Prince Waqas Khan, Yung-Cheol Byun*
CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1893-1913, 2021, DOI:10.32604/cmc.2021.018523
Abstract Despite the advancement within the last decades in the field of smart grids, energy consumption forecasting utilizing the metrological features is still challenging. This paper proposes a genetic algorithm-based adaptive error curve learning ensemble (GA-ECLE) model. The proposed technique copes with the stochastic variations of improving energy consumption forecasting using a machine learning-based ensembled approach. A modified ensemble model based on a utilizing error of model as a feature is used to improve the forecast accuracy. This approach combines three models, namely CatBoost (CB), Gradient Boost (GB), and Multilayer Perceptron (MLP). The ensembled CB-GB-MLP model’s inner mechanism consists of generating… More >
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