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.
Highlights
Electric load forecasting (ELF) is essential for the effective and stable planning and operation of power systems [1]
The aim of this paper is to examine the performance of the proposed input selection methods, so the procedures for selecting the design parameters will not be described in detail
We present the results of applying the input selection methods to the prepared learning dataset
Summary
Electric load forecasting (ELF) is essential for the effective and stable planning and operation of power systems [1]. Forecasting models are constructed based on historical load series and exogenous variables (e.g., weather, economic, and social factors), and the models are used to predict future loads for a specified period of time ahead. Various decisions related to power systems depend on the future behavior of loads, such as unit commitment, spinning reserve reduction, economic dispatch, automatic generation control, reliability analysis, maintenance scheduling, and energy commercialization [2,3]. ELF, especially, has major effects on deregulated electricity markets (e.g., demand response management [4,5,6]) and their participants because the prices on the markets are determined by the predicted future loads. It is difficult to precisely forecast time-series loads because they exhibit a high degree of seasonality and nonlinear characteristics
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