Joint bagged-boosted artificial neural networks: Using ensemble machine learning to improve short-term electricity load forecasting

•Load forecasting using bagged-boosted artificial neural networks is presented.•The presented technique improves forecasting accuracy and consistency.•New England Pool region data are used for performance validation.•Experiments are carried out with varying number of bagged and boosted models.•The technique can be extended to multiple model strained on different months. This paper uses artificial neural networks (ANNs) based ensemble machine learning for improving short-term electricity load forecasting. Unlike existing methods, the proposed technique combines both bagging and boosting to train bagged-boosted ANNs. The combination consists of training an ensemble of multiple ANN models in parallel and averaging the forecasted loads from these models to get the final forecasted load. The models are trained on data obtained by bootstrap sampling the original training data. Each of these models further consists of an ensemble of ANNs that are trained sequentially, such that the prediction error between the training output and the forecasted output from the preceding ANN in the sequence is minimized. We use real data to demonstrate that the proposed technique enables reduction of both bias and variance compared to a single ANN, bagged ANN and boosted ANN. We further show that it reduces the forecasting error compared to existing load forecasting methods.