Analysis and Comparison of Modeling Methods for Energy Consumption Forecasting Based on Big Data

Syafa’atul Uzma, Syaharuddin Syaharuddin, Abdillah Abdillah, Vera Mandailina

Abstract


This study presents a systematic literature review aimed at analyzing and comparing modeling methods used in forecasting energy consumption based on big data. Literature sources were selected from Google Scholar, DOAJ, and SCOPUS, spanning the years 2014-2024. The research findings indicate that the use of machine learning models, such as SVM, ANN, RF, and classical statistical models, has demonstrated superiority in capturing complex energy consumption patterns. However, the importance of selecting exogenous data and time lags in the complexity and accuracy of machine learning model predictions is also highlighted. The involvement of diverse prediction methods allows researchers to accommodate variations in data characteristics and environmental conditions. Additionally, a strong theoretical foundation and exploration of advanced data analysis methods are crucial in maximizing the potential of big data in predicting energy consumption. These findings affirm that there is no single model suitable for all situations, and careful evaluation of contextual factors and data characteristics is essential in selecting the most appropriate forecasting method. Lastly, the importance of human factors and work culture in modeling performance is also emphasized, underscoring the integration of human factors in the development and implementation of predictive models. Thus, this research provides valuable insights for the development of effective modeling methods in forecasting energy consumption based on big data.

Keywords


Energy Consumption Forecasting, Big Data, Modeling.

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References


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