction research. Due to the strong randomness of new energy power generation, net load has strong uncertainty and poor regularity, which makes it difficult to predict accurately. To this end, this paper proposes a net load prediction method based on the feature-weighted Stacking ensemble algorithm. Firstly, through the analysis of the prediction performance and difference of different prediction models, this paper chooses Long Short-Term Memory Network, Elman Neural Network, Random Forest Tree and Least Squares Support Vector Machine as stacking ensemble learners. Secondly, because the traditional Stacking ensemble prediction model ignores the differences between learners, the model’s prediction ability is insufficient. Therefore, this paper weights the features of the learners according to the prediction accuracy to correct the prediction error introduced by different learners. Finally, the measured data in the German TENNET area is analyzed as an example. The simulation results show that, compared with the single forecasting model and the traditional Stacking integrated forecasting method, the payload prediction method based on feature-weighted stacking ensemble learning has higher forecasting accuracy in sunny, cloudy, rainy, snowy and other weather conditions.

Key words: renewable energy, net load forecasting, stacking ensemble learning, feature weighting

CLC Number:

TM714

BAO Haibo, WU Yangchen, ZHANG Guoying, LI Jiangwei, GUO Xiaoxuan, LI Jinghua. Net Load Forecasting Method Based on Feature-Weighted Stacking Ensemble Learning[J]. ELECTRIC POWER CONSTRUCTION, 2022, 43(9): 104-116.

Figures/Tables 17

Fig.1 The framework for Stacking ensemble forecasting

Fig.2 Schematic diagram of k-fold cross validation

Fig.3 Net load forecasting based on feature-weighted Stacking framework

Table 1 The simulation scene

Table 2 The MAPE of single prediction model %

Fig.4 Correlation analysis of forecasting error for each model

Table 3 MAPE of different prediction models on December

Fig.5 MAPE of net load forecast for the 24 hours of December by different forecasting methods

Fig.6 Comparison between various prediction models over the weekend

Fig.7 Comparison between various prediction models on sunny days

Table 4 The MAPE of prediction models for sunny days

Fig.8 Comparison between various prediction models on cloudy days

Table 5 The MAPE of prediction models for cloudy days

Fig.9 Comparison between various prediction models on snowy days

Table 6 The MAPE of prediction models for snowy days

Fig.10 Comparison between various prediction models on rainy days

Table 7 The MAPE of prediction models for rainy days

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