Abstract
Recently, the smart grid has been attracting much attention as a viable solution to the power shortage problem. One of critical issues for improving its operational efficiency is to predict the short-term electric load accurately. So far, many works have been done to construct STLF (Short-Term Load Forecasting) models using a variety of machine learning algorithms. By taking many influential variables into account, they gave satisfactory results in predicting overall electric load pattern. But, they are still lacking in predicting minute electric load patterns. To overcome this problem, in this paper, we propose a new STLF model that combines Auto-Encoder (AE) based feature extraction and Random Forest (RF) and show its performance by carrying out several experiments for the actual power consumption data collected from diverse types of building clusters.
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Acknowledgements
This research was supported by Korea Electric Power Corporation (Grant number: R18XA05).
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Son, M., Moon, J., Jung, S., Hwang, E. (2019). A Short-Term Load Forecasting Scheme Based on Auto-Encoder and Random Forest. In: Ntalianis, K., Vachtsevanos, G., Borne, P., Croitoru, A. (eds) Applied Physics, System Science and Computers III. APSAC 2018. Lecture Notes in Electrical Engineering, vol 574 . Springer, Cham. https://doi.org/10.1007/978-3-030-21507-1_21
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DOI: https://doi.org/10.1007/978-3-030-21507-1_21
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