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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

How good are learning-based control v.s. model-based control for load shifting? Investigations on a single zone building energy system

Both model predictive control (MPC) and deep reinforcement learning control (DRL) have been presented as a way to approximate the true optimality of a dynamic programming problem, and these two have shown significant operational cost saving potentials for building energy systems. Furthermore, there is still a lack of in-depth quantitative studies on their approximation levels to the true optimality, especially in the building energy domain. To fill in the gap, this paper provides a numerical framework that enables the evaluation of the optimality levels of different controllers for building energy systems. This framework is then used to comprehensively compare the optimal control performance of both MPC and DRL controllers with given computation budgets for a single zone fan coil unit system. Note the optimality is estimated based on a user-specific selection of trade-off weights among energy costs, thermal comfort and control slew rates. Compared with the best optimality we can find through expensive optimization simulations, the best DRL agent can maximally approximate the optimality by 96.54%, which outperforms the best MPC whose optimality level is 90.11%. However, due to the stochasticity, the DRL agent is only expected to approximate the optimality by 90.42%, which is almost equivalent to the best MPC. Except for Proximal Policy Optimization (PPO), all DRL agents can have a better approximation to the optimality than the best MPC, and are expected to have better approximation than the MPC with a prediction horizon of 32 steps (15 min per step). In terms of reducing energy cost and thermal discomfort, MPC can outperform the rule-based control (RBC) by 18.47%–25.44%. DRL can be expected to outperform RBC by 18.95%–25.65% ,and the best DRL control policy can outperform RBC by 20.29%–29.72%. Although the comparison of the optimality level is performed in a perfect setting, e.g., MPC assumes perfect models, and DRL assumes a perfect offline training process and online deployment process, this can shed insight on their capabilities of approximating to the original dynamic programming problem.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2015

Long term energy and emission implications of a global shift to electricity-based public rail transportation system

With high reliance on light-duty vehicles in the present, the future of global transportation system is also geared towards private modes, which has significant energy and emission implications. Public transportation has been argued as an alternative strategy for meeting the rising transportation demands of the growing world, especially the poor, in a sustainable and energy efficient way. The present study analyzes an important yet under-researched question – what are the long-term energy and emission implications of an electric rail based passenger transportation system for meeting both long and short distance passenter transportation needs? We analyze a suite of electric rail share scenarios with and without climate policy. In the reference scenario, the transportation system will evolve towards dominance of fossil based light-duty vehicles. We find that an electric rail policy is more successful than an economy wide climate policy in reducing transport sector energy demand and emissions. Economy wide emissions however can only be reduced through a broader climate policy, the cost of which can be reduced by hundreds of billions of dollars across the century when implemented in combination with the transport sector focused electric rail policy. Moreover, higher share of electric rail enhances energy security for oil importing nations and reduces vehicular congestion and road infrastructure requirement as well.

Chaturvedi, Vaibhav↗
DOE OSTI2019

Context dependent fungal and bacterial soil community shifts in response to recent wildfires in the Southern Appalachian Mountains

Decades of fire suppression coupled with changing climatic conditions have increased the frequency and intensity of wildfires. Here, the Southern Appalachia region of the United States is predicted to be particularly susceptible to climatic changes, with predicted increases in fire severity and occurrence. Following the record breaking fire season in 2016 in Southern Appalachia, we examined wildfire impacts on soil chemistry and below ground communities (fungi and bacteria – Illumina MiSeq) within two substrates (duff and soil) at two adjacent locations with similar plant communities (Great Smoky Mountains National Park – ‘Chimney Top 2’ Fire (GRSM) and Nantahala National Forest – ‘Cliffside’ Fire (NNF)) from replicate plots representing a range of fire severities (unburned, low severity, moderate severity, severe). Differing fire severities changed community composition, and fire severity played a stronger role in structuring bacterial communities than in structuring fungal communities. Further, fire impacts on soil communities and functional guilds responses were location- and substrate-specific with NNF responding more strongly to fire than GRSM. Additionally, using a novel analysis tool (Axis Weighted Ordination Distance – AWOrD), domain and location specific responses to wildfire severity are demonstrated. Taken together, our results suggest context-dependency in microbial responses to fire that must be accounted for to generate ecosystem-wide recovery predictions.

54 ENVIRONMENTAL SCIENCES↗