Short-term Forecasting of User Power Load in China Based on XGBoost

Cai, Jing; Cai, Hui; Cai, Yangyang; Wu, Lingting; Shen, Yu

doi:10.1109/appeec48164.2020.9220335

Cited by 2 publications

(1 citation statement)

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“…Similarly, the authors of Reference [31] submitted a comparative study between many models to forecast smart buildings' electricity load. ARIMA, Seasonal ARIMA (SARIMA), RF, and extreme gradient boosting (XGB) were on this set of models.…”

Section: Literature Reviewmentioning

confidence: 99%

Short-Term Electricity Load Forecasting with Machine Learning

Madrid

António

2021

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An accurate short-term load forecasting (STLF) is one of the most critical inputs for power plant units’ planning commitment. STLF reduces the overall planning uncertainty added by the intermittent production of renewable sources; thus, it helps to minimize the hydrothermal electricity production costs in a power grid. Although there is some research in the field and even several research applications, there is a continual need to improve forecasts. This research proposes a set of machine learning (ML) models to improve the accuracy of 168 h forecasts. The developed models employ features from multiple sources, such as historical load, weather, and holidays. Of the five ML models developed and tested in various load profile contexts, the Extreme Gradient Boosting Regressor (XGBoost) algorithm showed the best results, surpassing previous historical weekly predictions based on neural networks. Additionally, because XGBoost models are based on an ensemble of decision trees, it facilitated the model’s interpretation, which provided a relevant additional result, the features’ importance in the forecasting.

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Section: Literature Reviewmentioning

confidence: 99%