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1. Geophysical constraints on the reliability of solar and wind power in the United States

   https://doi.org/10.1039/c7ee03029k  

   Shaner, Matthew R.; Davis, Steven J.; Lewis, Nathan S.; Caldeira, Ken (January 2018, Energy & Environmental Science)

   We analyze 36 years of global, hourly weather data (1980–2015) to quantify the covariability of solar and wind resources as a function of time and location, over multi-decadal time scales and up to continental length scales. Assuming minimal excess generation, lossless transmission, and no other generation sources, the analysis indicates that wind-heavy or solar-heavy U.S.-scale power generation portfolios could in principle provide ∼80% of recent total annual U.S. electricity demand. However, to reliably meet 100% of total annual electricity demand, seasonal cycles and unpredictable weather events require several weeks’ worth of energy storage and/or the installation of much more capacity of solar and wind power than is routinely necessary to meet peak demand. To obtain ∼80% reliability, solar-heavy wind/solar generation mixes require sufficient energy storage to overcome the daily solar cycle, whereas wind-heavy wind/solar generation mixes require continental-scale transmission to exploit the geographic diversity of wind. Policy and planning aimed at providing a reliable electricity supply must therefore rigorously consider constraints associated with the geophysical variability of the solar and wind resource—even over continental scales. 

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2. Solar-TK: A Data-Driven Toolkit for Solar PV Performance Modeling and Forecasting

   https://doi.org/10.1109/MASS.2019.00060  

   Bashir, Noman; Chen, Dong; Irwin, David; Shenoy, Prashant (November 2019, IEEE 16th International Conference on Mobile Ad Hoc and Sensor Systems (MASS))

   Solar energy capacity is continuing to increase. The key challenge with integrating solar into buildings and the electric grid is its high power generation variability, which is a function of many factors, including a site's location, time, weather, and numerous physical attributes. There has been significant prior work on solar performance modeling and forecasting that infers a site's current and future solar generation based on these factors. Accurate solar performance models and forecasts are also a pre-requisite for conducting a wide range of building and grid energy-efficiency research. Unfortunately, much of the prior work is not accessible to researchers, either because it has not been released as open source, is time-consuming to re-implement, or requires access to proprietary data sources. To address the problem, we present Solar-TK, a data-driven toolkit for solar performance modeling and forecasting that is simple, extensible, and publicly accessible. Solar-TK's simple approach models and forecasts a site's solar output given only its location and a small amount of historical generation data. Solar-TK's extensible design includes a small collection of independent modules that connect together to implement basic modeling and forecasting, while also enabling users to implement new energy analytics. We plan to release Solar-TK as open source to enable research that requires realistic solar models and forecasts, and to serve as a baseline for comparing new solar modeling and forecasting techniques. We compare Solar-TK's simple approach with PVlib and show that it yields comparable accuracy. We present three case studies showing how Solar-TK can advance energy-efficiency research. 

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3. Creating Temporally Correlated High-Resolution Profiles of Load Injection Using Constrained Generative Adversarial Networks

   https://doi.org/10.1109/NAPS61145.2024.10741806  

   Shah, Hritik Gopal; Azimian, Behrouz; Pal, Anamitra (October 2024, IEEE)

   Traditional smart meters, which measure energy usage every 15 minutes or more and report it at least a few hours later, lack the granularity needed for real-time decision-making. To address this practical problem, we introduce a new method using generative adversarial networks (GAN) that enforces temporal consistency on its high-resolution outputs via hard inequality constraints using convex optimization. A unique feature of our GAN model is that it is trained solely on slow timescale aggregated historical energy data obtained from smart meters. The results demonstrate that the model can successfully create minute-by-minute temporally correlated profiles of power usage from 15-minute interval average power consumption information. This innovative approach, emphasizing inter-neuron constraints, offers a promising avenue for improved high-speed state estimation in distribution systems and enhances the applicability of data-driven solutions for monitoring and subsequently controlling such systems. 

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4. An Open Problem: Energy Data Super-Resolution

   https://doi.org/10.1145/3427771.3429995  

   Kukunuri, Rithwik; Batra, Nipun; Wang, Hongning (November 2020, NILM'20: Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring)

   null (Ed.)

   In this notes paper, we present an open problem to the Buildsys community: energy data super-resolution, referring to the task of estimating the power consumption of a home at a higher resolution given the low-resolution power consumption. Super-resolution is especially useful when the smart meters collect data at a very low-sampling rate owing to a plethora of issues such as bandwidth, pricing, old hardware, among others. The problem is motivated by the success of image super resolution in the computer vision community. In this paper, we formally introduce the problem and present baseline methods and the algorithms we used to "solve" this problem. We evaluate the performance of the algorithms on a real-world dataset and discuss the results. We also discuss what makes this problem hard and why a trivial baseline is hard to beat. 

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5. Safety Analysis of AMI Networks Through Smart Fraud Detection

   https://doi.org/10.1109/CNS.2019.8802845  

   Jakaria, A H; Rahman, Mohammad Ashiqur; Moula Mehedi Hasan, Md Golam (June 2019, IEEE Conference on Communications and Network Security (CNS))

   Advanced metering infrastructure (AMI)is a critical part of a modern smart grid that performs the bidirectional data flow of sensitive power information such as smart metering data and control commands. The real-time monitoring and control of the grid are ensured through AMI. While smart meter data helps to improve the overall performance of the grid in terms of efficient energy management, it has also made the AMI an attractive target of cyber attackers with a goal of stealing energy. This is performed through the physical or cyber tampering of the meters, as well as by manipulating the network infrastructure to alter collected data. Proper technology is required for the identification of energy fraud. In this paper, we propose a novel technique to detect fraudulent data from smart meters based on the energy consumption patterns of the consumers by utilizing deep learning techniques. We also propose a method for detecting the suspicious relay nodes in the AMI infrastructure that may manipulate the data while forwarding it to the aggregators. We present the performance of our proposed technique, which shows the correctness of the models in identifying the suspicious smart meter data. 

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