Next Article in Journal
Fusion of Improved Sparrow Search Algorithm and Long Short-Term Memory Neural Network Application in Load Forecasting
Previous Article in Journal
Dynamic Modelling and Advanced Process Control of Power Block for a Parabolic Trough Solar Power Plant
Previous Article in Special Issue
Performance Evaluation of Forecasting Strategies for Electricity Consumption in Buildings
Article

XGBoost-Based Day-Ahead Load Forecasting Algorithm Considering Behind-the-Meter Solar PV Generation

Department of Electrical Engineering, Soongsil University, Seoul 06978, Korea
*
Author to whom correspondence should be addressed.
Academic Editors: Antonio Gabaldón, María Carmen Ruiz-Abellón and Luis Alfredo Fernández-Jiménez
Energies 2022, 15(1), 128; https://doi.org/10.3390/en15010128
Received: 24 November 2021 / Revised: 13 December 2021 / Accepted: 21 December 2021 / Published: 24 December 2021
(This article belongs to the Special Issue Short-Term Load Forecasting 2021)
With the rapid expansion of renewable energy, the penetration rate of behind-the-meter (BTM) solar photovoltaic (PV) generators is increasing in South Korea. The BTM solar PV generation is not metered in real-time, distorts the electric load and increases the errors of load forecasting. In order to overcome the problems caused by the impact of BTM solar PV generation, an extreme gradient boosting (XGBoost) load forecasting algorithm is proposed. The capacity of the BTM solar PV generators is estimated based on an investigation of the deviation of load using a grid search. The influence of external factors was considered by using the fluctuation of the load used by lighting appliances and data filtering based on base temperature, as a result, the capacity of the BTM solar PV generators is accurately estimated. The distortion of electric load is eliminated by the reconstituted load method that adds the estimated BTM solar PV generation to the electric load, and the load forecasting is conducted using the XGBoost model. Case studies are performed to demonstrate the accuracy of prediction for the proposed method. The accuracy of the proposed algorithm was improved by 21% and 29% in 2019 and 2020, respectively, compared with the MAPE of the LSTM model that does not reflect the impact of BTM solar PV. View Full-Text
Keywords: day-ahead load forecasting; behind-the-meter solar PV generator; reconstituted load method; extreme gradient boosting day-ahead load forecasting; behind-the-meter solar PV generator; reconstituted load method; extreme gradient boosting
Show Figures

Figure 1

MDPI and ACS Style

Bae, D.-J.; Kwon, B.-S.; Song, K.-B. XGBoost-Based Day-Ahead Load Forecasting Algorithm Considering Behind-the-Meter Solar PV Generation. Energies 2022, 15, 128. https://doi.org/10.3390/en15010128

AMA Style

Bae D-J, Kwon B-S, Song K-B. XGBoost-Based Day-Ahead Load Forecasting Algorithm Considering Behind-the-Meter Solar PV Generation. Energies. 2022; 15(1):128. https://doi.org/10.3390/en15010128

Chicago/Turabian Style

Bae, Dong-Jin, Bo-Sung Kwon, and Kyung-Bin Song. 2022. "XGBoost-Based Day-Ahead Load Forecasting Algorithm Considering Behind-the-Meter Solar PV Generation" Energies 15, no. 1: 128. https://doi.org/10.3390/en15010128

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop