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Short-Term Electricity Load Forecasting with Machine Learning
| Content Provider | MDPI |
|---|---|
| Author | Antonio, Nuno |
| Copyright Year | 2021 |
| Description | 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. |
| Starting Page | 50 |
| e-ISSN | 20782489 |
| DOI | 10.3390/info12020050 |
| Journal | Information |
| Issue Number | 2 |
| Volume Number | 12 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-01-22 |
| Access Restriction | Open |
| Subject Keyword | Information Industrial Engineering Short-term Load Forecasting Machine Learning Weekly Forecast Electricity Electricity Market |
| Content Type | Text |
| Resource Type | Article |
