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http://dx.doi.org/10.3745/KTSDE.2017.6.11.527

An Electric Load Forecasting Scheme with High Time Resolution Based on Artificial Neural Network  

Park, Jinwoong (고려대학교 전기전자공학과)
Moon, Jihoon (고려대학교 전기전자공학과)
Hwang, Eenjun (고려대학교 전기전자공학과)
Publication Information
KIPS Transactions on Software and Data Engineering / v.6, no.11, 2017 , pp. 527-536 More about this Journal
Abstract
With the recent development of smart grid industry, the necessity for efficient EMS(Energy Management System) has been increased. In particular, in order to reduce electric load and energy cost, sophisticated electric load forecasting and efficient smart grid operation strategy are required. In this paper, for more accurate electric load forecasting, we extend the data collected at demand time into high time resolution and construct an artificial neural network-based forecasting model appropriate for the high time resolution data. Furthermore, to improve the accuracy of electric load forecasting, time series data of sequence form are transformed into continuous data of two-dimensional space to solve that problem that machine learning methods cannot reflect the periodicity of time series data. In addition, to consider external factors such as temperature and humidity in accordance with the time resolution, we estimate their value at the time resolution using linear interpolation method. Finally, we apply the PCA(Principal Component Analysis) algorithm to the feature vector composed of external factors to remove data which have little correlation with the power data. Finally, we perform the evaluation of our model through 5-fold cross-validation. The results show that forecasting based on higher time resolution improve the accuracy and the best error rate of 3.71% was achieved at the 3-min resolution.
Keywords
Energy Management System; Smart Grid; Electric Load Forecasting; Artificial Neural Network;
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Times Cited By KSCI : 1  (Citation Analysis)
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