Our Services

Solar Radiation Data
We have the best quality solar radiation data available for the continental United States.  To our knowledge, all of our competitors use either satellite or modeled data which has approximately double the error of our ground measurements.  Our data page has more information.

Are you looking for coverage maps, sample data, pricing, or statistics comparing our data with others?  Please visit our data page for detailed information about our datasets.  Let us know if we can help with your next project.

Solar Forecasting
California has a goal of generating 33% of its power from renewable energy sources by 2020. The vagaries of solar radiation will require more accurate forecasting technologies if such a utility grid is to be successfully built and operated.

Some attempts to forecast solar power have relied on super computers running NWP (numerical weather prediction) models. However current NWP models are unable to forecast cloud density, formation and movement accurately. Other attempts have focused on predicting solar radiation based on satellite images of cloud movements, but these models do not forecast the dissipation or formation of clouds, nor the cloud’s opacity to solar radiation.

Past modeling attempts have lacked an accurate, hourly dataset of solar radiation that extends back several years. The Solar Data Warehouse now provides ground-based measurements of solar radiation and other weather parameters in near-real time for 5000 US locations. It has been demonstrated that this dataset is more accurate than satellite-based observations and the National Solar Radiation Database.

There is an old adage: if you want to forecast the weather, predict no rain for the next 24 hours and you will be right most of the time. In a similar way, many solar models are based on minimizing average error, but this may not be the best measure of a solar forecasting system for balancing the electrical grid. To illustrate: We predict the solar radiation in Fontana, CA during May will be 90% of the clear sky maximum. The graph above (click for larger version) shows the forecasts from this simple model overlaid onto the actual observations. For three weeks during May 2009, we see the average error is only 50 W/m2. These would be great results except that most of the period was cloud-free. Clearly, there is more to a good model than simply low average errors. We feel it is important to judge the success of a model on how well it is able to forecast solar radiation on difficult days rather than the average error across all days.  We believe we have modeled these difficult days successfully for the L.A. basin in our Phase I project.  Please look through our results and references

Out-of-sample testing means applying the model to a significant amount of data it hasn't seen before (e.g. live forecasting).  Without out-of-sample tests, you are simply judging a model on how well it can do curve fitting.  The Phase I accuracy was better than any published out-of-sample forecasting results, but clearly some improvements could be made.

For our Phase II project, we created forecasts over a 50 x 50 km grid for the Greater L.A. region with improved dampening in the model and some new proprietary techniques.  This study was presented at the 2011 ASES conference in Raleigh.  Currently we believe we have the most accurate solar forecasting system demonstrated by true out-of-sample testing.  The next natural step will be to develop a utility-grade solar forecasting system using live data as well as cost and risk control measures.

ASES 2011 Links:
Phase II Paper
PowerPoint Presentation

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