Energy Consumption Prediction Using Deep Learning Technique

Maha Alanbar, Amal Alfarraj, Manal Alghieth


In the present era, due to technological advances, the problem of energy consumption has become one of the most important problems for its environmental and economic impact. Educational buildings are one of the highest energy consuming institutions. Therefore, one has to direct the individual and society to reach the ideal usage of energy. One of the possible methods to do that is to prediction energy consumption. This study proposes an energy consumption prediction model using deep learning algorithm. To evaluate its performance, College of Computer (CoC) at Qassim University was selected to analyze the elements in the college that affect high energy consumption and data were collected from the Saudi Electricity Company of daily for 13 years. This research applied Long short term memory (LSTM) technique for medium-term prediction of energy consumption. The performance of the proposed model has been measured by evaluation metrics and achieved low Root mean square error (RMSE) which means higher accuracy of the model compared to relative studies. Consequently, this research provides a recommendation for educational organizations to reach optimal energy consumption.


Energy consumption; Educational building; Deep Learning; LSTM; Prediction.

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International Journal of Interactive Mobile Technologies (iJIM) – eISSN: 1865-7923
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