Electrification, particularly switching out gas furnaces with electric heat pumps, is increasingly seen as an important decarbonization pathway for residential buildings but could impact low-income residents through higher energy bills. Bill impacts for rent-restricted affordable housing are particularly hard to assess due to barriers discouraging electrification and difficulties with obtaining household level bill data. This study predicts energy consumption and bill impacts using a simple linear regression model and actual bill data from 78 households across three affordable housing properties in California. Two electrification scenarios are considered: electrification of heating and cooling systems (partial electrification) and full electrification. Estimated household energy savings from electrification ranged from 2-62% across the 78 households. Despite this, 26% of the households had estimated bill increases after partial electrification, and 31% after full electrification. For one property with gas water heating, only 8% of households had predicted bill increases after full electrification, compared to 70% and 45% for the properties where cooking was the only remaining gas usage, suggesting that partial electrification may have higher bill savings potential than full electrification unless the existing gas end uses include water heating. For one property where data was available after a partial electrification retrofit, 30% of households saw bill increases despite predicted decreases. Since actual bill impacts can differ greatly from predicted, the results demonstrate the importance of property-specific and household-level analyses both pre- and post-retrofit to support electrification decision-making and policy. This study also highlights the need for electrification policy alternatives to consider difficulties with resident bill data collection, unfavorable rates, and outdated Utility Allowance policy to encourage electrification while mitigating potential bill impacts.

Electrifying the built environment will be an important portion of economy-wide decarbonization efforts. However, significant barriers remain for electrifying existing multifamily buildings. This study focuses specifically on the reality of multifamily electrification in Sacramento Municipal Utility District (SMUD) territory. A review of existing literature uncovers qualitative studies on the barriers to multifamily building retrofits, including the number of stakeholders and aversion to new technologies, and also aims to identify optimal efficiency and load shifting measures to pair with electrification but finds conflicting results about the benefits of such measures. A study of pre- and post-electrification impacts on local electrical infrastructure finds that 10% of distribution transformers, 12% of main service building panels, and 2% of unit panels are currently loaded above their nameplate ratings and will need to be upsized during electrification, and that the specific impact of electrification on this infrastructure is negligible. The average total electrification project cost for buildings with all gas end uses and overloaded electrical infrastructure is projected to be $12,370 per unit, compared to $10,680 per unit for gas like-for-like replacement, but incentives of $3,700 per unit are currently available from SMUD to more than cover the gap. All other building tiers have more comparable electrification vs. gas project costs. The study projects that 92% of customers will see a decrease in total annual energy bills, and that average total energy bills will decrease by 11%. Finally, the study lays out a method for SMUD to target neighborhoods for electrification that will help reverse historic lack of investment into buildings and air quality in disadvantaged communities while avoiding overloaded electrical infrastructure and maximizing energy bill and emissions reductions. Ultimately, the study finds that the biggest barrier to multifamily electrification is not financial but rather the time and expertise required to perform electrification projects.

Energy efficient buildings require a thorough commissioning process to successfully transition from design to operation. However, many new buildings leave their warranty period not operating the way they were designed. This thesis aims to share UC Davis’ Energy and Engineering approach to implementing SkySpark in the post-occupancy phase of commissioning. This thesis also considers other facilities management groups using similar methods to more broadly understand challenges. The insight is used to qualitatively assess SkySpark-driven commissioning and provide a framework for future implementations. Major considerations identified were the workload of the commissioning agent, the availability of data on the first day of the warranty period, the costs of monitoring a building, and the necessity of a two-year warranty period.

The Gambia, like other Sub-Saharan African nations, has seen a slow transition from traditional, dirty fuels to cleaner ones. Although measurement of energy access for lighting and cooking purposes has been performed in other Sub-Saharan African countries, no similar analysis has been published for The Gambia. Using data from the 2010 and 2015 Gambian Integrated Household Surveys, this study derives lighting and cooking fuel access levels and factors driving energy fuel transitions. Generalized linear modeling is used to determine the driving factors of lighting and cooking fuel choice among Gambian households over time. Results indicate that household’s primary lighting fuel shows an overall decline in fuel use and rise in decentralized energy sources, instead of grid electricity. Further, grid electricity is concentrated in urban areas compared to rural. Household primary cooking fuel has shown little change over time and remains to be mostly firewood. Access to charcoal, a slightly cleaner fuel, is concentrated in urban areas.

With 30% of the world's final energy consumption and 40% of carbon dioxide emissions being attributed to the building sector, accurately predicting building energy consumption has become essential for various energy management applications such as identification of energy efficiency measures. With the increasing availability of smart utility meters and data related to building energy consumption, multiple techniques in Artificial Intelligence (AI) such as machine learning are being successfully applied to identify building energy usage patterns and develop focused energy efficiency plans. The UC Davis campus buildings are highly information-intensive and effective interpretation of this building data can help identify energy demand patterns, predict future energy demands, and plan varied types of energy saving strategies. This thesis performs energy demand prediction using multiple machine learning models and analyzes the prediction outcome to identify major areas to focus energy efficiency efforts at a student dining facility on campus: Segundo Dining Commons. Six machine learning models for each of the three energy commodities: Steam, Chilled Water, and Electricity were created and the best-performing model for each commodity was selected to generate a prediction of future demand. The model developed was also fed with four different meteorological scenarios: 0.5°C, 1°C, 2°C rise in outside air temperatures and a typical year with max recorded temperatures for each day of each hour in the past 5 years. The major observations made were simultaneous heating and cooling demands in summer, high energy demands even during school breaks, constant electricity demands and minor changes in demand for different temperature scenarios. Broad energy saving areas were then identified from the observations that can help develop focused energy efficiency plans.

This study focuses on the design of a narrow (44 inches maximum width) vehicle capable of moving two occupants safely at freeway speeds with an emphasis on comfort, efficiency and performance. The design addresses consumer acceptance problems of past narrow vehicles such as "too small", "too ugly", "too unstable", "too wet", "too slow", "too complicated", and "too expensive". A full CAD model was developed to show the external vehicle shape, occupant seating and ergonomics, and the packaging of driveline components. Simulations were run using S/MPLFV and Advisor 2002 to predict vehicle performance and range. The size and mass characteristics of the driveline components used in the simulations were based on commercially-available EV products and selected for the special requirements of a relatively lightweight (450400 kg) vehicle. Dynamic stability and safety of the vehicle are of prime importance and were considered in all phases of the design. The narrow lane "commute/'vehicle is designed to permit two cars to travel abreast on a standard lane, or single file on a shoulder or auxiliary lane of highway. The vehicle modeled incorporates electric drive and is designed to have a >100 mile range using lithium-ion batteries. This study shows that narrow lane vehicles using current technology power train components are a viable option to increase vehicle throughput on existing roadways and reduce transportation energy consumption.