Solar Energy Forecasting Using Machine Learning

Kumar, Karan; Batra, Nipun

doi:10.1145/3371158.3371212

Cited by 2 publications

(2 citation statements)

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“…Moreover, other work focused on linear regression for data prediction [17][18][19]. In [18], the authors used multiple linear regression (MLR) to predict solar intensity from a set of weather metrics.…”

Section: Related Workmentioning

confidence: 99%

“…In [18], the authors used multiple linear regression (MLR) to predict solar intensity from a set of weather metrics. In [19], the authors determined the most influential features for predicting photo-voltaic production, using linear regression. However, in these work, all input variables need to be available all the time in order to predict a single output.…”

Section: Related Workmentioning

confidence: 99%

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RADAR - Regression Based Energy-Aware DAta Reduction in WSN: Application to Smart Grids

Chreim¹,

Nassar²,

Habib³

2021

Advanced Information Networking and Applications

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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 finegrained 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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