Data-driven Toolkit for Solar PV Performance Modeling and Forecasting | Noman Bashir (UMass Amherst)

Nomans presentation was recognized with an honorable mention in the competition, which attracted 21 entries from emerging scholars at 12 universities and organizations. Judges assessed participants’ five-minute “lightning talks” on 1) compelling communication of the core ideas and outcomes of the project to an interdisciplinary audience; and 2) innovation and potential for impact of the energy application and data science methodology.


Abstract: "Solar energy capacity is continuing to increase. The key challenge with integrating solar into buildings and the electric grid is its high-power generation variability, which is a function of many factors, including a site's location, time, weather, and numerous physical attributes. There has been significant prior work on solar performance modeling and forecasting that infers a site's current and future solar generation based on these factors. Accurate solar performance models and forecasts are also a pre-requisite for conducting a wide range of building and grid energy-efficiency research. Unfortunately, much of the prior work is not accessible to researchers, either because it has not been released as open-source, is time-consuming tore-implement, or requires access to proprietary data sources.

The Energy Data Analytics Symposium was organized by the Energy Data Analytics Lab at Duke University, and was supported by a grant from the Alfred P. Sloan Foundation.

Note: Conclusions reached or positions taken by researchers or other grantees represent the views of the grantees themselves and not those of the Alfred P. Sloan Foundation or its trustees, officers, or staff.