A Multilayer Perceptron-Based Electric Load Forecasting Scheme via Effective Recovering Missing Data


KIPS Transactions on Software and Data Engineering, Vol. 8, No. 2, pp. 67-78, Feb. 2019
https://doi.org/10.3745/KTSDE.2019.8.2.67,   PDF Download:
Keywords: Smart Grid, Electric Load Forecasting, Missing Data Handling, Deep Learning
Abstract

Accurate electric load forecasting is very important in the efficient operation of the smart grid. Recently, due to the development of IT technology, many works for constructing accurate forecasting models have been developed based on big data processing using artificial intelligence techniques. These forecasting models usually utilize external factors such as temperature, humidity and historical electric load as independent variables. However, due to diverse internal and external factors, historical electrical load contains many missing data, which makes it very difficult to construct an accurate forecasting model. To solve this problem, in this paper, we propose a random forest-based missing data recovery scheme and construct an electric load forecasting model based on multilayer perceptron using the estimated values of missing data and external factors. We demonstrate the performance of our proposed scheme via various experiments.


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Cite this article
[IEEE Style]
J. Moon, S. Park, E. Hwang, "A Multilayer Perceptron-Based Electric Load Forecasting Scheme via Effective Recovering Missing Data," KIPS Transactions on Software and Data Engineering, vol. 8, no. 2, pp. 67-78, 2019. DOI: https://doi.org/10.3745/KTSDE.2019.8.2.67.

[ACM Style]
Jihoon Moon, Sungwoo Park, and Eenjun Hwang. 2019. A Multilayer Perceptron-Based Electric Load Forecasting Scheme via Effective Recovering Missing Data. KIPS Transactions on Software and Data Engineering, 8, 2, (2019), 67-78. DOI: https://doi.org/10.3745/KTSDE.2019.8.2.67.