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The article has been withdrawn at the request of the authors and editor of the journal Recent Advances in Computer Science and Communications. The Bentham Editorial Policy on Article Withdrawal can be found at https://benthamscience.com/editorial-policies-main.php BENTHAM SCIENCE DISCLAIMER: It is a condition of publication that manuscripts submitted to this journal have not been published and will not be simultaneously submitted or published elsewhere. Furthermore, any data, illustration, structure or table that has been published elsewhere must be reported, and copyright permission for reproduction must be obtained. Plagiarism is strictly forbidden, and by submitting the article for publication the authors agree that the publishers have the legal right to take appropriate action against the authors, if plagiarism or fabricated information is discovered. By submitting a manuscript the authors agree that the copyright of their article is transferred to the publishers if and when the article is accepted for publication.

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Recently, predictive analytic contributes very well for reliable electric power supply. It provides advanced techniques to process, interpret and analyze big energy data and make it more valuable. In this paper, we have presented a benchmark of the most used forecasting models in predicting electrical energy consumption for educational institutions. This study is based on a real use case, implemented using Big Data eco-system based on SMACK architecture. The proposed system analyzes six years of data sets that highly impact National School of Applied Sciences of El Jadida-Morocco energy consumption including planning data (courses, activities, holiday etc) and meteorological data (temperature, pressure, humidity etc). The aim of this benchmark is to evaluate the prediction performance of each forecasting model in order to choose the accurate one to predict electricity consumption in educational institutions.

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