Short-term Nodal Load forecasting for SCUC Based on Data Mining methodology
| dc.contributor.author | Lu, Dan | |
| dc.contributor.author | Bao, Zhen | |
| dc.contributor.author | Li, Zuyi | |
| dc.contributor.author | Zhao, Dongbo | |
| dc.date.accessioned | 2019-05-07T14:16:45Z | |
| dc.date.available | 2019-05-07T14:16:45Z | |
| dc.date.issued | 2018-10-12 | |
| dc.description.abstract | Our poster introduces an advanced method to predict Short-term Nodal Load data in power system. This method forecasts a set of load profiles for the next day which can cover minor changes. Data Mining (DM) techniques are heavily used to deal with the existing historical data. Least absolute shrinkage and selection operator (LASSO) is employed to reduce the number of features for a single nodal load forecasting. Principal component analysis (PCA) is used to capture the features of the historical load in low dimensional space compared to the original high-dimensional load space, whose feature is hard to describe. Bayesian Ridge Regression (BRR) is employed to form the prediction model which is sophisticated method to decide the parameters in the model from statistical point of view. | en_US |
| dc.identifier.uri | http://hdl.handle.net/10829/23387 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Alfred University | en_US |
| dc.relation.ispartof | Scholes Library | en_US |
| dc.rights | http://libguides.alfred.edu/AURA/termsofuse | en_US |
| dc.subject | AU Energy Symposium | en_US |
| dc.subject | Energy | en_US |
| dc.subject | Renewable energy | en_US |
| dc.subject | Nodal load forecasting | en_US |
| dc.subject | Data mining | en_US |
| dc.title | Short-term Nodal Load forecasting for SCUC Based on Data Mining methodology | en_US |
| dc.type | Presentation or Speech | en_US |
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