A critical analysis of the interrelationships between success in musical performance, intelligence quotient, and grade point index at Boston University.

Thesis (M.M.)--Boston Universit

Smart Energy Research Lab: Energy use in GB domestic buildings 2021

Variation in annual, seasonal, and diurnal gas and electricity use with weather, building and occupant characteristics

Virtualizing the Stampede2 Supercomputer with Applications to HPC in the Cloud

Methods developed at the Texas Advanced Computing Center (TACC) are described and demonstrated for automating the construction of an elastic, virtual cluster emulating the Stampede2 high performance computing (HPC) system. The cluster can be built and/or scaled in a matter of minutes on the Jetstream self-service cloud system and shares many properties of the original Stampede2, including: i) common identity management, ii) access to the same file systems, iii) equivalent software application stack and module system, iv) similar job scheduling interface via Slurm. We measure time-to-solution for a number of common scientific applications on our virtual cluster against equivalent runs on Stampede2 and develop an application profile where performance is similar or otherwise acceptable. For such applications, the virtual cluster provides an effective form of "cloud bursting" with the potential to significantly improve overall turnaround time, particularly when Stampede2 is experiencing long queue wait times. In addition, the virtual cluster can be used for test and debug without directly impacting Stampede2. We conclude with a discussion of how science gateways can leverage the TACC Jobs API web service to incorporate this cloud bursting technique transparently to the end user.Comment: 6 pages, 0 figures, PEARC '18: Practice and Experience in Advanced Research Computing, July 22--26, 2018, Pittsburgh, PA, US

The SERL Observatory Dataset: Longitudinal Smart Meter Electricity and Gas Data, Survey, EPC and Climate Data for over 13,000 Households in Great Britain

The Smart Energy Research Lab (SERL) Observatory dataset described here comprises half-hourly and daily electricity and gas data, SERL survey data, Energy Performance Certificate (EPC) input data and 24 local hourly climate reanalysis variables from the European Centre for Medium-Range Weather Forecasts (ECMWF) for over 13,000 households in Great Britain (GB). Participants were recruited in September 2019, September 2020 and January 2021 and their smart meter data are collected from up to one year prior to sign up. Data collection will continue until at least August 2022, and longer if funding allows. Survey data relating to the dwelling, appliances, household demographics and attitudes was collected at sign up. Data are linked at the household level and UK-based academic researchers can apply for access within a secure virtual environment for research projects in the public interest. This is a data descriptor paper describing how the data was collected, the variables available and the representativeness of the sample compared to national estimates. It is intended as a guide for researchers working with or considering using the SERL Observatory dataset, or simply looking to learn more about it

Pave It or Plant It, 7th - 12th Grade Curriculum

Students will use simple watershed models to measure runoff from natural and urbanized areas. They will graph the data to create and compare the hydrographs of these two different types of watersheds. Students will also map different areas in their neighborhood, calculate the amount of impervious surface in the area, and determine how this affects the volume of stormwater runoff expected from a typical rainstorm. Finally, students will list different types of pollutants that enter the streams from urban areas and learn about possible impacts to the stream. They will also learn about possible methods of reducing these impacts through community or individual actions

Self-reported energy use behaviour changed significantly during the cost-of-living crisis in winter 2022/23: insights from cross-sectional and longitudinal surveys in Great Britain

The winter of 2022/23 has seen large increases in energy prices and in the cost of living in many countries around the world, including Great Britain. Here, we report the results of two surveys, combining cross-sectional and longitudinal analysis, in a sample of about 5400 British households. One survey was conducted early in 2023, the other when participants had signed up to an ongoing research study in the past five years. Thermostat settings were about 1°C lower during the cost-of-living crisis than before, and householders were more likely to turn the heating off when the home was unoccupied. The effort to save energy increased compared to pre-cost-of-living-crisis levels. Using the in-home display more in the cost-of-living crisis than before correlated with greater effort to save energy, supporting the notion that displaying energy data can be a useful tool for energy reductions. Finding it difficult to keep comfortably warm in the home and struggling with meeting heating costs were linked to lower wellbeing, strengthening evidence links between cold, damp, and hard-to-heat homes and negative mental health outcomes. About 40% of respondents lowered the flow temperature of the boiler which might imply that highly tailored information campaigns can be effective in changing behaviour

The impact of COVID-19 on household energy consumption in England and Wales from April 2020 to March 2022

