Understanding the energy usage of buildings is crucial for policy-making, energy planning, and achieving sustainable development. Unfortunately, instrumenting buildings to collect energy usage data is difficult and all publicly available datasets typically include only a few hundred homes within a region. Due to their relatively small size, these datasets provide limited insight and are insufficient for analyses that require a larger representation, such as an entire city or town. In recent years, utility companies have installed advanced electric and gas meters, i.e., "smart meters" that enable energy data collection on a massive scale. In this paper, we analyze such a dataset from a utility company that includes energy data from 14,836 smart meters covering a small city. We conduct a wide-ranging analysis of the city's gas and electric data to gain insights into the energy consumption of both individual homes and the city as a whole.   In doing so, we demonstrate how city-scale smart meter datasets can answer a variety of questions on building energy consumption, such as the impact of weather on energy usage, the correlation between the size and age of a building and its energy usage, the impact of increasing levels of renewable penetration, etc. For example, we show that extreme weather events significantly increase energy usage, e.g., by 36% and 11.5% on hot summer and cold winter days, respectively. As another example, we observe that 700 homes are highly energy inefficient as its energy demand variability is twice that of the aggregate grid demand. Finally, we study the impact of increasing level of renewable integration in homes and show that solar penetration rates higher than 20% of demand increases the risk of over-generation and may impact utility operations.
['Srinivasan Iyengar', 'Stephen Lee', 'David E. Irwin', 'Prashant J. Shenoy']
Analyzing Energy Usage on a City-scale using Utility Smart Meters