A Dataset of the Operating Station Heat Rate for 806 Indian Coal Plant Units using Machine Learning

• URL: http://arxiv.org/abs/2410.00016v1
• Date: Sat, 14 Sep 2024 19:12:57 GMT
• Title: A Dataset of the Operating Station Heat Rate for 806 Indian Coal Plant Units using Machine Learning
• Authors: Yifu Ding, Jansen Wong, Serena Patel, Dharik Mallapragada, Guiyan Zang, Robert Stoner,
• Abstract summary: India aims to achieve net-zero emissions by 2070 and has set an ambitious target of 500 GW of renewable power generation capacity by 2030. Coal plants currently contribute to more than 60% of India's electricity generation in 2022. This dataset could inform energy and environmental policies for India's coal power generation as the country transitions towards its renewable energy targets.
• Score: 3.5669362640232323
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: India aims to achieve net-zero emissions by 2070 and has set an ambitious target of 500 GW of renewable power generation capacity by 2030. Coal plants currently contribute to more than 60\% of India's electricity generation in 2022. Upgrading and decarbonizing high-emission coal plants became a pressing energy issue. A key technical parameter for coal plants is the operating station heat rate (SHR), which represents the thermal efficiency of a coal plant. Yet, the operating SHR of Indian coal plants varies and is not comprehensively documented. This study extends from several existing databases and creates an SHR dataset for 806 Indian coal plant units using machine learning (ML), presenting the most comprehensive coverage to date. Additionally, it incorporates environmental factors such as water stress risk and coal prices as prediction features to improve accuracy. This dataset, easily downloadable from our visualization platform, could inform energy and environmental policies for India's coal power generation as the country transitions towards its renewable energy targets.

Related papers

• Generative AI for Low-Carbon Artificial Intelligence of Things with Large Language Models [67.0243099823109]
  Generative AI (GAI) holds immense potential to reduce carbon emissions of Artificial Intelligence of Things (AIoT) In this article, we explore the potential of GAI for carbon emissions reduction and propose a novel GAI-enabled solution for low-carbon AIoT. We propose a Large Language Model (LLM)-enabled carbon emission optimization framework, in which we design pluggable LLM and Retrieval Augmented Generation (RAG) modules.
  arXiv  Detail & Related papers  (2024-04-28T05:46:28Z)
• GloSoFarID: Global multispectral dataset for Solar Farm IDentification in satellite imagery [1.2021565114959365]
  This study develops the first comprehensive global dataset of multispectral satellite imagery of solar panel farms. It is intended to form the basis for training robust machine learning models, which can accurately map and analyze the expansion and distribution of solar panel farms globally.
  arXiv  Detail & Related papers  (2024-04-08T04:10:50Z)
• Counting Carbon: A Survey of Factors Influencing the Emissions of Machine Learning [77.62876532784759]
  Machine learning (ML) requires using energy to carry out computations during the model training process. The generation of this energy comes with an environmental cost in terms of greenhouse gas emissions, depending on quantity used and the energy source. We present a survey of the carbon emissions of 95 ML models across time and different tasks in natural language processing and computer vision.
  arXiv  Detail & Related papers  (2023-02-16T18:35:00Z)
• Decarbonizing Indian Electricity Grid [0.0]
  India is responsible for 7 percent of global CO2 emissions. The electricity sector accounts for nearly 35 percent of emissions from the country. The switch from fossil fuels to renewable sources is the key in decarbonizing this sector.
  arXiv  Detail & Related papers  (2022-11-07T16:51:50Z)
• Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model [72.65502770895417]
  We quantify the carbon footprint of BLOOM, a 176-billion parameter language model, across its life cycle. We estimate that BLOOM's final training emitted approximately 24.7 tonnes ofcarboneqif we consider only the dynamic power consumption. We conclude with a discussion regarding the difficulty of precisely estimating the carbon footprint of machine learning models.
  arXiv  Detail & Related papers  (2022-11-03T17:13:48Z)
• Location-aware green energy availability forecasting for multiple time frames in smart buildings: The case of Estonia [0.5156484100374058]
  This research aims to forecast PV system output power based on weather and derived features using different machine learning models. The objective is to obtain the best-fitting model to precisely predict output power by inspecting the data.
  arXiv  Detail & Related papers  (2022-10-04T14:02:43Z)
• Measuring the Carbon Intensity of AI in Cloud Instances [91.28501520271972]
  We provide a framework for measuring software carbon intensity, and propose to measure operational carbon emissions. We evaluate a suite of approaches for reducing emissions on the Microsoft Azure cloud compute platform.
  arXiv  Detail & Related papers  (2022-06-10T17:04:04Z)
• An Artificial Intelligence Dataset for Solar Energy Locations in India [6.454602468926006]
  India has set ambitious goals to install 300 gigawatts of solar energy capacity by 2030. Land use planners will need access to up-to-date and accurate geo-spatial information of PV infrastructure. We develop a spatially explicit machine learning model to map utility-scale solar projects across India.
  arXiv  Detail & Related papers  (2022-01-31T23:53:19Z)
• SunCast: Solar Irradiance Nowcasting from Geosynchronous Satellite Data [2.285928372124628]
  We propose a Convolutional Long Short-Term Memory Network model that treats solar nowcasting as a next frame prediction problem. Our models can predict solar irradiance for entire North America for up to 3 hours in under 60 seconds on a single machine without a GPU.
  arXiv  Detail & Related papers  (2022-01-17T01:55:26Z)
• Modelling the transition to a low-carbon energy supply [91.3755431537592]
  A transition to a low-carbon electricity supply is crucial to limit the impacts of climate change. Reducing carbon emissions could help prevent the world from reaching a tipping point, where runaway emissions are likely. Runaway emissions could lead to extremes in weather conditions around the world.
  arXiv  Detail & Related papers  (2021-09-25T12:37:05Z)
• Optimizing carbon tax for decentralized electricity markets using an agent-based model [69.3939291118954]
  Averting the effects of anthropogenic climate change requires a transition from fossil fuels to low-carbon technology. Carbon taxes have been shown to be an efficient way to aid in this transition. We use the NSGA-II genetic algorithm to minimize average electricity price and relative carbon intensity of the electricity mix.
  arXiv  Detail & Related papers  (2020-05-28T06:54:43Z)

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.