<?xml version='1.0' encoding='utf-8'?>
<?xml-stylesheet type="text/xsl" href="/v2/static/oai2.xsl"?>
<OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd">
  <responseDate>2026-10-06T12:11:32Z</responseDate>
  <request identifier="oai:figshare.com:article/33869962" metadataPrefix="oai_dc" verb="GetRecord">https://api.figshare.com/v2/oai</request>
  <GetRecord>
    <record>
      <header>
        <identifier>oai:figshare.com:article/33869962</identifier>
        <datestamp>2026-09-17T03:23:54Z</datestamp>
        <setSpec>category_29173</setSpec>
        <setSpec>category_26470</setSpec>
        <setSpec>item_type_3</setSpec>
        <setSpec>month_year_09_2026</setSpec>
      </header>
      <metadata>
        <oai_dc:dc xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"  xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
          <dc:title>Incentive-Aware Deep-Learning-Based Residential Load Forecasting and Dynamic Demand Response Strategy Optimization With Large-Scale User Data</dc:title>
          <dc:creator>Hongrong Fu (25000252)</dc:creator>
          <dc:subject>Machine learning not elsewhere classified</dc:subject>
          <dc:subject>Electrical energy transmission, networks and systems</dc:subject>
          <dc:subject>residential demand response</dc:subject>
          <dc:subject>load forecasting</dc:subject>
          <dc:subject>LSTM-Transformer</dc:subject>
          <dc:subject>incentive elasticity</dc:subject>
          <dc:subject>user profiling</dc:subject>
          <dc:description>&lt;p dir="ltr"&gt;Reproducibility materials for the manuscript "Incentive-Aware Deep-Learning-Based Residential Load Forecasting and Dynamic Demand Response Strategy Optimization With Large-Scale User Data" (Manuscript ID 6597722), submitted to the International Journal of Energy Research. This record provides the publicly releasable components supporting the study, including aggregate, event-level, and figure-source data, feature definitions, preprocessing rules, model hyperparameters, optimization settings, sensitivity scenarios, and verification statistics. Raw user-level smart-meter records are not included, as they contain sensitive residential electricity-use information governed by platform data-confidentiality agreements. See README.md and MANIFEST.md for the folder structure and file descriptions.&lt;/p&gt;</dc:description>
          <dc:date>2026-09-17T03:23:54Z</dc:date>
          <dc:type>Dataset</dc:type>
          <dc:type>Dataset</dc:type>
          <dc:identifier>10.6084/m9.figshare.33869962.v1</dc:identifier>
          <dc:relation>https://figshare.com/articles/dataset/Incentive-Aware_Deep-Learning-Based_Residential_Load_Forecasting_and_Dynamic_Demand_Response_Strategy_Optimization_With_Large-Scale_User_Data/33869962</dc:relation>
          <dc:rights>CC BY 4.0</dc:rights>
          <dc:rights>Open Access after 2028-09-17</dc:rights>
        </oai_dc:dc>
      </metadata>
    </record>
  </GetRecord>
</OAI-PMH>
