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Title: DeepSnow: Modeling the Impact of Snow on Solar Generation
The falling cost of solar energy deployments has resulted in ever-increasing growth in solar capacity worldwide. The primary challenge posed by increasing grid-tied solar capacity is handling its variability due to continuously changing conditions. Thus, prior work has developed highly sophisticated models to estimate and forecast solar power output based on many characteristics, including location, elevation, time, weather, shading, module type, wiring, etc. These models are highly accurate for estimating solar power, especially over long periods, for sites at low latitudes, i.e., closer to the equator. However, models for sites at higher latitudes are less accurate due to the effect of snow on solar output, since even a small amount of snow can cover panels and reduce power to zero. Improving the accuracy of these models for annual solar output by even 2--3% is significant, as power translates directly into revenue, which compounds over the system's lifetime. Thus, if a site produces 2--3% less power on average per year due to snow than a model predicts, it can mean the difference between a positive or negative return-on-investment. To address the problem, we develop DeepSnow, a data-driven approach that models the effect of snow on solar power generation. DeepSnow integrates with existing more » solar modeling frameworks, and uses publicly available snow data to learn its effect on solar generation. We leverage deep learning to quantify the effect of different snow variables on solar power using 4 million hourly readings from 40 solar sites. We evaluate our approach on 10 solar sites, and show that it yields a higher accuracy than the current approach for modeling snow effects used by the U.S. Department of Energy's System Advisor Model (SAM), a popular solar modeling framework. « less
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Award ID(s):
Publication Date:
Journal Name:
Proceedings of the 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys)
Page Range or eLocation-ID:
11 to 20
Sponsoring Org:
National Science Foundation
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