@Article{cmc.2021.018523,
AUTHOR = {Prince Waqas Khan, Yung-Cheol Byun},
TITLE = {Adaptive Error Curve Learning Ensemble Model for Improving Energy Consumption Forecasting},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {69},
YEAR = {2021},
NUMBER = {2},
PAGES = {1893--1913},
URL = {http://www.techscience.com/cmc/v69n2/43893},
ISSN = {1546-2226},
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 a meta-data from Gradient Boosting and CatBoost models to compute the final predictions using the Multilayer Perceptron network. A genetic algorithm is used to obtain the optimal features to be used for the model. To prove the proposed model’s effectiveness, we have used a four-phase technique using Jeju island’s real energy consumption data. In the first phase, we have obtained the results by applying the CB-GB-MLP model. In the second phase, we have utilized a GA-ensembled model with optimal features. The third phase is for the comparison of the energy forecasting result with the proposed ECL-based model. The fourth stage is the final stage, where we have applied the GA-ECLE model. We obtained a mean absolute error of 3.05, and a root mean square error of 5.05. Extensive experimental results are provided, demonstrating the superiority of the proposed GA-ECLE model over traditional ensemble models.},
DOI = {10.32604/cmc.2021.018523}
}