Artificial Intelligence Model for Accurate Prediction of Energy Consumption in Buildings at design stage (E-MAP)

能源效率在帮助应对气候危机方面至关重要--英国26%的二氧化碳排放来自家庭。建筑环境占英国碳足迹的40%([www.ukgbc.org/Climate-change][0])，英国拥有欧洲最古老、效率最低的住房部门之一(绿色金融研究所-打造绿色家居行业)。2021年在格拉斯哥举行的第26届COP强调了人类活动导致的二氧化碳排放增加，并将其作为全球优先事项来解决。该项目(E-MAP)专注于开展一项可行性研究，以开发一种人工智能(AI)模型，该模型可以准确而高效地预测建筑设计阶段的能源消耗，而不需要广泛的建筑特征细节。E-MAP旨在评估通过利用先进的机器学习算法实施这种模型的技术可行性，以及它对建筑业和可持续建筑实践的潜在好处。E-MAP还将支持政府大幅减少新建筑能源消耗的挑战。[0]：http://www.ukgbc.org/climate-change

Energy efficiency is crucial in helping to tackle the climate crisis - 26% of UK carbon dioxide emissions come from homes. The building environment is responsible for 40% of the UK's carbon footprint ([www.ukgbc.org/climate-change][0]) and the UK has one of the oldest and least efficient housing sectors in Europe (Green Finance Institute - Tooling up the Green Homes Industry).COP 26 in Glasgow in 2021 highlighted the increase in carbon dioxide emissions from human activity and the need to address this as a global priority.This project(E-MAP) focuses on conducting a feasibility study for the development of an Artificial Intelligence (AI) model that can accurately and efficiently predict energy consumption at the design stage of buildings, without requiring extensive building feature details. E-MAP aims to assess the technical viability of implementing such a model by utilizing advanced machine-learning algorithms with its potential benefits for the construction industry and sustainable building practices.E-MAP will also support the government's challenge of substantially reducing the energy use of new buildings.[0]: http://www.ukgbc.org/climate-change