Journal
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
Volume 82, Issue -, Pages 2254-2269Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.rser.2017.08.066
Keywords
Solar irradiance; Energy harvesting; Solar forecasting; Machine learning
Ask authors/readers for more resources
Rechargeable wireless sensor networks mitigate the life span and cost constraints propound in conventional battery operated networks. Reliable knowledge of solar radiation is essential for informed design, deployment planning and optimal management of self-powered nodes. The problem of solar irradiance forecasting can be well addressed by machine learning methodologies over historical data set. In proposed work, forecasts have been done using FoBa, leapForward, spikeslab, Cubist and bagEarthGCV models. To validate the effectiveness of these methodologies, a series of experimental evaluations have been presented in terms of forecast accuracy, correlation coefficient and root mean square error (RMSE). The r interface has been used as simulation platform for these evaluations. The dataset from national renewable energy laboratory (NREL) has been used for experiments. The experimental results exhibits that from few hours to two days ahead solar irradiance prediction is precisely estimated by machine learning based models irrespective of seasonal variation in weather conditions.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available