Name
Title | ||
|---|---|---|
Big-Data Architecture for Electrical Consumption Forecasting in Educational Institutions Buildings |
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.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3320326.3320356 | Proceedings of the 2nd International Conference on Networking, Information Systems & Security |
Keywords | DocType | ISBN |
Big Data, Electrical forecasting, HBase storage system, Kafka, Lambda architecture, Machine learning, Smart grid, Spark | Conference | 978-1-4503-6645-8 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Houda Daki | 1 | 5 | 1.18 |
| El Hannani | 2 | 48 | 11.48 |
| Hassan Ouahmane | 3 | 1 | 2.73 |