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Adaptive Error Curve Learning Ensemble Model for Improving Energy Consumption Forecasting

Prince Waqas Khan, Yung-Cheol Byun*

Department of Computer Engineering, Jeju National University, Jeju-si, Korea

* Corresponding Author: Yung-Cheol Byun. Email: email

(This article belongs to this Special Issue: Emerging Trends in Artificial Intelligence and Machine Learning)

Computers, Materials & Continua 2021, 69(2), 1893-1913. https://doi.org/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 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.

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Cite This Article

P. Waqas Khan and Y. Byun, "Adaptive error curve learning ensemble model for improving energy consumption forecasting," Computers, Materials & Continua, vol. 69, no.2, pp. 1893–1913, 2021.

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cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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