Building Eco-Informatics: Examining the Dynamics of Eco-Feedback Design and Peer Networks to Achieve Sustainable Reductions in Energy Consumption

The built environment accounts for a substantial portion of energy consumption in the United States and in many parts of the world. Due to concerns over rising energy costs and climate change, researchers and practitioners have started exploring the area of eco-informatics to link information from the human, natural and built environments. Specifically, they have begun exploring the use of normative eco-feedback systems to encourage energy efficient behavior and reduce building energy consumption. A normative eco-feedback system provides building occupants with information regarding their own energy consumption and the energy consumption of others in their peer network. While such eco-feedback systems have been observed to drive significant reductions in energy consumption, little is known about the specific system and peer network dynamics that are driving observed reductions. Without this deeper understanding, researchers run the risk of designing eco-feedback systems with low efficacy and may therefore fail to capitalize on potential energy savings. The central aim of this dissertation is to investigate the impact eco-feedback system design and peer network dynamics have on occupant energy consumption behavior. To enable both energy consumption and network data collection, I developed a web-based of an eco-feedback system prototype for an 69 unit residential building in New York City and utilized the system in three empirical experiments. The first experiment was designed to ascertain the effect eco-feedback interface design components have on energy consumption behavior. Analysis of time stamped interface usage and energy consumption data revealed evidence that providing users with incentives and information on their historical consumption levels encourages conservation behavior. Results also suggested that penalizing users for using more energy is not effective in driving energy reductions and instead discourages user engagement. To further understand the effect eco-feedback system design has on energy consumption behavior, a second experiment was conducted using an email-based eco-feedback system. The aim of this study was to examine the role feedback representation plays in encouraging reductions in energy consumption. Participants were broken into two different study groups; one group was provided with feedback in kWh, while a second group was provided with feedback in the equivalent trees required to offset emissions associated with their kWh energy usage. Results revealed that users who received feedback in the form of equivalent trees were more likely to reduce their consumption and had a less dramatic response-relapse effect to feedback emails than their counterparts who received feedback in kWh. The third experiment aimed to characterize the impact peer networks have on modifying energy consumption behavior. Specifically, the experiment was designed to determine if social influence drives energy savings in eco-feedback systems. Analysis of user interaction and energy consumption data was conducted by developing an algorithmic approach based on stochastic and social network test procedures. Social influence was found to impact energy consumption behavior and results indicated the potential of utilizing social influence and peer networks as a means to encourage energy conservation

A data-driven and simulation approach for understanding thermal performance of slum redevelopment in Mumbai, India

Over 54% of the world’s population reside in informal settlements like slums. Thus, any meaningful strategy for transitioning the world toward a more sustainable urban built environment must address the redevelopment of slum communities. This study aims to take a first-step towards the design optimization of slum redevelopment by evaluating the thermal performance of various urban slum redevelopment morphologies in Mumbai, India. We propose a data-driven and simulation-based approach to understand how slum redevelopment morphologies impact thermal performance and validate our approach using real data collected from a field site in the Dharavi slum, Mumbai, India. Results indicate that the existing horizontal form outperforms proposed vertical redevelopment forms in terms of maintaining lower operating temperatures. In other words, vertical redevelopment could worsen thermal comfort conditions for existing residents. Given the current push towards vertical housing structures by the Indian government, our results have substantial implications for the design and redevelopment of such informal settlements and lays the groundwork for establishing design guidelines and processes that ensure more sustainable and inclusive redevelopment

Detailed Case Studies

Wireless body area networks (WBANs) are one of the key technologies that support the development of pervasive health monitoring (remote patient monitoring systems), which has attracted more attention in recent years. These WBAN applications requires stringent security requirements as they are concerned with human lives. In the recent scenario of the corona pandemic, where most of the healthcare providers are giving online services for treatment, DDoS attacks become the major threats over the internet. This chapter particularly focusses on detection of DDoS attack using machine learning algorithms over the healthcare environment. In the process of attack detection, the dataset is preprocessed. After preprocessing the dataset, the cleaned dataset is given to the popular classification algorithms in the area of machine learning namely, AdaBoost, J48, k-NN, JRip, Random Committee and Random Forest classifiers. Those algorithms are evaluated independently and the results are recorded. Results concluded that J48 outperform with accuracy of 99.98% with CICIDS dataset and random forest outperform with accuracy of 99.917, but it takes the longest model building time. Depending on the evaluation performance the appropriate classifier is selected for further DDoS detection at real-time

A Global Building Occupant Behavior Database

This paper introduces a database of 34 field-measured building occupant behavior datasets collected from 15 countries and 39 institutions across 10 climatic zones covering various building types in both commercial and residential sectors. This is a comprehensive global database about building occupant behavior. The database includes occupancy patterns (i.e., presence and people count) and occupant behaviors (i.e., interactions with devices, equipment, and technical systems in buildings). Brick schema models were developed to represent sensor and room metadata information. The database is publicly available, and a website was created for the public to access, query, and download specific datasets or the whole database interactively. The database can help to advance the knowledge and understanding of realistic occupancy patterns and human-building interactions with building systems (e.g., light switching, set-point changes on thermostats, fans on/off, etc.) and envelopes (e.g., window opening/closing). With these more realistic inputs of occupants’ schedules and their interactions with buildings and systems, building designers, energy modelers, and consultants can improve the accuracy of building energy simulation and building load forecasting

Modeling aggregate human mobility patterns in cities based on the spatial distribution of local infrastructure

