Applications

The following are web applications I developed for the Center for Sustainable Energy that allows users to easily access, modify, and visualize data related to renewable energy. The tools I used to build and maintain these sites include HTML5, Javascript, Node.JS, JQuery, Python, SQL, and Various APIs including Google Fusion Tables, Google Maps, and others.

The Most Accurate Solar Residential Rate Analyzer in the World

costhouse
Created a javascript based application that allows a user to type in their address and upload their energy use file (using the green button format). The program geocodes the address and determines the utility and climate zone based on the coordinates. Using Open EI’s utility rates database API this program also determines the appropriate tariff corresponding to the users location and transforms the green button data into actual rates. Finally by using NREL’s PVWatts API and the geocoded address the actual solar insolation at the house location is determined. This allows the client to enter in their desired system capacity and subtract this production from their actual consumption data to produce a before and after solar cost scenario at the residence.

Commercial Solar Rate Calculator

RateCalc

Designed an interactive Commercial Solar Rate Calculator that allows a user to enter energy usage data, select a utility tariff, and solar system size to receive customized estimates on the cost and benefit of switching to solar in SDG&E territory. Technology used to build this web application includes JavaScript, HTML5, and jQuery.

Property Assessed Clean Energy Programs

PACE_MAP

Problem Statement: PACE is a novel way to pay for renewable energy upgrades to a building, including solar and energy efficiency, through one’s property tax. Customers seeking to finance through PACE need to know which providers are in their jurisdiction according to category. This site maps and redirects those customers. The technology stack used to make this application includes Javascript, JQuery, HTML5, SQL, and Python.

Home Energy Upgrade Potential Map

remp

Mining County Assessor Data, SDG&E residential energy use data, NOAA Weather Stations, and other parameters created a multivariate regression model that maps which homes in San Diego are most likely to participate in energy efficiency upgrades. Presented results to the San Diego Regional Energy Partnership Meeting.
http://energycenter.org/remp-sd

 

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