Apparatus and method for solar energy resource micro-forecasts for solar generation sources and utilities

1. A method of forecasting solar energy irradiance potential and subsequent photovoltaic output in a region, the method comprising:

collecting meteorological data for a given region via a camera;

estimating irradiance levels using parameters collected from the meteorological data via a neural network coupled to the camera; and

forecasting solar energy irradiance potential and subsequent photovoltaic output in the given region using the collected meteorological data, estimated irradiance levels, and physical characteristics of a solar generating system in the given region at a predetermined time in the future via the neural network.

2. The method of claim 1 , wherein the camera is a shadowband-less fixed camera using a Si-based sensor or other lower energy photon sensors.

3. The method of claim 1 , further comprising analyzing surrounding and occluding clouds as seen from one or more pictures from the camera.

4. The method of claim 1 , wherein the camera takes a picture with lower energy photon wavelengths and is less sensitive to higher energy photon wavelengths.

5. The method of claim 1 , further comprising simulating future solar energy production of a utility system based on the forecast of solar energy irradiance potential and subsequent photovoltaic output.

6. The method of claim 1 wherein the neural network comprises a neural network architecture.

7. The method of claim 1 , wherein the neural network comprises a Lateral Adaptive Priming Adaptive Resonance Theory (LAPART) neural network.

8. The method of claim 6 , wherein the neural network architecture comprises weighted connections associated with each category neuron in a layer that adapts during learning.

9. The method of claim 1 wherein the neural network comprises a sub-network and wherein cloud images are processed and presented to the sub-network.

10. The method of claim 9 wherein the neural network comprises a second sub-network comprising a solar irradiance signal at a future time.

11. The method of claim 5 further comprising smoothing photovoltaic output using a battery system based on the simulated future solar energy production.

12. The method of claim 5 further comprising adjusting the energy requirements produced by the utility system based on the simulated future solar energy production.

13. An apparatus for forecasting solar energy irradiance potential and subsequent photovoltaic output in a predetermined region, the apparatus comprising:

a camera for collecting meteorological data for a predetermined region;

a neural network coupled to the camera for estimating irradiance levels using parameters collected from the meteorological data, wherein the neural network forecasts solar energy irradiance potential and subsequent photovoltaic output in the predetermined region using the collected meteorological data, estimated irradiance levels, and physical characteristics of a solar generating system in the predetermined region at a predetermined time in the future;

wherein the neural network simulates future solar energy production; and

a utility system coupled to the neural network, wherein energy requirements of the utility system are adjusted based on the simulated future solar energy production determined by the neural network.

14. The apparatus of claim 13 wherein the camera is a shadowband-less fixed camera using a Si-based sensor or other lower energy photon sensors.

15. The apparatus of claim 13 wherein the camera comprises infrared imaging.

16. The apparatus of claim 13 wherein the neural network comprises a neural architecture.

17. The apparatus of claim 13 wherein the utility system includes a photovoltaic system.

18. The apparatus of claim 17 wherein the utility system includes one or more batteries coupled to the photovoltaic system.

19. The apparatus of claim 13 wherein the neural network comprises a sub-network where cloud images are processed and presented to the sub-network.

20. The apparatus of claim 19 wherein the neural network comprises a second subnetwork comprising a solar irradiance signal at a future time.

21. The apparatus of claim 13 wherein the neural network comprises a self-organizing neural network.

22. The method of claim 1 wherein the neural network comprises a self-organizing neural network.