A New Input Selection Algorithm Using the Group Method of Data Handling and Bootstrap Method for Support Vector Regression Based Hourly Load Forecasting

Abstract

Electric load forecasting is indispensable for the effective planning and operation of power systems. Various decisions related to power systems depend on the future behavior of loads. In this paper, we propose a new input selection procedure, which combines the group method of data handling (GMDH) and bootstrap method for support vector regression based hourly load forecasting. To construct the GMDH network, a learning dataset is divided into training and test datasets by bootstrapping. After constructing GMDH networks several times, the inputs that appeared frequently in the input layers of the completed networks were selected as the significant inputs. Filter methods based on linear correlation and mutual information (MI) were employed as comparison methods, and the performance of hybrids of the filter methods and the proposed method were also confirmed. In total, five input selection methods were compared. To verify the performance of the proposed method, hourly load data from South Korea was used and the results of one-hour, one-day and one-week-ahead forecasts were investigated. The experimental results demonstrated that the proposed method has higher prediction accuracy compared with the filter methods. Among the five methods, a hybrid of an MI-based filter with the proposed method shows best prediction performance.

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“…Therefore, the results confirm the validity of their proposed short-term wind energy prediction method. Yu et al [16] introduced a new import selection program that combines the group method of data handling and SVR to predict short-term hourly load. After configuring the group method of data handling networks multiple times under the same experimental conditions, they also set the network numerous times.…”

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“…Liu et al enhanced GMDH networks by introducing elastic net regression and enriching with difference degree weighting optimization for forecasting hourly loads in data sets pertaining to three locations in China [38] against ANN, SVM, least absolute shrinkage and selection operator (LASSO), ridge regression (RR), and traditional GMDH networks. For South Korea's hourly load data, Yu et al suggested a forecasting methodology based on SVR, which implements GMDH networks and bootstrap methods for the input selection procedure in comparison with different variations of linear correlation (LC) and mutual information (MI) based filter methods [39]. Izzatillaev and Yusupov analyzed hourly electrical energy consumption forecasting in a grid-connected microgrid within a commercial bank by employing GMDH networks and ANN [40].…”

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