An Ensemble Feature Selection Method for Short-Term Electrical Load Forecasting

Chen, Junxingxu; Li, Ting; Zou, Yanhui; Wang, Guorui; Ye, Huisheng; Lv, Fengyi

doi:10.1109/ei247390.2019.9062042

Cited by 15 publications

(9 citation statements)

References 11 publications

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“…It is necessary to construct a forecasting model by collecting and analyzing multiple input variables. Final input variables for distribution line peak load forecasting should be selected, and a forecasting model should be presented by comparing the performance of forecasting models according to the combination of input variables through correlation analysis of input variables and output variables such as Pearson correlation, Spearman correlation, and mutual information analysis [17][18][19][20][21][22]. Figure 4 shows the input variable selection process for constructing a machine learning model.…”

Section: Input Variable Selection Processmentioning

confidence: 99%

“…The mutual information is called interdependence information and is an indicator that can determine the correlation between two data sets in addition to the linear correlation. The Pearson correlation coefficient, which is often used in correlation analysis, analyzes linear correlations, and the Spearman correlation coefficient has a high correlation even in the case of non-linear monotonic functions by analyzing the linear correlation of rank [17][18][19][20][21][22]. Figures 6 and 7 show the results of analyzing the correlation coefficients of 46 input variables, representing that the first row or column shows the correlation between the month peak and other input variables.…”

Section: Input and Output Correlation Analysismentioning

confidence: 99%

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A Study on Load Forecasting of Distribution Line Based on Ensemble Learning for Mid- to Long-Term Distribution Planning

et al. 2022

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The complexity and uncertainty of the distribution system are increasing as the connection of distributed power sources using solar or wind energy is rapidly increasing, and digital loads are expanding. As these complexity and uncertainty keep increasing the investment cost for distribution facilities, optimal distribution planning becomes a matter of greater focus. This paper analyzed the existing mid-to-long-term load forecasting method for KEPCO’s distribution planning and proposed a mid- to long-term load forecasting method based on ensemble learning. After selecting optimal input variables required for the load forecasting model through correlation analysis, individual forecasting models were selected, which enabled the derivation of the optimal combination of ensemble load forecast models. This paper additionally offered an improved load forecasting model that considers the characteristics of each distribution line for enhancing the mid- to long-term distribution line load forecasting process for distribution planning. The study verified the performance of the proposed method by comparing forecasting values with actual values.

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Section: Input Variable Selection Processmentioning

confidence: 99%

Section: Input and Output Correlation Analysismentioning

confidence: 99%

A Study on Load Forecasting of Distribution Line Based on Ensemble Learning for Mid- to Long-Term Distribution Planning

et al. 2022

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“…In typical engineering applications of machine learning, feature selection methods can be roughly classified into filtering, wrapping, and embedding methods. Feature dimension reduction methods include the principal component and linear discriminant analyses [5,[10][11][12][13]. Although the machine learning feature engineering method has its general advantages in application, the interpretability of the finally formed load features is not clear and sufficiently intuitive.…”

Section: Introductionmentioning

confidence: 99%

Short-term load forecasting method based on fuzzy optimization combined model of load feature recognition

Xie,

Lin,

Xie

2024

Preprint

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With the continuous development of smart grid construction and the gradual improvement of power market operation mechanisms, the importance of power load forecasting is continually increasing. In this study, a short-term load prediction method based on the fuzzy optimization combined model of load feature recognition was designed to address the problems of weak generalization ability and poor prediction accuracy of the conventional feedforward neural network prediction model. First, the Douglas–Peucker algorithm and fuzzy optimization theory of load feature recognition was analyzed, and the combined prediction model was constructed. Second, data analysis and pre-processing were performed based on the actual historical load data of a certain area and the corresponding meteorological and calendar rule information data. Finally, a practical example was used to test and analyze the short-term load forecasting effect of the fuzzy optimization combined model. The calculation results proved that the presented fuzzy optimization combined model of load feature recognition outperformed the conventional model in terms of computational efficiency and specific performance; therefore, the proposed model supports further development of actual power load prediction.

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“…At this point, feature selection plays an important role in removing information complexity. Advanced ML methods such as mutual information (MI), ReliefF (RF), and correlation-based selection (CFS) can be used to interpret the grade of candidate features and give weights to features [31].…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Deep-Learning-Based Model for Hour-Ahead Load Forecasting with a Feature Selection Approach: A Comparative Study with State-of-the-Art Methods

Yaprakdal¹

2022

Energies

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The realization of load forecasting studies within the scope of forecasting periods varies depending on the application areas and estimation purposes. It is mainly carried out at three intervals: short-term, medium-term, and long-term. Short-term load forecasting (STLF) incorporates hour-ahead load forecasting, which is critical for dynamic data-driven smart power system applications. Nevertheless, based on our knowledge, there are not enough academic studies prepared with particular emphasis on this sub-topic, and none of the related studies evaluate STLF forecasting methods in this regard. As such, machine learning (ML) and deep learning (DL) architectures and forecasters have recently been successfully applied to STLF, and are state-of-the-art techniques in the energy forecasting area. Here, hour-ahead load forecasting methods, the majority of which are frequently preferred high-performing up-to-date methods in the literature, were first examined based on different forecasting techniques using two different aggregated-level datasets and observing the effects of these methods on both. Case and comparison studies have been conducted on these high-performing methods before, but there are not many examples studied using data from two different structures. Although the data used in this study were different from each other in terms of the time step, they also had very different and varied features. In addition, feature selection was studied on both datasets and a backward-eliminated exhaustive approach based on the performance of the artificial neural network (ANN) on the validation set was proposed for the development study of the forecasting models. A new DL-based ensemble approach was proposed after examining the results obtained on two separate datasets by applying the feature selection approach to the working forecasting methods, and the numerical results illustrate that it can significantly improve the forecasting performance compared with these up-to-date methods.

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An Ensemble Feature Selection Method for Short-Term Electrical Load Forecasting

Cited by 15 publications

References 11 publications

A Study on Load Forecasting of Distribution Line Based on Ensemble Learning for Mid- to Long-Term Distribution Planning

A Study on Load Forecasting of Distribution Line Based on Ensemble Learning for Mid- to Long-Term Distribution Planning

Short-term load forecasting method based on fuzzy optimization combined model of load feature recognition

An Ensemble Deep-Learning-Based Model for Hour-Ahead Load Forecasting with a Feature Selection Approach: A Comparative Study with State-of-the-Art Methods

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