Problem Description:
The analytics question: How does the City of New York prioritize resource allocation, market incentives and policy interventions toward building energy reductions and sustainability goals. The Mayor of New York has signed an executive order (EO 26), to commit to the principles of the Paris Climate Agreement (City of New York, 2017).
In New York, 70% of GHGs are derived from energy consumed in buildings. Building energy efficiency is linked to GHG reductions. The latest benchmarking report released on building energy and water use, relies on the annual data collected under Local Law 84 and 87. Overall, reductions have not been substantial within the residential sector and trailing the improvements of the municipal and commercial sector (City of New York, 2017).
Data:
Benchmarking Local Law 84 (LL84): NYC has a self-reported annual building energy benchmarking law for buildings over 50,000 square feet . The data is logged in the Environmental Protection Agency’s (EPA), ENERGY STAR Portfolio Manager. This tool compares buildings typologies across the nation and is normalized for weather and usage to designate a score based on a ranking percentile.
Primary Land Use Tax Lot Output (PLUTO): PLUTO is a publicly available record of tax lot level data of NYC land usage and building information . PLUTO includes land use, geographic data, building characteristics maintained by the Department of City Planning (DCP), the Department of Finance (DOF), the Department of Citywide Administrative Services (DCAS), and the Landmarks Preservation Commission.
Data for building energy (LL84) is cleansed of bad data, empty values (NaN) and anomalous outliers. LL84, PLUTO, and data sets are merged on the common category, BBL to then be analyzed and explored for predictive models to benchmark the current state of performance.
Analysis:
The analysis will look to compare peer building types, also known as building "typology", to develop metrics to prioritize energy projects and quantify policy interventions. The methodology will use multivariate regression or other linear regression tools to build benchmarks and thresholds of building performance. Also it may be worth looking at SVM, PCA and regression trees. The exogenous factors to explores are measurements of building occupancy, location, age and others to characterize correlative indicators of poor performers to focus stakeholder's attention.
Deliverable:
The residential sector of buildings is trailing in energy efficiency improvements compared to the municipal and commercial sector. To assist in designing interventions, a building energy statistical ranking and energy benchmarking model with figures and tables will help to develop iterative policy and market interventions. A public discussion with clear information of the building population performance is key for stakeholder engagement and mobilizartion toward actions.
References:
City of New York. (2017). New York City Local Law 84 Benchmarking Report: October 2017. New York: City of New YorkU.S. Environmental Protection Agency (EPA), “ENERGY STAR Portfolio Manager Technical Reference: Greenhouse Gas Emissions.” (2013.) Retrieved from:
portfoliomanager.energystar.gov/pdf/reference/Emissions.pdf