Sensor network based solar forecasting using a local vector autoregressive ridge framework
| Autor: | Jin Xu, Paul Kalb, Shinjae Yoo, John Heiser |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
business.industry
Computer science 020209 energy Real-time computing Photovoltaic system Irradiance Cloud computing 02 engineering and technology Solar energy Solar irradiance Autoregressive model Auxiliary power unit 0202 electrical engineering electronic engineering information engineering business Wireless sensor network Simulation |
| Zdroj: | SAC |
| Popis: | The significant improvements and falling costs of photovoltaic (PV) technology make solar energy a promising resource, yet the cloud induced variability of surface solar irradiance inhibits its effective use in grid-tied PV generation. Short-term irradiance forecasting, especially on the minute scale, is critically important for grid system stability and auxiliary power source management. Compared to the trending sky imaging devices, irradiance sensors are inexpensive and easy to deploy but related forecasting methods have not been well researched. The prominent challenge of applying classic time series models on a network of irradiance sensors is to address their varying spatio-temporal correlations due to local changes in cloud conditions. We propose a local vector autoregressive framework with ridge regularization to forecast irradiance without explicitly determining the wind field or cloud movement. By using local training data, our learned forecast model is adaptive to local cloud conditions and by using regularization, we overcome the risk of overfitting from the limited training data. Our systematic experimental results showed an average of 19.7% RMSE and 20.2% MAE improvement over the benchmark Persistent Model for 1-5 minute forecasts on a comprehensive 25-day dataset. |
| Databáze: | OpenAIRE |
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