Data analytics for simplifying thermal efficiency planning in cities

Abstract

More than 44% of building energy consumption in the USA is used for space heating and cooling, and this accounts for 20% of national CO 2 emissions. This prompts the need to identify among the 130 million households in the USA those with the greatest energy-saving potential and the associated costs of the path to reach that goal. Whereas current solutions address this problem by analysing each building in detail, we herein reduce the dimensionality of the problem by simplifying the calculations of energy losses in buildings. We present a novel inference method that can be used via a ranking algorithm that allows us to estimate the potential energy saving for heating purposes. To that end, we only need consumption from records of gas bills integrated with a building's footprint. The method entails a statistical screening of the intricate interplay between weather, infrastructural and residents' choice variables to determine building gas consumption and potential savings at a city scale. We derive a general statistical pattern of consumption in an urban settlement, reducing it to a set of the most influential buildings' parameters that operate locally. By way of example, the implications are explored using records of a set of (N ¼ 6200) buildings in Cambridge, MA, USA, which indicate that retrofitting only 16% of buildings entails a 40% reduction in gas consumption of the whole building stock. We find that the inferred heat loss rate of buildings exhibits a power-law data distribution akin to Zipf's law, which provides a means to map an optimum path for gas savings per retrofit at a city scale. These findings have implications for improving the thermal efficiency of cities' building stock, as outlined by current policy efforts seeking to reduce home heating and cooling energy consumption and lower associated greenhouse gas emissions.

Role of City Texture in Urban Heat Islands at Nighttime

“…4(a)], which yield a coefficient of determination of R 2 ¼ 0.88, similar to what has been observed for a linear relation [33]. Our analysis thus suggests that city texture plays an important role in determining its response to heat radiation phenomena and points to urban design parameters that can be regulated to mitigate UHIs in planning and retrofitting of cities [6,34,35]. In a broader context, our work suggests that tools and methods from statistical physics at the right scale can provide means to quantitatively address the response of cities to climate.…”

“…DOI: 10.1103/PhysRevLett.120.108701 In the century of growing urbanization with 55% of people worldwide living in cities [1], there is an urgent need for establishing quantitative means for controlling urban climate [2]. One of the most substantial local climate effects [3], which has a profound impact on health [4,5] and energy consumption [6,7], is the urban heat island (UHI). While it is well known that the release of solar irradiance heat at night is the inducement of intensified temperatures in cities [8], detailed quantitative descriptions of correlations with city texture parameters are mostly limited to single street canyons [9].…”

Ten questions on urban building energy modeling

“…Aligned with the stakeholders' interest, potential applications of UBEM are reviewed in three domains as summarized in Table 3. Qomi et al, 2016;Hong, Chen, Piette, et al, 2016) Demand energy auditing and forecasting Demand flexibility (Pezzulli et al, 2006;Fu et al, 2009;Delmastro et al, 2017;Wang et al, 2018) Urban resiliency (Gros, Bozonnet and Inard, 2014;Link, Pillich and Klein, 2014;Caro-Martínez and Sendra, 2018;Frayssinet et al, 2018;Oregi et al, 2018) Existing urban buildings retrofiting Energy savings; GHG emissions reduction; Cost effectiveness (Ward and Choudhary, 2014;Lee et al, 2015;McArthur and Jofeh, 2016;Monteiro et al, 2018;Nagpal and Reinhart, 2018) Urban energy planning Energy efficiency (Chow, Chan and Song, 2004;Fu et al, 2009;Lin et al, 2010;Koch, 2016;Delmastro et al, 2017) Urban resiliency under climate (Pisello et al, 2015;Martin et al, 2017; change effects Ciancio et al, 2018;Katal, Mortezazadeh and Wang, 2019) First of all, the formulation of energy policies for urban building stock frequently requires the evaluation of the overall building energy performance of the urban districts (Tardioli et al, 2018)…”

“…For existing districts, by applying the UBEM tool -CityBES, also presented a retrofit analysis case study to evaluate the energy saving potential and cost effectiveness of individual ECMs, as well as ECM packages for small and medium office and retail buildings in San Francisco (Chen, Hong and Piette, 2017). These studies have demonstrated the feasibility of UBEM to serve as a facility planning and maintenance tool for the assessment of effective strategies to reduce energy footprints and GHG emissions (Abdolhosseini Qomi et al, 2016), as well as the potential to evolve over time as new information becomes available (Buffat et al, 2017). On the other hand, the studies also point out that future efforts are required to calibrate district-to city-scale building energy models and validate the results (Booth, Choudhary and Spiegelhalter, 2012;Louis and Cerezo Davila, 2016;Santos et al, 2018).…”

Parametric Performance Analysis and Energy Model Calibration Workflow Integration—A Scalable Approach for Buildings

“…While being less sophisticated than other machine learning techniques available today, multivariate regression models have been chosen because of a set of important features. First of all, standardization [29,30], temporal [34,35] and spatial scalability [36,37], weather normalization using Variable Base Degree-Days (VBDD) [38,39]. After that, the applicability to multiple types of building end-uses [33] and the flexibility with respect to diverse operational strategies and conditions [12,40,41], e.g., accounting for different levels of thermal inertia [42].…”