The COVID-19 pandemic changed the way people lived, worked, and studied around the world, with direct consequences for domestic energy use. This study assesses the impact of COVID-19 lockdowns in the first two years of the pandemic on household electricity and gas use in England and Wales. Using data for 508 (electricity) and 326 (gas) homes, elastic net regression, neural network and extreme gradient boosting predictive models were trained and tested on pre-pandemic data. The most accurate model for each household was used to create counterfactuals (predictions in the absence of COVID-19) against which observed pandemic energy use was compared. Median monthly model error (CV(RMSE)) was 3.86% (electricity) and 3.19% (gas) and bias (NMBE) was 0.21% (electricity) and −0.10% (gas). Our analysis showed that on average (electricity; gas) consumption increased by (7.8%; 5.7%) in year 1 of the pandemic and by (2.2%; 0.2%) in year 2. The greatest increases were in the winter lockdown (January – March 2021) by 11.6% and 9.0% for electricity and gas, respectively. At the start of 2022 electricity use remained 2.0% higher while gas use was around 1.9% lower than predicted. Households with children showed the greatest increase in electricity consumption during lockdowns, followed by those with adults in work. Wealthier households increased their electricity consumption by more than the less wealthy and continued to use more than predicted throughout the two-year period while the less wealthy returned to pre-pandemic or lower consumption from summer 2021. Low dwelling efficiency was associated with a greater increase in energy consumption during the pandemic. Additionally, this study shows the value of different machine learning techniques for counterfactual modelling at the individual-dwelling level, and our approach can be used to robustly estimate the impact of other events and interventions

Capturing variation in daily energy demand profiles over time with cluster analysis in British homes (September 2019 – August 2022)

This study investigates typical domestic energy demand profiles and their variation over time. It draws on a sample of 13,000 homes from Great Britain, applying k-means cluster analysis to smart meter data on their electricity and gas demand over a three-year period from September 2019 to August 2022. Eight typical demand archetypes are identified from the data, varying in terms of the shape of their demand profile over the course of the day. These include an ‘All daytime’ archetype, where demand rises in the morning and remains high until the evening. Several other archetypes vary in terms of the presence and timing of morning and/or evening peaks. In the case of electricity demand, a ‘Midday trough’ archetype is notable for its negative midday demand and high overnight demand, likely a combination of the effects of rooftop solar panels exporting to the grid during the day and overnight charging of electric vehicles or electric storage heating. The prevalence of each archetype across the sample varies substantially in relation to different temporally-varying factors. Fluctuations in their prevalence on weekends can be identified, as can Christmas Day. Among homes with gas central heating, the prevalence of gas archetypes strongly relates to external temperature, with around half of homes fitting the ‘All daytime’ archetype at temperatures below 0 °C, and few fitting it above 14 °C. COVID-19 pandemic restrictions on work and schooling are associated with households' patterns of daily demand becoming more similar on weekdays and weekends, particularly for households with children and/or workers. The latter group had still not returned to pre-pandemic patterns by March 2022. The results indicate that patterns of daily energy demand vary with factors ranging from societal weekly rhythms and festivals to seasonal temperature changes and system shocks like pandemics, with implications for demand forecasting and policymaking

Capturing variation in daily energy demand profiles over time with cluster analysis in British homes (September 2019 – August 2022)

This study investigates typical domestic energy demand profiles and their variation over time. It draws on a sample of 13,000 homes from Great Britain, applying k-means cluster analysis to smart meter data on their electricity and gas demand over a three-year period from September 2019 to August 2022. Eight typical demand archetypes are identified from the data, varying in terms of the shape of their demand profile over the course of the day. These include an ‘All daytime’ archetype, where demand rises in the morning and remains high until the evening. Several other archetypes vary in terms of the presence and timing of morning and/or evening peaks. In the case of electricity demand, a ‘Midday trough’ archetype is notable for its negative midday demand and high overnight demand, likely a combination of the effects of rooftop solar panels exporting to the grid during the day and overnight charging of electric vehicles or electric storage heating. The prevalence of each archetype across the sample varies substantially in relation to different temporally-varying factors. Fluctuations in their prevalence on weekends can be identified, as can Christmas Day. Among homes with gas central heating, the prevalence of gas archetypes strongly relates to external temperature, with around half of homes fitting the ‘All daytime’ archetype at temperatures below 0 °C, and few fitting it above 14 °C. COVID-19 pandemic restrictions on work and schooling are associated with households' patterns of daily demand becoming more similar on weekdays and weekends, particularly for households with children and/or workers. The latter group had still not returned to pre-pandemic patterns by March 2022. The results indicate that patterns of daily energy demand vary with factors ranging from societal weekly rhythms and festivals to seasonal temperature changes and system shocks like pandemics, with implications for demand forecasting and policymaking