For efficient and effective energy management, accurate energy consumption forecasting is required in energy management systems (EMSs). Recently, several artificial intelligence-based techniques have been proposed for accurate electric load forecasting; moreover, perfect energy consumption data are critical for the prediction. However, owing to diverse reasons, such as device malfunctions and signal transmission errors, missing data are frequently observed in the actual data. Previously, many imputation methods have been proposed to compensate for missing values; however, these methods have achieved limited success in imputing electric energy consumption data because the period of data missing is long and the dependency on historical data is high. In this study, we propose a novel missing-value imputation scheme for electricity consumption data. The proposed scheme uses a bagging ensemble of multilayer perceptrons (MLPs), called softmax ensemble network, wherein the ensemble weight of each MLP is determined by a softmax function. This ensemble network learns electric energy consumption data with explanatory variables and imputes missing values in this data. To evaluate the performance of our scheme, we performed diverse experiments on real electric energy consumption data and confirmed that the proposed scheme can deliver superior performance compared to other imputation methods.
Bibliographical noteFunding Information:
Funding: This research was supported in part by the Korea Electric Power Corporation (grant number: R18XA05) and in part by Energy Cloud R&D Program (grant number: 2019M3F2A1073179) through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT.
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.
- Deep learning
- Electric energy consumption data
- Ensemble learning
- Missing-value imputation
- Multilayer perceptron
- Smart meter
ASJC Scopus subject areas
- Analytical Chemistry
- Information Systems
- Atomic and Molecular Physics, and Optics
- Electrical and Electronic Engineering