HEMStoEC: Home Energy Management Systems to Energy Communities DataSet

The building sector is responsible for about 1/3 of all final energy consumed in the world. It is also responsible for about 30 % of CO² emissions from the end-use sector when accounting for indirect emissions from the use of electricity and heat in buildings. Focusing on EU, the use of electricity to satisfy the loads of lighting and most electrical appliances represents about 14.5 % of the energy consumed in residential sector, excluding heating and cooling systems, and 24.8 % including the latter. Therefore, we are in the presence of a sector that has a significant weight in the final energy consumption figures. Thus, innovative energy initiatives should contribute towards reducing energy consumption, reducing the effects on the climate, and achieving greater energy efficiency. These initiatives begin to emerge to a certain extent from small consumers, as they become more aware of environmental issues, either isolated or grouped in an energy community, where generated or stored energy is shared between stakeholders. In addition, energy markets go through a transition period and begin to give way, recognize, and promote the emerging role of prosumers (producers+consumers). It is within this context that this dataset is introduced. It allows, for a single prosumer, to: Test and validate different control strategies for home energy management systems; Design forecasting energy consumption models; Design forecasting PV energy generation models; Test and validate different non-invasive load monitoring (NILM) algorithms; Design forecasting thermal comfort models, as well as test and validate control strategies for Heating, Ventilation and Air Conditioning (HVAC) systems. Additionally, for a community of 4 houses, it allows to: Test and validate different control strategies for the community energy management system; Design forecasting community energy consumption models; Test and validate transfer learning strategies for NILM. The data, spanning more than three years, is stored in Matlab -v7 format, . This allows to be read by other languages, such as python.建筑行业约占全球最终能源总消耗量的三分之一。若计入建筑用电与用热产生的间接碳排放，其在终端用能领域的二氧化碳排放量占比也达到约30%。聚焦欧盟地区，照明及多数电器用电的能耗占住宅领域终端能耗的约14.5%（不含供暖与制冷系统），若包含该两类系统，该占比则升至24.8%。由此可见，建筑行业在全球最终能耗格局中占据举足轻重的地位。因此，亟需通过创新性能源举措降低能耗、减缓气候变化影响并提升能源利用效率。随着小型消费者对环境问题的认知逐步加深，无论是以个体形式还是加入能源社区（可在各参与方间共享所产生或储存的能源），这类创新能源举措正逐步涌现。与此同时，能源市场正处于转型阶段，逐步认可并推广产消者（prosumers，生产者与消费者的结合体）这一新兴角色。本数据集正是在此背景下推出的。针对单个产消者，本数据集可实现以下应用：测试并验证家用能源管理系统的各类控制策略；构建能源消耗预测模型；构建光伏（PV）能源发电预测模型；测试并验证各类非侵入式负载监测（Non-Intrusive Load Monitoring, NILM）算法；构建热舒适预测模型，同时测试并验证供暖、通风与空调（Heating, Ventilation and Air Conditioning, HVAC）系统的控制策略。此外，针对由4户住宅组成的社区，本数据集可支持以下应用：测试并验证社区能源管理系统的各类控制策略；构建社区能源消耗预测模型；测试并验证适用于NILM的迁移学习策略。本数据集的采集周期超过三年，以Matlab v7格式存储，可被Python等其他编程语言读取。