Name
Abstract | ||
|---|---|---|
Solar power prediction remains an important challenge for renewable energy integration primarily due to its inherent variability and intermittency. In this work, a neural network based solar power forecasting framework is developed for the NASA Ames Sustainability Base (SB) solar array using the publicly available National Oceanic and Atmospheric Administration (NOAA) weather data forecasts. The prediction inputs include temperature, irradiance and wind speed obtained through the NOAA NOMADS server in real-time. The neural network (ANN) is trained and tested on input-output data from on-site sensors. The NOAA archived forecast data is then input to the trained ANN model to predict power output spanning over nine months (June 2013-March 2014). The efficacy of the model is determined by comparing predicted power output against on-site sensor data. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/CIASG.2014.7011545 | Computational Intelligence Applications in Smart Grid |
Keywords | Field | DocType |
electric sensing devices,neural nets,power engineering computing,solar cell arrays,solar power,weather forecasting,NASA ames sustainability base,NOAA NOMADS server,NOAA model archive and distribution system server,SB solar array,input-output data test,irradiance inputs,national oceanic and atmospheric administration,neural network forecasting framework,on-site sensor data,power output spanning predict,renewable energy integration,solar power prediction,temperature inputs,trained ANN model,weather data forecasts,wind speed inputs | Meteorology,Wind speed,Renewable energy,Solar power forecasting,Remote sensing,Intermittency,Solar power,Irradiance,Artificial neural network,Geography,Photovoltaic system | Conference |
Citations | PageRank | References |
1 | 0.43 | 5 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Poolla, C. | 1 | 1 | 0.43 |
| Ishihara, A. | 2 | 1 | 0.43 |
| Rosenberg, S. | 3 | 1 | 0.77 |
| Martin, R. | 4 | 63 | 43.99 |