Load Forecasting11

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  Efficient Residential Electric Load Forecasting viaTransfer Learning and Graph Neural Networks Authors Di Wu, Weixuan Lin Title Efficient Residential Electric Load Forecasting via Transfer Learning and Graph Neural Networks Publication IEEE Transactions on Smart Grid Volume x Issue x Pages x Year 2022 DOI https://doi.org/10.1109/TSG.2022.3208211 Introduction Background Electric load forecasting is crucial for the efficient operation of modern power grids. Short-term load forecasting (STLF.. Zettelkasten/Paper Summarization 2023. 3. 3.

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  Gated spatial-temporal graph neural network based short-term load forecasting for wide-area multiple buses Authors Nantian Huang, Shengyuan Wang, Rijun Wang, Guowei Cai, Yang Liu, Qianbin Dai Title Gated spatial-temporal graph neural network based short-term load forecasting for wide-area multiple buses Publication International Journal of Electrical Power & Energy Systems Volume 145 Issue x Pages x Year 2023 DOI https://doi.org/10.1016/j.ijepes.2022.108651 Introduction Background Modern power system.. Zettelkasten/Paper Summarization 2023. 2. 26.

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  Monthly electric load forecasting using transfer learning for smart cities Authors Seungmin Jung, Sungwoo Park, Seungwon Jung, Eenjun Hwang Title Monthly electric load forecasting using transfer learning for smart cities Publication Sustainability Volume 12 Issue 16 Pages x Year 2020 DOI https://doi.org/10.3390/su12166364 Introduction Background With the recent increase in the use of fossil fuels to cope with the explosive demand for energy, diverse global problems, su.. Zettelkasten/Paper Summarization 2023. 2. 25.

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  A robust support vector regression model for electric load forecasting Authors Jian Luo, Tao Hong, Zheming Gao, Shu-Cherng Fang Title A robust support vector regression model for electric load forecasting Publication International Journal of Forecasting Volume x Issue x Pages x Year 2022 DOI https://doi.org/10.1016/j.ijforecast.2022.04.001 Introduction Background Load forecasts are widely used in the power industry to operate and plan power systems, such as unit co.. Zettelkasten/Paper Summarization 2023. 2. 24.

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  An effective dimensionality reduction approach for short-term load forecasting Authors Yang Yang, Zijin Wang, Yuchao Gao, Jinran Wu, Shangrui Zhao, Zhe Ding Title An effective dimensionality reduction approach for short-term load forecasting Publication Electric Power Systems Research Volume 210 Issue x Pages x Year 2022 DOI https://doi.org/10.1016/j.epsr.2022.108150 Introduction Background Establishment of reliable energy management system (EMS) has become the focus given.. Zettelkasten/Paper Summarization 2023. 2. 24.

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  Short-term residential load forecasting: Impact of calendar effects and forecast granularity Authors Peter Lusis, Kaveh Rajab Khalilpour, Lachlan Andrew, Ariel Liebman Title Short-term residential load forecasting: Impact of calendar effects and forecast granularity Publication Applied energy Volume 205 Issue x Pages 654-669 Year 2017 DOI https://doi.org/10.1016/j.apenergy.2017.07.114 Introduction Background Electricity demand depends on weather, time, and socio-economic constraints. Lo.. Zettelkasten/Paper Summarization 2023. 2. 20.

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  Short-term commercial load forecasting based on peak-valley features with the TSA-ELM model Authors Mengran Zhou, Ziwei Zhu, Feng Hu, Kai Bian, Wenhao Lai, Tianyu Hu Title Short-term commercial load forecasting based on peak‐valley features with the TSA‐ELM model Publication Energy Science & Engineering Volume 10 Issue 8 Pages 2622-2636 Year 2022 DOI https://doi.org/10.1002/ese3.1203 Introduction Background The demand for electricity in buildings is increasing year after year. Building.. Zettelkasten/Paper Summarization 2023. 2. 19.

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  A comparative analysis of artificial neural network architectures for building energy consumption forecasting Authors Jihoon Moon, Sungwoo Park, Seungmin Rho, Eenjun Hwang Title A comparative analysis of artificial neural network architectures for building energy consumption forecasting Publication International Journal of Distributed Sensor Networks Volume 15 Issue 9 Pages x Year 2019 DOI https://doi.org/10.1177/1550147719877616 Introduction Background Smart grids are a solution for energy shortage and.. Zettelkasten/Paper Summarization 2023. 2. 18.

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  Load forecasting Information Load forecasting is the process of estimating future electricity demand based on historical and current data, as well as other relevant factors such as weather patterns and economic trends. Load forecasting is essential for utilities and energy providers to plan for the future and ensure a reliable and efficient electricity supply. Load forecasting can be short-term (up to 24 hours),.. Zettelkasten/Terminology Information 2023. 2. 16.

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  Household electricity demand forecast based on context information and user daily schedule analysis from meter data Authors Yu-Hsiang Hsiao Title Household electricity demand forecast based on context information and user daily schedule analysis from meter data Publication IEEE Transactions on Industrial Informatics Volume 11 Issue 1 Pages 33-43 Year 2014 DOI https://doi.org/10.1109/TII.2014.2363584 Introduction Background Efficient energy usage is an important issue due to the limited energy sources and the .. Zettelkasten/Paper Summarization 2023. 2. 16.

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  A two-stage industrial load forecasting scheme for day-ahead combined cooling, heating and power scheduling Authors Sungwoo Park, Jihoon Moon, Seungwon Jung, Seungmin Rho, Sung Wook Baik, Eenjun Hwang Title A two-stage industrial load forecasting scheme for day-ahead combined cooling, heating and power scheduling Publication Energies Volume 13 Issue 2 Pages x Year 2020 DOI https://doi.org/10.3390/en13020443 Introduction Background There are growing concerns about environmental problems caused by carbo.. Zettelkasten/Paper Summarization 2023. 2. 14.