TSRC

There is growing interest in the use of hydrogen as a transportation fuel but the environmental benefits of using hydrogen depend critically on how it is produced and distributed. Leading alternatives to using fossil natural gas to make hydrogen through the conventional method of steam methane reforming include using electrolyzers to split water into hydrogen and oxygen, and the use of biogas as an alternative feedstock to fossil natural gas. This report examines the latest carbon intensity (CI) estimates for these and various other hydrogen production processes, adding important nuances...

Transit bus operations in California are experiencing new challenges due to economic conditions and the ongoing global pandemic. A confluence of factors has created a focus on this critical public-needs serving industry, due to state and local efforts to reduce emissions of pollutants and climate-changing gases. Transit bus operations in California provide essential and additional useful services that offer critical mobility to needy populations (elderly and handicapped) as well as many other groups for whom transit buses provide the most economical, convenient, and low-emission options....

As interest in hydrogen as an energy carrier has increased, the various ways that hydrogen is made are being categorized as “green,” “blue,” “gray,” and other colors in relation to their environmental impact. While these categorizations are somewhat useful to indicate the environmental and climate change impacts of different production pathways, they are not especially useful for policy making or industry decisionmaking purposes because they are subjective. For example, most definitions of green pathways for hydrogen production only include electrolysis from renewable electricity sources;...

Reliable, non-intrusive, short-term (of up to 12 h ahead) prediction of a building's energy demand is a critical component of intelligent energy management applications. A number of such approaches have been proposed over time, utilizing various statistical and, more recently, machine learning techniques, such as decision trees, neural networks and support vector machines. Importantly, all of these works barely outperform simple seasonal auto-regressive integrated moving average models, while their complexity is significantly higher. In this work, we propose a novel low-complexity non-...