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<p>This paper presents a novel and extensive dataset featuring comprehensive cross-sectional data from 13 households with nearly three years of electrical load, energy cost, and on-premises solar energy production directly linked to solar irradiation and weather parameters (SHEERM dataset). The dataset is essential for understanding and optimizing energy utilization to achieve Sustainable Development Goals (SDG) 7, 9, 11 and 13. It provides data about solar energy production, weather conditions, residential energy needs, and market prices. The combination of these variables facilitates multifaceted analysis, fostering advancements in renewable energy forecasting, climate-sensitive environments, grid management, and energy policy formulation. This paper details the data collection process, including the sources and methodologies employed. Following established literature, we developed and implemented machine learning models that comprehensively validate the data. Furthermore, as usage notes, we offer additional results by applying machine-learning approaches to the provided data. This dataset aims to help design new energy systems that enhance sustainable energy strategies and demonstrate their potential to accelerate the transition toward renewable energy and carbon neutrality.</p>

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Leveraging Sustainable Household Energy and Environment Resources Management with Time-Series

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月22日

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  • José Cecílio,
  • Tiago Rodrigues,
  • Márcia Barros,
  • Alan Oliveira de Sá

摘要

This paper presents a novel and extensive dataset featuring comprehensive cross-sectional data from 13 households with nearly three years of electrical load, energy cost, and on-premises solar energy production directly linked to solar irradiation and weather parameters (SHEERM dataset). The dataset is essential for understanding and optimizing energy utilization to achieve Sustainable Development Goals (SDG) 7, 9, 11 and 13. It provides data about solar energy production, weather conditions, residential energy needs, and market prices. The combination of these variables facilitates multifaceted analysis, fostering advancements in renewable energy forecasting, climate-sensitive environments, grid management, and energy policy formulation. This paper details the data collection process, including the sources and methodologies employed. Following established literature, we developed and implemented machine learning models that comprehensively validate the data. Furthermore, as usage notes, we offer additional results by applying machine-learning approaches to the provided data. This dataset aims to help design new energy systems that enhance sustainable energy strategies and demonstrate their potential to accelerate the transition toward renewable energy and carbon neutrality.