Forecasting Solar Power Using Long-Short Term Memory and Convolutional Neural Networks

As solar photovoltaic (PV) generation becomes cost-effective, solar power comes into its own as the alternative energy with the potential to make up a larger share of growing energy needs. Consequently, operations and maintenance cost now have a large impact on the profit of managing power modules, and the energy market participants need to estimate the solar power in short or long terms of future. In this paper, we propose a solar power forecasting technique by utilizing convolutional neural networks and long-shortterm memory networks recently developed for analyzing time series data in the deep learning communities. Considering that weather information may not be always available for the location where PV modules are installed and sensors are often damaged, we empirically confirm that the proposed method predicts the solar power well with roughly estimated weather data obtained from national weather centers as well as it works robustly without sophisticatedly preprocessed input to remove outliers.This work was supported in part by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) granted financial resource from the Ministry of Trade, Industry & Energy, South Korea, under Grant 20153010011980, in part by the Next-Generation Information Computing Development Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning under Grant 2017M3C4A7063570, and in part by the research fund of Hanyang University under Grant HY-2014-N