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1. Unveiling hidden energy poverty using the energy equity gap

   https://doi.org/10.1038/s41467-022-30146-5  

   Cong, Shuchen; Nock, Destenie; Qiu, Yueming Lucy; Xing, Bo (May 2022, Nature Communications)

   Abstract Income-based energy poverty metrics ignore people’s behavior patterns, particularly reducing energy consumption to limit financial stress. We investigate energy-limiting behavior in low-income households using a residential electricity consumption dataset. We first determine the outdoor temperature at which households start using cooling systems, the inflection temperature. Our relative energy poverty metric, theenergy equity gap, is defined as the difference in the inflection temperatures between low and high-income groups. In our study region, we estimate the energy equity gap to be between 4.7–7.5 °F (2.6–4.2 °C). Within a sample of 4577 households, we found 86 energy-poor and 214 energy-insecure households. In contrast, the income-based energy poverty metric, energy burden (10% threshold), identified 141 households as energy-insecure. Only three households overlap between our energy equity gap and the income-based measure. Thus, the energy equity gap reveals a hidden but complementary aspect of energy poverty and insecurity. 

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2. Redefining Energy Justice in Physics Classrooms

   https://doi.org/10.1089/env.2021.0042  

   Hernandez, Jessica; Scherr, Rachel; Robertson, Amy D. (April 2022, Environmental Justice)

   Energy is one of the fundamental topics taught in high school physics. However, energy continues to betaught as an abstract concept that removes itself from the social implications energy systems have onsociety, in particular toward Indigenous communities. Given the importance of integrating discussionsaround equity into our science courses, in this study we propose a way in which energy justice can beredefined and included in physics classrooms. Redefining energy justice into physics classrooms allows usto connect energy justice to existing energy physics curriculum and lessons plans. In Summer 2020, 22physics teachers participated in a professional development that centered on discussions around energyand equity. We analyzed and coded teachers’ dialogues and conversations around energy and equity toidentify energy justice pillars. The energy justice pillars we identified formed the basis of an energy justiceframework that redefines energy justice for physics classrooms. This energy justice framework allows usto bridge the separation between physics and social justice, as they continue to be viewed as two separateschools of thought in the field of physics. 

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3. Probability distribution for vacuum energy flux fluctuations in two spacetime dimensions

   https://doi.org/10.1103/PhysRevD.111.085015  

   Fewster, Christopher J; Ford, L H (April 2025, Physical Review D)

   The probability distribution for vacuum fluctuations of the energy flux in two dimensions is constructed, along with the joint distribution of energy flux and energy density. Our approach is based on previous work on probability distributions for the energy density in two-dimensional conformal field theory. In both cases, the relevant stress tensor component must be averaged in time, and the results are sensitive to the form of the averaging function. Here we present results for two classes of such functions, which include the Gaussian and Lorentzian functions. The distribution for the energy flux is symmetric, unlike that for the energy density. In both cases, the distribution may possess an integrable singularity. The functional form of the flux distribution function involves a modified Bessel function and is distinct from the shifted Gamma form for the energy density. By considering the joint distribution of energy flux and energy density, we show that the distribution of energy flux tends to be more centrally concentrated than that of the energy density. We also determine the distribution of energy fluxes, conditioned on the energy density being negative. Some applications of the results are also discussed. 

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4. The Digital Power Networks: Energy Dissemination Through a Micro-Grid

   https://doi.org/10.1109/Cybermatics_2018.2018.00068  

   Miao, Xin; Rojas-Cessa, Roberto; Mohamed, Ahmed; Grebel, Haim (July 2018, 2018 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData))

   The Digital Power Network (DPN) is an energy-on-demand approach. In terms of Internet of Things (IoT), it treats the energy itself as a `thing' to be manipulated (in contrast to energy as the `thing's enabler'). The approach is mostly appropriate for energy starving micro-grids with limited capacity, such as a generator for the home while the power grid is down. The process starts with a request of a user (such as, appliance) for energy. Each appliance, energy source or energy storage has an address which is able to communicate its status. A network server, collects all requests and optimizes the energy dissemination based on priority and availability. Energy is then routed in discrete units to each particular address (say air-condition, or, A/C unit). Contrary to packets of data over a computer network whose data bits are characterized by well-behaved voltage and current values at high frequencies, here we deal with energy demands at highvoltage, low-frequency and fluctuating current. For example, turning a motor ON requires 8 times more power than the level needed to maintain a steady states operation. Our approach is seamlessly integrating all energy resources (including alternative sources), energy storage units and the loads since they are but addresses in the network. Optimization of energy requests and the analysis of satisfying these requests is the topic of this paper. Under energy constraints and unlike the current power grid, for example, some energy requests are queued and granted later. While the ultimate goal is to fuse information and energy together through energy digitization, in its simplest form, this micro-grid can be realized by overlaying an auxiliary (communication) network of controllers on top of an energy delivery network and coupling the two through an array of addressable digital power switches. 

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5. Packet Pruning: Finding Better Energy Spending Policies for Batteryless Human Activity Recognition

   https://doi.org/10.1109/BSN63547.2024.10780463  

   Cooper, Geffen; Marculescu, Radu (October 2024, IEEE)

   Batteryless sensing devices which rely on energy harvesting can enable more sustainable and long-lasting Internet of Things (IoT) based wearables. While it has become feasible to implement energy-harvesting based wearables for digital health applications, it remains challenging to integrate such devices and the data they collect into machine learning pipelines for tasks such as human activity recognition (HAR). A key obstacle is uncertainty in the data acquisition process. Given the discontinuous and uncertain availability of harvested energy, when should a sensor spend energy to sample and transmit data packets for processing? A common approach is to spend energy opportunistically by sending packets whenever sufficient energy is available. However, when considering a specific task, namely HAR with kinetic energy harvesting based sensors, this approach unfairly prioritizes data from activities where more energy can be harvested (e.g., running). In this work, we improve the opportunistic energy spending policy by pruning redundant packets to reallocate energy towards activities where less energy is harvested. Our approach results in an increase in the F1-score of ‘lower energy’ activities while having a minimal impact on the F1-score of ‘higher energy’ activities. 

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