A Generative AI Technique for Synthesizing a Digital Twin for U.S. Residential Solar Adoption and Generation

• URL: http://arxiv.org/abs/2410.08098v1
• Date: Thu, 10 Oct 2024 16:41:43 GMT
• Title: A Generative AI Technique for Synthesizing a Digital Twin for U.S. Residential Solar Adoption and Generation
• Authors: Aparna Kishore, Swapna Thorve, Madhav Marathe,
• Abstract summary: We discuss a novel methodology to generate a granular, residential-scale realistic dataset for rooftop solar adoption across the contiguous United States. The data-driven methodology consists of: (i) integrated machine learning models to identify PV adopters, (ii) methods to augment the data using explainable AI techniques, and (iii) methods to generate household-level hourly solar energy output. The resulting synthetic datasets are validated using real-world data and can serve as a digital twin for modeling downstream tasks.
• Score: 0.6144680854063939
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Residential rooftop solar adoption is considered crucial for reducing carbon emissions. The lack of photovoltaic (PV) data at a finer resolution (e.g., household, hourly levels) poses a significant roadblock to informed decision-making. We discuss a novel methodology to generate a highly granular, residential-scale realistic dataset for rooftop solar adoption across the contiguous United States. The data-driven methodology consists of: (i) integrated machine learning models to identify PV adopters, (ii) methods to augment the data using explainable AI techniques to glean insights about key features and their interactions, and (iii) methods to generate household-level hourly solar energy output using an analytical model. The resulting synthetic datasets are validated using real-world data and can serve as a digital twin for modeling downstream tasks. Finally, a policy-based case study utilizing the digital twin for Virginia demonstrated increased rooftop solar adoption with the 30\% Federal Solar Investment Tax Credit, especially in Low-to-Moderate-Income communities.

Related papers

• Enhancing Indoor Temperature Forecasting through Synthetic Data in Low-Data Environments [42.8983261737774]
  We investigate the efficacy of data augmentation techniques leveraging SoTA AI-based methods for synthetic data generation. Inspired by practical and experimental motivations, we explore fusion strategies of real and synthetic data to improve forecasting models.
  arXiv  Detail & Related papers  (2024-06-07T12:36:31Z)
• SolNet: Open-source deep learning models for photovoltaic power forecasting across the globe [0.0]
  SolNet is a novel, general-purpose, multivariate solar power forecaster. We show that SolNet improves forecasting performance over data-scarce settings. We provide guidelines and considerations for transfer learning practitioners.
  arXiv  Detail & Related papers  (2024-05-23T12:00:35Z)
• Open-sourced Data Ecosystem in Autonomous Driving: the Present and Future [130.87142103774752]
  This review systematically assesses over seventy open-source autonomous driving datasets. It offers insights into various aspects, such as the principles underlying the creation of high-quality datasets. It also delves into the scientific and technical challenges that warrant resolution.
  arXiv  Detail & Related papers  (2023-12-06T10:46:53Z)
• Power Hungry Processing: Watts Driving the Cost of AI Deployment? [74.19749699665216]
  generative, multi-purpose AI systems promise a unified approach to building machine learning (ML) models into technology. This ambition of generality'' comes at a steep cost to the environment, given the amount of energy these systems require and the amount of carbon that they emit. We measure deployment cost as the amount of energy and carbon required to perform 1,000 inferences on representative benchmark dataset using these models. We conclude with a discussion around the current trend of deploying multi-purpose generative ML systems, and caution that their utility should be more intentionally weighed against increased costs in terms of energy and emissions
  arXiv  Detail & Related papers  (2023-11-28T15:09:36Z)
• Unveiling the Invisible: Enhanced Detection and Analysis of Deteriorated Areas in Solar PV Modules Using Unsupervised Sensing Algorithms and 3D Augmented Reality [1.0310343700363547]
  This article presents a groundbreaking methodology for automatically identifying and analyzing anomalies like hot spots and snail trails in Solar Photovoltaic (PV) modules. By transforming the traditional methods of diagnosis and repair, our approach not only enhances efficiency but also substantially cuts down the cost of PV system maintenance. Our immediate objective is to leverage drone technology for real-time, automatic solar panel detection, significantly boosting the efficacy of PV maintenance.
  arXiv  Detail & Related papers  (2023-07-11T09:27:00Z)
• Improving day-ahead Solar Irradiance Time Series Forecasting by Leveraging Spatio-Temporal Context [46.72071291175356]
  Solar power harbors immense potential in mitigating climate change by substantially reducing CO$_2$ emissions. However, the inherent variability of solar irradiance poses a significant challenge for seamlessly integrating solar power into the electrical grid. In this paper, we put forth a deep learning architecture designed to harnesstemporal context using satellite data.
  arXiv  Detail & Related papers  (2023-06-01T19:54:39Z)
• A Comparative Study on Generative Models for High Resolution Solar Observation Imaging [59.372588316558826]
  This work investigates capabilities of current state-of-the-art generative models to accurately capture the data distribution behind observed solar activity states. Using distributed training on supercomputers, we are able to train generative models for up to 1024x1024 resolution that produce high quality samples indistinguishable to human experts.
  arXiv  Detail & Related papers  (2023-04-14T14:40:32Z)
• SKIPP'D: a SKy Images and Photovoltaic Power Generation Dataset for Short-term Solar Forecasting [0.0]
  There are few publicly available standardized benchmark datasets for image-based solar forecasting. We introduce SKIPP'D -- a SKy Images and Photovoltaic Power Generation dataset. The dataset contains quality-controlled down-sampled sky images and PV power generation data ready-to-use for short-term solar forecasting using deep learning.
  arXiv  Detail & Related papers  (2022-07-02T21:52:50Z)
• Feature Construction and Selection for PV Solar Power Modeling [1.8960797847221296]
  Building a model to predict photovoltaic (PV) power generation allows decision-makers to hedge energy shortages. The solar power output is time-series data dependent on many factors, such as irradiance and weather. A machine learning framework for 1-hour ahead solar power prediction is developed in this paper based on the historical data.
  arXiv  Detail & Related papers  (2022-02-13T06:49:28Z)
• Estimating Crop Primary Productivity with Sentinel-2 and Landsat 8 using Machine Learning Methods Trained with Radiative Transfer Simulations [58.17039841385472]
  We take advantage of all parallel developments in mechanistic modeling and satellite data availability for advanced monitoring of crop productivity. Our model successfully estimates gross primary productivity across a variety of C3 crop types and environmental conditions even though it does not use any local information from the corresponding sites. This highlights its potential to map crop productivity from new satellite sensors at a global scale with the help of current Earth observation cloud computing platforms.
  arXiv  Detail & Related papers  (2020-12-07T16:23:13Z)
• Short term solar energy prediction by machine learning algorithms [0.47791962198275073]
  We report daily prediction of solar energy by exploiting the strength of machine learning techniques. Forecast models of base line regressors including linear, ridge, lasso, decision tree, random forest and artificial neural networks have been implemented. It has been observed that improved accuracy is achieved through random forest and ridge regressor for both grid sizes.
  arXiv  Detail & Related papers  (2020-10-25T17:56:03Z)

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.