research-article

Big-Data Architecture for Electrical Consumption Forecasting in Educational Institutions Buildings

Authors:

Asmaa El Hannani,

Hassan OuahmaneAuthors Info & Claims

NISS '19: Proceedings of the 2nd International Conference on Networking, Information Systems & Security

Article No.: 24, Pages 1 - 6

https://doi.org/10.1145/3320326.3320356

Published: 27 March 2019 Publication History

Abstract

Recently, educational institutions suffer from high electrical consumption due to their new practices and activities. One of the promising solutions to overcome this challenge is to improve their energy management strategies using smart grids which ensure efficiency, reliability and energy saving. For this same reason, the National School of Applied Sciences of El Jadida -- Morocco has decided to install a private smart grid based on photovoltaic panels that will cover 40% of its electricity needs. But the problem that arises when using this new approach is the high level of complexity in term of data management due to the variety, veracity and the volume of the data. So, to meet these needs the use of Big Data technologies is required. In this paper, we propose a Big Data solution based on Lambda architecture to handle electrical consumption data in the National School of Applied Sciences of El Jadida -- Morocco. This system collects all parameters that might influence electrical consumption with Kafka, then it applies Spark libraries to analyze it. The solution allows also electrical energy forecasting using Spark machine learning library and the data persistence using HBase storage system.

References

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Digital Library

[8]

Gul, M.S. and Patidar, S., 2015. Understanding the energy consumption and occupancy of a multi-purpose academic building. Energy and Buildings, 87, pp. 155--165.

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Daki, H., El Hannani, A., Aqqal, A., Haidine, A., Dahbi, A. and Ouahmane, H., 2016, May. Towards adopting Big Data technologies by mobile networks operators: A Moroccan case study. In Cloud Computing Technologies and Applications (CloudTech), 2016 2nd International Conference on (pp. 154--161). IEEE.

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Cited By

Azzabi SAlfughi ZOuda A(2024)Data Lakes: A Survey of Concepts and ArchitecturesComputers10.3390/computers1307018313:7(183)Online publication date: 22-Jul-2024
https://doi.org/10.3390/computers13070183
Daki HSaad BHannani AOuahmane H(2023)Evaluation of Selected Machine Learning Models and Features for Electrical Consumption Prediction in Educational InstitutionsProceedings of the 6th International Conference on Big Data and Internet of Things10.1007/978-3-031-28387-1_26(303-315)Online publication date: 29-Mar-2023
https://doi.org/10.1007/978-3-031-28387-1_26

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Published In

cover image ACM Other conferences

NISS '19: Proceedings of the 2nd International Conference on Networking, Information Systems & Security

March 2019

512 pages

ISBN:9781450366458

DOI:10.1145/3320326

Editors:
Badr Abouelmajd
FSR, Mohammed V University, Morocco
,
Mohamed Ben Ahmed
FSTT, Abdelmalek Essaâdi University, Morocco
,
Boudhir Anouar Abdelhakim
FSTT, Abdelmalek Essaâdi University, Morocco
,
Hassan El Ghazi
INPT, Morocco

Copyright © 2019 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 March 2019

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NISS19: Networking, Information Systems & Security

March 27 - 29, 2019

Rabat, Morocco

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Cited By

Azzabi SAlfughi ZOuda A(2024)Data Lakes: A Survey of Concepts and ArchitecturesComputers10.3390/computers1307018313:7(183)Online publication date: 22-Jul-2024
https://doi.org/10.3390/computers13070183
Daki HSaad BHannani AOuahmane H(2023)Evaluation of Selected Machine Learning Models and Features for Electrical Consumption Prediction in Educational InstitutionsProceedings of the 6th International Conference on Big Data and Internet of Things10.1007/978-3-031-28387-1_26(303-315)Online publication date: 29-Mar-2023
https://doi.org/10.1007/978-3-031-28387-1_26

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