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Article

Solving the Cold-Start Problem in Short-Term Load Forecasting Using Tree-Based Methods

1
School of Electrical Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Korea
2
School of Industrial Management Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Korea
*
Author to whom correspondence should be addressed.
Energies 2020, 13(4), 886; https://doi.org/10.3390/en13040886
Received: 20 January 2020 / Revised: 8 February 2020 / Accepted: 12 February 2020 / Published: 17 February 2020
(This article belongs to the Special Issue Short-Term Load Forecasting 2019)
An energy-management system requires accurate prediction of the electric load for optimal energy management. However, if the amount of electric load data is insufficient, it is challenging to perform an accurate prediction. To address this issue, we propose a novel electric load forecasting scheme using the electric load data of diverse buildings. We first divide the electric energy consumption data into training and test sets. Then, we construct multivariate random forest (MRF)-based forecasting models according to each building except the target building in the training set and a random forest (RF)-based forecasting model using the limited electric load data of the target building in the test set. In the test set, we compare the electric load of the target building with that of other buildings to select the MRF model that is the most similar to the target building. Then, we predict the electric load of the target building using its input variables via the selected MRF model. We combine the MRF and RF models by considering the different electric load patterns on weekdays and holidays. Experimental results demonstrate that combining the two models can achieve satisfactory prediction performance even if the electric data of only one day are available for the target building. View Full-Text
Keywords: short-term load forecasting; building electric energy consumption forecasting; cold-start problem; transfer learning; multivariate random forests; random forest short-term load forecasting; building electric energy consumption forecasting; cold-start problem; transfer learning; multivariate random forests; random forest
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MDPI and ACS Style

Moon, J.; Kim, J.; Kang, P.; Hwang, E. Solving the Cold-Start Problem in Short-Term Load Forecasting Using Tree-Based Methods. Energies 2020, 13, 886. https://doi.org/10.3390/en13040886

AMA Style

Moon J, Kim J, Kang P, Hwang E. Solving the Cold-Start Problem in Short-Term Load Forecasting Using Tree-Based Methods. Energies. 2020; 13(4):886. https://doi.org/10.3390/en13040886

Chicago/Turabian Style

Moon, Jihoon, Junhong Kim, Pilsung Kang, and Eenjun Hwang. 2020. "Solving the Cold-Start Problem in Short-Term Load Forecasting Using Tree-Based Methods" Energies 13, no. 4: 886. https://doi.org/10.3390/en13040886

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