Understanding human mobility patterns in urban areas is key to solving a wide range of socio-technical problems at the human-infrastructure interface. Extending the intervening opportunities concept, we showcase a data-driven, network-based model that reproduces aggregate mobility patterns in cities. Using this model, we create a digital replication of daily travel across different trip purposes in 5 U.S. metropolitan areas and compare results against publicly available reference data. We find that our proposed model explains a large fraction of the variation in mean and median travel distance across the 5 cities. In particular, it accurately captures the effect of density on aggregate travel patterns. These findings add to evidence that human mobility patterns are strongly governed by the structure of the built environment. We discuss implications for the ongoing transformation of cities and for developing more sophisticated models that replicate human behavior based on crowd-sourced, spatio-temporal data streams

TOM.D: Taking advantage of microclimate data for urban building energy modeling

Urban Building Energy Modeling (UBEM) provides a framework for decarbonization decision-making on an urban scale. However, existing UBEM systems routinely neglect microclimate effects on building energy consumption, potentially leading to major sources of error. In this work, we attempt to address these sources of error by proposing the large scale collection of remote sensing and climate modeling data to improve the capabilities of existing systems. We explore situations when remote sensing might be most valuable, particularly when high quality weather station data might not be available. We show that lack of access to weather station data is unlikely to be driving existing errors in energy models, as most buildings are likely to be close enough to collect high quality data. We also highlight the significance of Landsat8’s thermal instrumentation to capture pertinent temperatures for the buildings through feature importance visualizations. Our analysis then characterizes the seasonal benefits of microclimate data for energy prediction. Landsat8 is found to provide a potential benefit of an 8% reduction in electricity prediction error in the spring and summertime of New York City. In contrast, NOAA RTMA may provide a benefit of a 2.5% reduction in natural gas prediction error in the winter and spring. Finally, we explore the potential of remote sensing to enhance the quality of energy predictions at a neighborhood level. We show that benefits for individual buildings translates to the regional level, as we can achieve improved predictions for groups of buildings

Natural ventilation versus air pollution: assessing the impact of outdoor pollution on natural ventilation potential in informal settlements in India

Despite the proven benefits of natural ventilation (NV) as an effective low-carbon solution to meet growing cooling demand, its effectiveness can be constrained by poor outdoor air quality. Here, we propose a modeling approach that integrates highly granular air pollution data with a coupled EnergyPlus and differential equation airflow model to evaluate how NV potential for space cooling changes when accounting for air pollution exposure (PM2.5). Given the high vulnerability of low-income populations to air pollution and the dearth of energy and thermal comfort research on informal settlements, we applied our model to a typical informal settlement residence in two large Indian cities: New Delhi and Bangalore. Our results indicate that outdoor PM2.5 levels have a significant impact on NV potential especially in highly polluted cities like New Delhi. However, we found that low-cost filtration (MERV 14) increased the NV potential by 25% and protected occupants from harmful exposure to PM2.5 with a minor energy penalty of 6%. We further find that adoption of low-cost filtration is a viable low-carbon solution pathway as it provides both thermal comfort and exposure protection at 65% less energy intensity—energy intensity reduced to 60 kWh m ^−2 from 173.5 kWh m ^−2 in case of adoption of potentially unaffordable full mechanical air conditioning. Our work highlights ample opportunities for reducing both air pollution and energy consumption in informal settlements across major Indian cities. Finally, our work can guide building designers and policymakers to reform building codes for adopting low-cost air filtration coupled with NV and subsequently reduce energy demand and associated environmental emissions

The impact of urban form on daily mobility demand and energy use: Evidence from the United States

The causal relationship between urban form, in particular density, and travel demand is subject to debate. Here, we investigate this relationship using a structured regression approach applied to a large-scale layered dataset of travel patterns and urban form. We find that residents of the densest urban areas use 80% less energy for transportation than residents of rural areas and explore the causal factors influencing this relationship. We find that the primary driver of the density–energy use relationship is the larger number of nearby destinations available to urban dwellers, followed by differences in road network properties and public transit infrastructure. The energy benefits of urban dwelling are weakened when there are more destinations available 10–100 km away due to urban sprawl. While these causal factors are correlated with density, urban form can substantially affect travel energy use even within a given density bracket. We also show that urban form predominantly influences how and where people travel, rather than how often and how long. These results outline pathways for cities and communities to reduce the environmental impact of travel while increasing access to relevant destinations

Automated identification of urban substructure for comparative analysis.

Neighborhoods are the building blocks of cities, and thus significantly impact urban planning from infrastructure deployment to service provisioning. However, existing definitions of neighborhoods are often ill suited for planning in both scale and pattern of aggregation. Here, we propose a generalized, scalable approach using topological data analysis to identify barrier-enclosed neighborhoods on multiple scales with implications for understanding social mixing within cities and the design of urban infrastructure. Our method requires no prior domain knowledge and uses only readily available building parcel information. Results from three American cities (Houston, New York, San Francisco) indicate that our method identifies neighborhoods consistent with historical approaches. Additionally, we uncover a consistent scale in all three cities at which physical isolation drives neighborhood emergence. However, our methods also reveal differences between these cities: Houston, although more disconnected on larger spatial scales than New York and San Francisco, is less disconnected at smaller scales

SynCity: Using open data to create a synthetic city of hourly building energy estimates by integrating data-driven and physics-based methods

10.1016/j.apenergy.2020.115981APPLIED ENERGY28