AutoEPC - Scalable, Accurate, Automated Building Fabric Assessment

AutoEPC - 可扩展、准确、自动化的建筑结构评估

• 批准号: 10074665
• 金额: $ 22.22万
• 依托单位: DIGILAB SOLUTIONS LTD
• 依托单位国家: 英国
• 项目类别: Collaborative R&D
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=10074665
• 关键词: AutoEPC; Scalable; Accurate; Automated; Building

AutoEPC is a commercially scalable solution that uses self-learning algorithms to provide accurate fabric performance with no specialist equipment, installation or monitoring. According to the European Commission, household energy consumption accounts for roughly 20% of total CO2 emissions in UK and Europe, and heating losses are responsible for half of this figure. It is therefore essential to understand buildings' fabric performance in order to reduce their environmental footprint and incentivise projects that align with net zero goals. Existing EPC certificates are too inaccurate due to manual input and calculations, whilst expensive sensors setups combined with long term controlled testing aren't cost effective and therefore scalable across domestic and industrial markets.This project address the technical challenges for large scale commercial adoption, focusing on automation of model setup, data efficient robust to noisy and limited data and exploring the opportunities good quantification of fabric performance in a home can have in realising carbon crediting or incentive opportunities of domestic and commercial buildings.

AutoEPC是一种商业可扩展的解决方案，它使用自学习算法来提供准确的织物性能，无需专业设备，安装或监控。根据欧盟委员会的数据，家庭能源消耗约占英国和欧洲二氧化碳排放总量的20%，而供暖损失占这一数字的一半。因此，了解建筑物的结构性能至关重要，以减少其环境足迹并激励符合净零目标的项目。现有的EPC证书由于手动输入和计算而太不准确，而昂贵的传感器设置与长期控制测试相结合并不具有成本效益，因此可在国内和工业市场扩展。该项目解决了大规模商业应用的技术挑战，专注于模型设置的自动化，数据效率对噪声和有限数据的鲁棒性，以及探索家庭中织物性能的良好量化在实现家庭和商业建筑物的碳信用或激励机会中可能具有的机会。