Tracking Electricity Production Patterns for Residential Solar Electric Systems in Massachusetts

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Tracking Electricity Production Patterns for Residential Solar Electric Systems in Massachusetts

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Title: Tracking Electricity Production Patterns for Residential Solar Electric Systems in Massachusetts
Author: Youngblood, Elizabeth A.
Citation: Youngblood, Elizabeth A. 2015. Tracking Electricity Production Patterns for Residential Solar Electric Systems in Massachusetts. Master's thesis, Harvard Extension School.
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Abstract: The number of residential small-scale solar electric, or photovoltaic (PV) systems installed in Massachusetts has increased over the past five years. However, expanded deployment of residential solar PV may be hindered by lack of awareness of expected electricity generation of solar PV systems, and corresponding financial return. Policymakers are also interested in using limited state resources to support the installation of well-producing solar PV systems that will help meet state greenhouse gas reduction goals. Operational residential solar PV systems may provide a key to understanding electricity production that can inform prospective system owners and policymakers.
This research utilizes monthly electricity production data for 5,400 residential solar PV systems in Massachusetts that were installed between 2010 and 2013. The analysis first focuses on understanding the aggregate dataset and distribution of systems, then explores the impact of fifteen different variables on residential solar PV system electricity production. These variables include shading, rebate eligibility, equipment type, ownership model, date in service year, system cost, selected installer, PTS reporting method, and others.
When controlling for system size, production over all systems was normally distributed. Through a multiple regression analysis, percent shading, roof inclination and azimuth, rebate eligibility and county were variables that had the greatest impact on system production, with shading being key among them, while other variables showed a more nuanced impact. Ultimately, the full regression resulted in an r2 value of 34.2, leaving a majority of the system production variability unexplained. The data also provide insight into the impact of state policy measures surrounding system siting, validation of production data, and forecasting as part of the production based SREC incentive. Ultimately, quantifying the impact of the variables on electricity production patterns can be an effective tool to provide guidance for both prospective system owners and policymakers.
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:24078372
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