a data-driven approach to identify peer-cities for sharing of best practices in energy management by...
TRANSCRIPT
A Data-driven Approach to Identify Peer-cities for Sharing of Best Practices in Energy Management
By Yiqian Jin, Denis Tan, Jing Ye, and Elliot Cohen, Ph.D.Available online at http://denistanwh.github.io
This study is part of theGlobal Trends in Urban Heating & Cooling Project
Created by Elliot Cohen, Henri Torbey, Michael Piccirilli, Yu Tian and Vijay Modi at the Sustainable Engineering Lab of
Columbia University.
Many of the today’s largest and fastest-growing cities are located in South Asia and Sub-Saharan Africa with tropical to sub-tropical
climates unlike those of most OECD member cities in the global North.
In the 1950s, there were only a handful of “great” cities with a population > 5 million; almost all in the U.S., Europe or Japan.
Today, there 70, with just 13 in the U.S., Europe or Japan.
This figure shows urbanization over the past 7 decades. Each dot represents a city with a population greater than 750,000. The area of the dot scales with population.
Commensurate with urbanization, the global center of mass for:• energy consumption• technology deployment• carbon emissions, and• environmental impactsare shifting rapidly from the U.S., Europe and Japan to South Asia, Africa and the Middle East.
As the tropics/sub-tropics become increasingly urban, industrial and affluent, it is important to consider how energy demand for thermal comfort may evolve
differently in these places than it has historically across the OECD.
Urban Energy Demand as a Function of Temperature in two developing cities (red and green) and one developed city (blue)
For a sense of scale…… HVAC already accounts for 35% of total primary energy requirements of the United States (Kwok and Rajkovich 2010)….. and is expected to reach similar proportions in China within 5 years (Wan et al 2011)…
Part I:– What is the current level of demand for electrical
heating & cooling in major emerging cities? – How does per-capita demand for heating &
cooling compare across cities?
Part II:– How can cities learn from one another?
Research Objectives
Handling large meteorological datasets can be unwieldy to the uninitiated.
To help make meteorological data more accessible for a wide range of scientists,
engineers and practitioners, we developed the weatheR library for the statistical computing language R.
The weatheR library facilitates geo-referenced, quality-control batch query of NOAA's National Climatic Data Center -- the world's largest archive of weather data.
Complete methodological details, step-by-step instructions and example vignettes are available on github. To summarize:
– Cities of interest are geo-referenced via the Google Maps API.– City coordinates are passed into a nearest-neighbor search algorithim to the find the k-
nearest active weather stations.– “Best” neighbor is selected from the k-nearest neighbors using multi-objective criteria of
geographic proximity and completeness of the meteorological record.– “Best” meteorlogical record is chosen, subset, scrubbed and interpolated to yield hourly
temperature and humidity observations for each city and period of interest.
Methods: Weather Data
• Hourly electricity demand was compiled from utilities & independent system operators serving cities of interest.
• Data was collected for the past 3 years, if possible, sometimes more.
• 18 non-OECED cities and 21 OECD cities and counting... • Data is available with permission on github
[Note: The National Capital Territory of Delhi (population 23 million) is served by five geographically-distinct distribution companies and is considered as five separate cities in this analysis.]
Methods: Demand Data
Fit Segmented Linear Model to Estimate Empirical Heating Demand, Cooling Demand and Threshold Temperature
Methods: Temperature-Load Model
Results: Per-capita peak demand for electrical cooling (most recent year)
Results: Per-capita peak demand for electrical heating (most recent year)
Results: Heating-Cooling Transition
Results: Per-Capita integral
energy consumption for heating & cooling [kWh/(capita x yr)]
Results: Per-capita normalized
integral energy consumption for heating
& cooling [fraction]
Key Findings for Part I
• OECD cities require 35-90 W/°C per capita above room temperature for cooling (interquartile range of estimates)
• Compared to just 2-9 W/°C per capita for Tropical/Subtropical cities outside the OECD .
• The latter is expected to catch up to the former as household incomes rise and adoption of AC approaches saturation.
• A similar story is unfolding on the heating side, with subtropical cities adopting (for the first time) electric resistive heaters and electric heat pumps for winter space heating.
Part II: Learning from Peer-Cities
• Cross-city collaboration on reducing energy demand tends to be politically- and economically-driven rather than data-driven.– For instance, Chinese cities have attempted to glean energy best
practices from Singapore, despite having vastly different climates (WEF, 2012).
• We posit that by identifying clusters of cities with similar energy demand profiles, sharing of best practices becomes more efficient.
Load
Pro
file
for 3
0 Ci
ties
Clustered into 4 Group'U' shape, peaking in January (boreal winter, austral summer) and a smooth valley in August (boreal summer, austral winter).
Flat, with Mild seasonality.
Strongly Bi-modal, with 4 distinct seasons. Weekly Bi-modal, with 3 seasons.
With logical, yet non-obvious and sometimes unexpected results
• Cluster1: – Abidjan, Cote d’Ivoire– Eugene and Tacoma, USA– Queensland, Australia
• Cluster2: – Dakar, Senegal– Manila, Philippines– Mbabane, Swaziland – Nairobi, Kenya– Antigua, Antigua and Barbuda– Honolulu, USA – Singapore.
• Cluster3: – Amman, Jordan– Chattanooga, Colorado
Springs, Kansas City, Louisville, New York, Omaha, Springfield, Tokyo, Detroit, Indianapolis, and Philadelphia, USA
• Cluster4:– Little Rock, El Paso, Los
Angeles, Memphis, and Sacramento, USA
– Delhi-BRPL– New South Wales, Australia
Cluster 1: – Abidjan, Cote d’Ivoire– Eugene and Tacoma, USA– Queensland, Australia
Cluster 2:– Dakar, Senegal– Manila, Philippines– Mbabane, Swaziland – Nairobi, Kenya– Honolulu, USA – Antigua– Singapore
Cluster 3:– Amman, Jordan– Chattanooga, Colorado Springs,
Kansas City, Louisville, New York, Omaha, Springfield, Tokyo, Detroit, Indianapolis, and Philadelphia, USA
Cluster4:– Little Rock, El Paso, Los Angeles,
Memphis, and Sacramento, USA– Delhi-BRPL– New South Wales, Australia
Extra Materials
Result 1: City-scale peak demand for electrical heating & cooling [MW/(∆T)]
Result 2: Per-capita peak demand for electrical heating & cooling [W/(∆T x capita)]