Shortterm Load Forecasting Based on Hybrid Strategy Using Warmstart Gradient Tree Boosting
Abstract
A deeplearning based hybrid strategy for shortterm load forecasting is presented. The strategy proposes a novel treebased ensemble method Warmstart Gradient Tree Boosting (WGTB). Current strategies either ensemble submodels of a single type, which fail to take advantage of statistical strengths of different inference models. Or they simply sum the outputs from completely different inference models, which doesn't maximize the potential of ensemble. WGTB is thus proposed and tailored to the great disparity among different inference models in accuracy, volatility and linearity. The complete strategy integrates four different inference models (i.e., autoregressive integrated moving average, nu support vector regression, extreme learning machine and long shortterm memory neural network), both linear and nonlinear models. WGTB then ensembles their outputs by hybridizing linear estimator ElasticNet and nonlinear estimator ExtraTree via boosting algorithm. It is validated on the real historical data of a grid from State Grid Corporation of China of hourly resolution. The result demonstrates the effectiveness of the proposed strategy that hybridizes statistical strengths of both linear and nonlinear inference models.
 Publication:

arXiv eprints
 Pub Date:
 May 2020
 arXiv:
 arXiv:2005.11478
 Bibcode:
 2020arXiv200511478Z
 Keywords:

 Computer Science  Machine Learning;
 Electrical Engineering and Systems Science  Signal Processing;
 Statistics  Machine Learning
 EPrint:
 14 pages, 7 figures