Real-time prediction of solar radiation based on online sequential extreme learning machine
Jie Zhang, Yuefan Xu, Jianqiang Xue, Wendong Xiao · 2018
Solar energy, which is one of the greatest potential renewable energies, has widely attracted attention in current years. Accurate prediction of solar radiation is the premise of exploiting and utilization of solar energy. However, most of the research works on solar radiation prediction focus on the offline prediction, which is not practical and suitable in real world applications. In order to tackle this issue, in this paper, we implement a new kind of machine learning algorithm, online sequential extreme learning machine (OS-ELM), to realize the real-time prediction of solar radiation. Comparing with existing batch learning algorithms for solar radiation prediction, OS-ELM can handle sequentially coming data one-by-one or chunk-by-chunk with fixed or varying chunk size. Its online learning capability makes it possible to reflect and adapt to the environmental changes in a timely manner.