Big-Data Architecture for Electrical Consumption Forecasting in Educational Institutions Buildings-Reference-Cited by-同舟云学术

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

Published:2019 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2nd International Conference on Networking, Information Systems & Security - NISS19
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Author:

Daki Houda¹,El Hannani Asmaa¹,Ouahmane Hassan¹

Affiliation:

1. Laboratory of Information Technologies, National School of Applied Sciences, University of Chouaib Doukkali, El Jadida, Morocco

Publisher

ACM Press

Reference15 articles.

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2. Bhojwani, N. and Shah, A.P.V., 2016. A survey on hadoop hbase system. Development, 3(1).

3. 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.

4. Munshi, A.A. and Mohamed, Y.A.R.I., 2018. Data lake lambda architecture for smart grids big data analytics. IEEE Access, 6, pp. 40463--40471.

5. Liu, X., Iftikhar, N., Nielsen, P.S. and Heller, A., 2016, September. Online anomaly energy consumption detection using lambda architecture. In International Conference on Big Data Analytics and Knowledge Discovery (pp. 193--209). Springer, Cham.

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2. Evaluation of Selected Machine Learning Models and Features for Electrical Consumption Prediction in Educational Institutions;Proceedings of the 6th International Conference on Big Data and Internet of Things;2023