Probabilistic Solar Power Forecasting: Long Short-Term Memory Network vs Simpler Approaches
Abstract
The high penetration of volatile renewable energy sources such as solar make methods for coping with the uncertainty associated with them of paramount importance. Probabilistic forecasts are an example of these methods, as they assist energy planners in their decision-making process by providing them with information about the uncertainty of future power generation. Currently, there is a trend towards the use of deep learning probabilistic forecasting methods. However, the point at which the more complex deep learning methods should be preferred over more simple approaches is not yet clear. Therefore, the current article presents a simple comparison between a long short-term memory neural network and other more simple approaches. The comparison consists of training and comparing models able to provide one-day-ahead probabilistic forecasts for a solar power system. Moreover, the current paper makes use of an open-source dataset provided during the Global Energy Forecasting Competition of 2014 (GEFCom14).
Cite
@article{arxiv.2101.08236,
title = {Probabilistic Solar Power Forecasting: Long Short-Term Memory Network vs Simpler Approaches},
author = {Vinayak Sharma and Jorge Angel Gonzalez Ordiano and Ralf Mikut and Umit Cali},
journal= {arXiv preprint arXiv:2101.08236},
year = {2021}
}
Comments
Submitted to the International Symposium of forecasting 2020