Jointly Managing Electrical and Thermal Energy in Solar- and
Battery-powered Computer Systems
- URL: http://arxiv.org/abs/2305.00855v1
- Date: Mon, 1 May 2023 14:53:53 GMT
- Title: Jointly Managing Electrical and Thermal Energy in Solar- and
Battery-powered Computer Systems
- Authors: Noman Bashir, Yasra Chandio, David Irwin, Fatima M. Anwar, Jeremy
Gummeson, Prashant Shenoy
- Abstract summary: We develop a thermodynamic model that captures the interplay of electrical and thermal energy in environmentally-powered computer systems.
We evaluate the thermal effects that impact these systems using a small-scale prototype and a programmable incubator.
- Score: 2.4607540629220384
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Environmentally-powered computer systems operate on renewable energy
harvested from their environment, such as solar or wind, and stored in
batteries. While harvesting environmental energy has long been necessary for
small-scale embedded systems without access to external power sources, it is
also increasingly important in designing sustainable larger-scale systems for
edge applications. For sustained operations, such systems must consider not
only the electrical energy but also the thermal energy available in the
environment in their design and operation. Unfortunately, prior work generally
ignores the impact of thermal effects, and instead implicitly assumes ideal
temperatures. To address the problem, we develop a thermodynamic model that
captures the interplay of electrical and thermal energy in
environmentally-powered computer systems. The model captures the effect of
environmental conditions, the system's physical properties, and workload
scheduling on performance. In evaluating our model, we distill the thermal
effects that impact these systems using a small-scale prototype and a
programmable incubator. We then leverage our model to show how considering
these thermal effects in designing and operating environmentally-powered
computer systems of varying scales can improve their energy-efficiency,
performance, and availability.
Related papers
- Predicting Solar Heat Production to Optimize Renewable Energy Usage [0.0]
We present an approach that uses machine learning to automatically construct and continuously adapt a model that predicts heat production.
We present positive empirical results for the predictive accuracy of our solution, and discuss the impact of these results on the end-to-end system.
arXiv Detail & Related papers (2024-05-16T10:32:39Z) - Global Transformer Architecture for Indoor Room Temperature Forecasting [49.32130498861987]
This work presents a global Transformer architecture for indoor temperature forecasting in multi-room buildings.
It aims at optimizing energy consumption and reducing greenhouse gas emissions associated with HVAC systems.
Notably, this study is the first to apply a Transformer architecture for indoor temperature forecasting in multi-room buildings.
arXiv Detail & Related papers (2023-10-31T14:09:32Z) - Non-Intrusive Electric Load Monitoring Approach Based on Current Feature
Visualization for Smart Energy Management [51.89904044860731]
We employ computer vision techniques of AI to design a non-invasive load monitoring method for smart electric energy management.
We propose to recognize all electric loads from color feature images using a U-shape deep neural network with multi-scale feature extraction and attention mechanism.
arXiv Detail & Related papers (2023-08-08T04:52:19Z) - Autonomous Payload Thermal Control [65.268245109828]
The proposed framework is able to learn to control the payload processing power to maintain the temperature under operational ranges.
The framework will be shipped in the future IMAGIN-e mission and hosted in the ISS.
arXiv Detail & Related papers (2023-07-28T09:40:19Z) - Intelligent Energy Management Systems -- A Review [0.0]
People consume electricity in order to use home/work appliances and devices and also reach certain levels of comfort while working or being at home.
Confronting such a problem efficiently will affect both the environment and our society.
Monitoring energy consumption in real-time, changing energy wastage behavior of occupants and using automations with incorporated energy savings scenarios are ways to decrease global energy footprint.
arXiv Detail & Related papers (2022-05-16T20:10:20Z) - Intelligent edge-based recommender system for internet of energy
applications [2.1874189959020423]
This paper presents the full integration of a proposed energy efficiency framework into the Home-Assistant platform using an edge-based architecture.
End-users can visualize their consumption patterns as well as ambient environmental data using the Home-Assistant user interface.
More notably, explainable energy-saving recommendations are delivered to end-users in the form of notifications via the mobile application.
arXiv Detail & Related papers (2021-11-25T23:28:14Z) - Learning, Computing, and Trustworthiness in Intelligent IoT
Environments: Performance-Energy Tradeoffs [62.91362897985057]
An Intelligent IoT Environment (iIoTe) is comprised of heterogeneous devices that can collaboratively execute semi-autonomous IoT applications.
This paper provides a state-of-the-art overview of these technologies and illustrates their functionality and performance, with special attention to the tradeoff among resources, latency, privacy and energy consumption.
arXiv Detail & Related papers (2021-10-04T19:41:42Z) - Enforcing Policy Feasibility Constraints through Differentiable
Projection for Energy Optimization [57.88118988775461]
We propose PROjected Feasibility (PROF) to enforce convex operational constraints within neural policies.
We demonstrate PROF on two applications: energy-efficient building operation and inverter control.
arXiv Detail & Related papers (2021-05-19T01:58:10Z) - Energy use in quantum data centers: Scaling the impact of computer
architecture, qubit performance, size, and thermal parameters [0.0]
As quantum computers increase in size, the total energy used by a quantum data center will become a greater concern.
The cooling requirements of quantum computers, which must operate at temperatures near absolute zero, are determined by computing system parameters.
This paper reports the impact of computer architecture and thermal parameters on the overall energy requirements.
arXiv Detail & Related papers (2021-03-30T23:50:02Z) - AI Chiller: An Open IoT Cloud Based Machine Learning Framework for the
Energy Saving of Building HVAC System via Big Data Analytics on the Fusion of
BMS and Environmental Data [12.681421165031576]
Energy saving and carbon emission reduction in buildings is one of the key measures in combating climate change.
The optimization of chiller system power consumption had been extensively studied in the mechanical engineering and building service domains.
With the advance of big data and AI, the adoption of machine learning into the optimization problems becomes popular.
arXiv Detail & Related papers (2020-10-09T09:51:03Z) - NeurOpt: Neural network based optimization for building energy
management and climate control [58.06411999767069]
We propose a data-driven control algorithm based on neural networks to reduce this cost of model identification.
We validate our learning and control algorithms on a two-story building with ten independently controlled zones, located in Italy.
arXiv Detail & Related papers (2020-01-22T00:51:03Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.