Abstract
The evolution towards Smart Grids (SGs) represents an important opportunity for the energy industry. It is characterized by the integration of renewable and alternative energy resources into the existing power grids while ensuring a fine-grained control for the different measuring points. Therefore, this evolution requires the ability to send a maximum of data over the network in real time while controlling the grid. A Wireless Sensor Network (WSN) deployed across the grid is a potent solution to achieve this task. However, sensor nodes have limited energy and computation resources especially the battery powered ones. For that, reducing transmission is an essential priority in order to increase the lifetime of the network. Data prediction is a widely used, yet effective, solution in literature to accomplish this task. In this paper, we propose a Quality of Service (QoS) aware algorithm based on time series prediction and linear regression for data prediction in WSN. We test our approach in a SG context on real data traces of photo-voltaic cells. Our algorithm takes into consideration the diversity of applications of SGs with different requirements while being energy efficient. Our results show that our proposal provides satisfactory results compared to literature solutions in terms of data reduction percentage, Root Mean Square Error (RMSE) and energy consumption.
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Notes
- 1.
Global horizontal irradiance represents the total solar radiation incident on a horizontal surface.
- 2.
Current black photon represents the current generated by photo-voltaic cells.
- 3.
It indicates the number of packets to be transmitted to the destination node, when the model is in the training phase or outdated.
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Acknowledgments
This work was partially funded by a grant from the SoMel SoConnected project. This project involves the MEL (Métropole Européenne de Lille), Enedis, EDF, Dalkia, Intent, the Lille Economie-Management Laboratory, HEI - Yncréa HdF and the Faculties of the Catholic University of Lille.
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Chreim, B., Nassar, J., Habib, C. (2021). RADAR - Regression Based Energy-Aware DAta Reduction in WSN: Application to Smart Grids. In: Barolli, L., Woungang, I., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2021. Lecture Notes in Networks and Systems, vol 226. Springer, Cham. https://doi.org/10.1007/978-3-030-75075-6_1
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