Name
Title | ||
|---|---|---|
Comparison of echo state network and extreme learning machine for PV power prediction |
Abstract | ||
|---|---|---|
The increasing use of solar power as a source of electricity has introduced various challenges to the grid operator due to the high PV power variability. The energy management systems in electric utility control centers make several decisions at different time scales. In this paper, power output predictions of a large photovoltaic (PV) plant at eight different time instances, ranging from few seconds to a minute plus, is presented. The predictions are provided by two learning networks: an echo state network (ESN) and an extreme learning machine (ELM). The predictions are based on current solar irradiance, temperature and PV plant power output. A real-time study is performed using a real-time and actual weather profiles and a real-time simulation of a large PV plant. Typical ESN and ELM prediction results are compared under varying weather conditions. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/CIASG.2014.7011546 | Computational Intelligence Applications in Smart Grid |
Keywords | DocType | Citations |
learning (artificial intelligence),load forecasting,photovoltaic power systems,power system simulation,sunlight,ELM,ESN,PV power prediction variability,echo state network,electric utility control center,energy management system,extreme learning machine,photovoltaic plant,power grid,solar irradiance,solar power source,Echo state network,PV,extreme learning machine,power prediction,real-time weather | Conference | 1 |
PageRank | References | Authors |
0.40 | 2 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Iroshani Jayawardene | 1 | 4 | 0.76 |
| Ganesh K. Venayagamoorthy | 2 | 2297 | 200.90 |